Transcriptomic Biomarkers in Safety and Risk Assessment of Chemicals

C H A P T E R

63 Transcriptomic Biomarkers in Safety and Risk Assessment of Chemicals David T. Szabo1, Amy A. Devlin2 1

PPG Industries Incorporated, Pittsburgh, PA, United States; 2US Food and Drug Administration, Silver Spring, MD, United States

INTRODUCTION Identification and use of transcriptomic biomarkers to distinguish physiological conditions or clinical stages is an emerging field that has advanced substantially this decade. This is partly due to several national and international agencies’ recommendations to incorporate advanced technologies in the safety and risk assessment process. Transcriptomics is recognized as an unbiased, sensitive, and personalized approach with the potential to reveal new predictive biomarkers of disease and ultimately improve the decision-making process. When performed in a doseeresponse format, the observed transcriptional changes can provide both quantitative and qualitative information on the dose at which cellular processes are affected. Transcriptomic approaches have transformed the way in which physicians approach diagnosis, prognosis, and treatment and in which regulators approach risk assessment. This chapter provides fundamental insights into the promising technology of transcriptomics with multiple applications in both regulatory science and clinical research, where it can be used to discover novel biomarkers potentially useful for human safety and risk assessment (Fig. 63.1).

Transcriptomics The transcriptome of an organism includes the total of all its RNA transcripts at one point in time. The term “transcriptome” was first used in the 1990s (Pie´tu et al., 1999; Velculescu et al., 1997). Although transcripts originate from less than 5% of the genome in humans and other mammals, each gene (locus of expressed DNA) may produce a variety of messenger RNA (mRNA) molecules using the process of alternative splicing. Therefore, the transcriptome has a level of Biomarkers in Toxicology, Second Edition https://doi.org/10.1016/B978-0-12-814655-2.00063-3

complexity greater than the genome that encodes it. Modern transcriptomics is regarded as a highthroughput technology concerned with determining how the transcriptome changes with respect to various factors at a certain time point and at a given biological state. Regulation of gene expression is highly complex and underlies many fundamental biological processes such as growth, differentiation, and disease pathogenesis with the ability to adapt rapidly with tremendous variability in different tissues and in response to stimuli (Sandvik et al., 2006). Transcriptomics and global gene expression are powerful tools used in the field of toxicology and, as in most cases, toxicity is not expected to occur without alterations at the transcriptional level (Eun et al., 2008; Gibb et al., 2011). A common application of transcriptomics in toxicology is to compare gene expression in samples following chemical administration from exposed and nonexposed animals to provide a list of genes that demonstrate altered expression in the diseased group. Such findings not only advance our understanding of disease pathogenesis but they also reveal transcripts that can be qualitatively or quantitatively assessed as new biomarkers. Within the National Institutes of Health, the term biomarker is defined as follows: a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention (Ilyin et al., 2004). Although a single biomarker can be easily understood, transcriptomic studies have uncovered aggregate measures composed of multiple genes as informative biomarkers of complex disease. Efforts in transcriptomic biomarker development are not restricted to medical diagnostics but include environmental chemical risk assessment and determination of exposure to

1125

Copyright © 2019 Elsevier Inc. All rights reserved.

1126

63. TRANSCRIPTOMIC BIOMARKERS IN SAFETY AND RISK ASSESSMENT OF CHEMICALS

EXPOSURE Environmental Pharmaceu cal Microbiological

FIGURE 63.1

TRANSCRIPTOMIC BIOMARKERS Methods qPCR microarrays RNA-Seq

Types mRNA miRNA lncRNA circRNA

RISK ASSESSMENT Biomarker of effect or exposure Cardiovascular Toxicity Infec ous Agents and Sepsis Hepa c Transplanta on Anabolic Steroid Use Tobacco Harm Reduc on Environmental Chemical Endpoints

Chapter overview on the use of transcriptomic biomarkers for safety and risk assessment.

microbes or chemical residues in food (Riedmaier et al., 2009a,b,c; Pinel et al., 2010).

Transcriptomic Methods There are three commonly used methods for assessing transcriptomes: quantitative real-time polymerase chain reaction (qPCR), microarrays, and RNA sequencing (RNA-Seq). Each method has advantages and disadvantages. qPCR is a targeted method that involves the reverse transcription of mRNA to cDNA followed by the use of short DNA sequences called primers to anneal and amplify known genes from a biological sample. Rapid quantitation of hundreds of transcripts using qPCR is relatively inexpensive, but knowledge of sequences of the candidate transcripts is required. Microarrays and RNA-Seq were developed more recently, within the last 30 years, with further advancements made in the 21st century (Wang et al., 2009; Nelson, 2001). Microarrays were the preferred method of transcriptional profiling until the late 2000s when the use of RNA-Seq increased (Medline trend, 2017; Nelson, 2001). Microarrays consist of probes, which are short nucleotide oligomers, arranged on a solid substrate, such as glass (Romanov et al., 2014). Relative transcriptional expression is determined by hybridization of fluorescently labeled transcripts to array probes. At each probe location, fluorescence intensity on the array signals transcript abundance for each probe sequence (Barbulovic-Nad et al., 2006). Unlike RNA-Seq, microarrays do require some knowledge of the organism of interest, such as its annotated genome sequence, that can be used to create probes for the array. Microarrays can be further subdivided into low-density spotted arrays or high-density short probe arrays (Heller, 2002). Singleor dual-channel detection of fluorescent tags can be used to record transcript presence. Low-density spotted arrays generally use a range of picoliter amounts of purified cDNAs arrayed on the surface of a glass slide (Auburn et al., 2005). Probes used are longer than those of high-density arrays, often lacking the transcript resolution of high-density arrays. Different fluorophores are used for exposed and nonexposed samples in spotted

arrays; the ratio of fluorescence is used to determine a relative calculation of abundance (Shalon et al., 1996). High-density arrays, popularized by Affymetrix GeneChip array (Santa Clara, CA), quantify transcripts using several short 25-mer probes (Irizarry et al., 2003). RNA-Seq combines high-throughput sequencing methodology with computational methods to assess and quantify transcripts present in an RNA extract (Ozsolak and Milos, 2011). As previously indicated, RNA-Seq usage exceeded the use of microarrays as the dominant transcriptomics technique in 2015 (Medline trend, 2017). Typical nucleotide sequences generated are approximately 100 base pairs (bp) in length, with a range of 30 bp to over 10,000 bp dependent on sequencing method used. RNA-Seq couples deep sampling of the transcriptome with many short fragments from a transcriptome to permit computational assembly of the original RNA transcript by aligning reads to a reference genome or to each other, the latter referred to as de novo assembly (Wang et al., 2009). RNA-Seq has a typical dynamic range of five orders of magnitude over microarray transcriptomes; additionally, input RNA quantities are much lower for RNA-Seq (nanogram quantity) compared with microarrays (microgram quantity). This allows more detailed examination of cellular structures down to single cells when used with linear amplification of cDNA (Hashimshony et al., 2012). Background signal is very low for 100 bp reads in nonrepetitive regions, and, in theory, there is no upper limit of quantification in RNA-Seq (Ozsolak and Milos, 2011). RNA-Seq can be used to identify genes within a genome or identify what genes are upregulated or downregulated at a specific point in time. Read counts and a simple quantitation approach allowing for the comparison across data sets can be used to correctly model relative gene expression. These three methods of assessing the transcriptome all have their benefits and challenges when compared with one another, and each method’s utility is dependent on the application and/or function of the study and outcome. Although RNA-Seq has already begun to replace microarrays in basic research, clinical studies will likely use both approaches depending on scientific goals, sample size, and cost. Looking forward, it is clear that advances in our fundamental understanding of gene transcription

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

TRANSCRIPTOMICS IN BIOMARKER DISCOVERY

and rapidly advancing techniques for transcriptome assessment will have a continued impact on biomarker development. However, one hurdle in the field is to establish better platforms for the reproducibility and predictive accuracy between sites (Fielden et al., 2008).

Types of Transcriptomic Biomarkers There are two frequently used types of transcriptomic biomarkers: mRNA and micro RNA (miRNA). Two other transcriptomic biomarkers less commonly used are noncoding RNA and circular RNA (circRNA). mRNA biomarkers are already an established method in several scientific fields. Diseased tissue can be distinguished from nondiseased tissue by analyzing the expression of specific genes. Use of mRNA gene expression analysis is helpful when validating differentiation of types or stages of diseases. miRNAs are endogenous noncoding RNA of approximately 19e22 nt in length (Krol et al., 2010). Transcription of miRNAs occurs via RNA polymerase II, resulting in primary miRNA with 500ee3000 nucleotides (Benz et al., 2016). Primary miRNA is next cleaved into premature miRNA of 70e80 nucleotides in length by the “microprocessor complex,” consisting of RNase III Drosha enzyme and the DiGeorge syndrome critical region 8 (DGCR8) protein (Lee et al., 2003). Premature miRNA is exported into the cytoplasm with aid of the nuclear export transporter, exportin 5, processing about 22 nucleotides by interacting with RNase III endonuclease Dicer protein and the cofounder double-stranded transactivation-responsive RNA-binding protein (Lund et al., 2004). This duplex is integrated into the “RNA-induced silencing complex” following binding to the argonaute protein and a glycine tryptophan repeat-containing protein, where they bind to partial or total complimentary sequences in the 30 or 50 untranslated region of the target mRNA (Ha and Kim, 2014; MacFarlane and Murphy, 2010). miRNAs are involved in posttranscriptional processing of mRNA. In this way, they are able to regulate physiological pathways and metabolic processes and therefore impact the entire cellular physiology, organ development, and tissue differentiation. The expression of miRNAs can be measured in cell culture samples. These miRNAs are also present in body fluids, such as urine, blood, and breast milk (Laterza et al., 2009; Kosaka et al., 2010; Kroh et al., 2010). Identification of single biomarkers on the mRNA or the miRNA level is not possible in most pathological disorders. In such cases, a set of multiple biomarkers must be present to distinguish between specific disease types, disease states, or applied treatments. Bioinformatics is one approach that can be used to integrate the data analysis of multiple biomarker levels of different types of RNA. This could help to generate an integrative gene expression pattern (Molloy et al., 2011). To achieve

1127

this goal, different multivariate analysis methods are available which are used for biomarker selection and validation, namely hierarchical cluster analysis and principal components analysis. Noncoding RNAs can be subdivided into small noncoding RNAs, which are shorter in length than 200 nucleotides (nt), and long noncoding RNAs (lncRNA), which are greater than 200 nt in length (Gibb et al., 2011). LncRNA transcripts do not code for proteins; there are greater than 60,000 members of the lncRNA family that have been catalogued (Volders et al., 2012, 2014). While the primary sequence of lncRNAs is not well conserved, it can be partially compensated through a high level of structural conservation (Johnsson et al., 2014). LncRNAs can be transcribed from conserved genomic regions (Boon et al., 2016) and back-splicing of exons, also capable of forming circRNAs (Jeck et al., 2013; Memczak et al., 2013). LncRNAs play an important role in the pathogenesis of several diseases as they are involved in cell functioning, including roles in chromatin rearrangement, histone modification, modification of alternative splicing genes, and regulation of gene expression (Hu et al., 2017; Fan et al., 2017). CircRNAs have recently been demonstrated to be widespread and abundant within transcriptomes (Jeck et al., 2013; Memczak et al., 2013; Jeck and Sharpless, 2014). CircRNAs are not formed by the usual model of RNA splicing but primarily from the exons of protein-coding genes (Salzman et al., 2012). CircRNAs are characterized by covalently closed loop structures through joining the 30 and 50 ends together via exon or intron circularization (Jeck and Sharpless, 2014; Zheng et al., 2016). Formation of circRNAs occurs through two varied mechanisms of exon circularization: intronpairing-driven circularization and lariat-driven circularization (Jeck et al., 2013). When introns between exons form circular structures, they are removed or retained to form exon-only circRNA or intron-retaining circRNA known as ElciRNA (Jeck et al., 2013; Li et al., 2015). CircRNAs can also be formed by the circularization of two flanking intronic sequences (Conn et al., 2015; AshwalFluss et al., 2014). To date, several circRNAs have been identified in organs or tissues, with some linked to disease, indicating that circRNAs have a role beyond being by-products of mis-splicing or splicing errors (Wang et al., 2016; Zheng et al., 2016). Many circRNAs have been found to behave as “miRNA sponges” to regulate gene expression (Memczak et al., 2013; Zheng et al., 2016; Hansen et al., 2013).

TRANSCRIPTOMICS IN BIOMARKER DISCOVERY Transcriptomic approaches have been used by various researchers to identify novel biomarkers, which

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

1128

63. TRANSCRIPTOMIC BIOMARKERS IN SAFETY AND RISK ASSESSMENT OF CHEMICALS

are beginning to influence both clinical practice and environmental chemical risk assessment. This section focuses on biomarkers of cardiovascular disease and hepatotoxicity, exposure to infectious agents, tobacco products, anabolic steroids, beta-agonists, and use of biomarkers in pathway analysis for quantitative environmental chemical risk assessment.

Cardiovascular Biomarkers Cardiovascular disorders are responsible for high morbidity and mortality and can lead to a substantial economic burden at the individual, institutional, and national levels. The use of the human cardiac transcriptome as a biomarker to improve clinical diagnosis is slow to develop, mostly due to risks and complications associated with endomyocardial biopsies (Zhang et al., 2003). However, recent transcriptomic experiments using human cardiac biopsies have been conducted and used to identify specific causes of cardiomyopathy (Farazi et al., 2011) including subtypes of myocarditis (Soga et al., 2002). The ST2 protein, encoded by the IL1RL1 gene, is markedly elevated in patients with heart failure and exists in a soluble form that can be measured in peripheral blood. ST2 was first identified as a potential biomarker upregulated in an in vitro transcriptomics study involving myocardial stretch. Follow-up studies in animals have suggested ST2 is part of a cardioprotective paracrine signaling axis between cardiac fibroblasts and myocytes (Sanada et al., 2007; Kakkar and Lee, 2008). ST2 signals the presence and severity of adverse cardiac remodeling and tissue fibrosis, which routinely occurs in response to myocardial infarction, acute coronary syndrome, or worsening heart failure. ST2 use as a biomarker has considerable prognostic value and is used as an aid for risk stratification in identifying patients who are either at high risk of mortality or need rehospitalization. As a result of these biomarker discovery studies, a commercial-grade soluble ST2 assay for use in assessing prognoses of chronic heart failure has been cleared by the United States Food and Drug Administration (US FDA) (Presage, Critical Diagnostics, San Diego, CA). Although the ST2 immunoassay measures protein levels in the blood, the use of transcriptomics was vital in its discovery. The initial findings of the transcriptomic screens have led to both a novel cardiac biomarker and therapeutic target for heart failure. The use of miRNAs as a cardiovascular disease biomarker is promising across several endpoints. They are considered to be involved in fibroblasts proliferation, collagen synthesis, and connective tissue growth factor signaling (Angelini et al., 2015). The implication of miRNAs in heart failure was also demonstrated; its increased expression can depress cardiac function (Wahlquist et al., 2014). Other associations between miRNAs and

cardiovascular endpoints include cardiac hypertrophy (Care et al., 2007; Bang et al., 2014), acute coronary syndrome (Widera et al., 2011), acute myocardial infarction (Ji et al., 2009; Wang et al., 2009; Corsten et al., 2010; D’Alessandra et al., 2010), hypertension (Li et al., 2011), and heart failure (Latronico et al., 2007; Tijsen et al., 2010). Above all, there are a large number of detectable miRNAs, but only a few that may currently provide valuable information. This area remains largely under investigation for cardiovascular endpoints.

Hepatic Biomarkers Several studies have used transcriptomics to identify biomarkers of hepatotoxicity and to determine their mechanism of action (Kussmann et al., 2006; Mutlib et al., 2006; Nie et al., 2006; Tugendreich et al., 2006; Fielden et al., 2007; Eun et al., 2008). Liver gene expression profiling by microarray technology was used to develop biomarkers for nongenotoxic hepatocarcinogens (Fielden et al., 2007). Fielden et al. (2007) collected hepatic gene expression data from rats treated for 5 days with 47 test chemicals. They observed that this shortterm in vivo rodent model was more sensitive, more accurate, and provided quicker results when compared with the traditional models for risk assessment of nongenotoxic carcinogens. qPCR has also been used for the identification of biomarkers of toxicity (Ellinger Ziegelbauer et al., 2009). These studies demonstrate that transcriptomic technologies are valuable sensitive tools for screening hepatotoxic and hepatocarcinogenic potential of suspected test agents, although their biomarkers and mechanisms of action may be different. Liver transplantation is a surgical procedure in which a liver that no longer functions properly is removed and replaced with a healthy liver from a living or deceased donor. The unique characteristics of the liver transplant present opportunities to decipher the immunological mechanisms underlying allograft survival and to develop therapeutic targets aimed toward tolerance strategies. RNA-Seq is beginning to be capitalized on to provide insights into normal, pathological, and pharmacological processes (Mastoridis et al., 2016). The integration of clinical and molecular data becomes essential to the pursuit of advancing the field of transplantation and developing personalized therapy.

Biomarkers of Anabolic Agents Screening for anabolic agents such as steroid hormones (testosterone or estrogen) and beta-agonists in meat-producing animals demonstrates an application of transcriptomics for use in safety and regulatory monitoring. In agricultural meat-producing animals, the myotropic, growth-promoting properties of steroid

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

TRANSCRIPTOMICS IN BIOMARKER DISCOVERY

hormones and beta-agonists are beneficial because of increased productivity and reduced costs with weight gain and feed efficiency (Chwalibog et al., 1996). As a consequence, hormone and drug residues in meat are increased and adverse side effects to the consumer occur (Daxenburger et al., 2001; Lange et al., 2001; Maume et al., 2001; Swan et al., 2007). Questions and controversy over the impacts of these added hormones on human development and health have lingered for decades. The use of growth promoters is approved in the United States, Canada, Mexico, Australia, and South Africa, and although the US FDA does not currently regulate their use in cattle, these drugs are prohibited for over-thecounter use by humans. The European Union (EU) forbids the use of these substances, including the importation of meat from animals treated with these substances. To enforce the EU rules, the monitoring of anabolic agents in meat is necessary. Current methods to uncover the abuse of anabolic agents in animal residues are limited to immune assays or chromatographical methods (Mayer et al., 1991, 2011) which can only detect known substances. The use of transcriptomics is a promising approach to develop a biomarker pattern based on physiological changes that result after illegal application of anabolic agents. Discovery of a single transcription biomarker in cattle for a particular organ or tissue is challenging; therefore, gene expression profiles, the measurement of several genes at one time, is used (Riedmaier et al., 2009a,b,c). Promising candidate genes and gene profiles for the development of a biomarker screening method in cattle include insulinlike growth factor 1 in liver and muscle (Johnson et al., 1998; Pfaffl et al., 2002; Reiter et al., 2007), steroid hormone receptors in the liver, muscle, uterus, gastrointestinal tract, kidney, prostate and blood cells (Pfaffl et al., 2002; Toffolatti et al., 2006; Reiter et al., 2007; Riedmaier et al., 2009a,b,c), and various inflammatory, apoptotic and proliferative genes in blood cells (Chang et al., 2004; Cantiello et al., 2007; Riedmaier et al., 2009a,b,c). Agonists for the beta adrenergic receptor are known to effect mRNA expression of different muscle proteins, such as a-actin, myosin, or calpastatin in cattle. Overall, the use of transcriptomics and other omics technologies can be used to develop new screening methods for the detection of the misuse of anabolic steroids and betaagonists for public health protection.

Infectious Agents and Sepsis One of the most common causes of mortality among hospitalized patients in the intensive care unit (ICU) is sepsis. Sepsis can be challenging to diagnose among patients in the ICU due to multiple comorbidities and underlying conditions (Novosad et al., 2016; Vincent et al., 2009). In 2016, after 25 years of no change in definition,

1129

the Sepsis-3 conference redefined sepsis as a “lifethreatening organ dysfunction caused by a deregulated host response to infection,” and septic shock as a “subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality” (Singer et al., 2016). Laboratory biochemical, microbiological, and hematological testing is fundamental in the diagnosis of sepsis. Because culture-based diagnosis is lengthy, considerable effort has been made to uncover biomarkers that allow early diagnosis of sepsis. Currently, biomarkers alone are not sufficient for diagnosis of sepsis and should accompany thoughtful clinical assessment and other supportive laboratory data (Faix, 2013; Vincent, 2016). Most frequently in sepsis, serum protein inflammatory biomarkers are used. Sepsis can be separated into two successive phases, the initial hyperinflammatory phase, which may resolve or come before the secondary immunosuppressive phase, generally characterized by organ dysfunction. Markers are available for both phases but biomarkers of the hyperinflammatory phase predominate and include procalcitonin (PCT) and C reactive protein (CRP). The second immunosuppressive phase contains biomarkers such as human leukocyte antigen-D related (HLA-DR). CRP is an acute-phase protein often manufactured in the liver and can rapidly rise in a few hours after exposure. Peripheral blood mononuclear cells have demonstrated upregulation of CRP mRNA in response to endotoxin, the most common cause of sepsis (Haider et al., 2006). CRP plasma concentration appears to reflect sepsis severity (Miglietta et al., 2015). Although CRP does not differentiate sepsis from other diseases, it is particularly useful to screen for early onset neonatal sepsis (Hofer et al., 2012) and to monitor patients after surgery (Watt et al., 2015). PCT is generally considered the most useful biomarker of sepsis (Riedel et al., 2011). Its production is stimulated similarly to that of CRP. PCT can discriminate between infectious and noninfectious systemic inflammation in critically ill patients (Harbarth et al., 2001) and may be able to differentiate between viral and bacterial infections (Ahn et al., 2011). PCT mRNA expression is elevated in septic tissues versus nonseptic tissues in the hamster model (Domenech et al., 2001). HLA-DR expression on the cell surface is a key indicator of immune response due to its vital role in antigen presentation. Expression of HLA-DR is lower in septic patients than in nonseptic patients, but its expression may also be low due to other causes of a weakened immune system (Das, 2014). Reduced HLA-DR expression in septic patients has also been determined transcriptionally (Winkler et al., 2017). Biomarkers of organ dysfunction include the following: lactate, venous to arterial

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

1130

63. TRANSCRIPTOMIC BIOMARKERS IN SAFETY AND RISK ASSESSMENT OF CHEMICALS

carbon dioxide pressure difference, proadrenomedullin (MR-proADM), and cell-free DNA (cf-DNA). Hyperlactatemia, high blood lactate level, is considered a severe sepsis marker of organ dysfunction, as it indicates poor tissue perfusion (Rello et al., 2017). Levels of CO2 in the blood above 6 mmHg within the first 24 h in critically ill patients are correlated with higher mortality (He et al., 2016). Higher levels of MR-proADM are associated with greater mortality, and investigations demonstrate that it has also been observed transcriptionally (Becker et al., 2004). Cf-DNA levels are greater in sepsis patients than in healthy individuals, but cf-DNA is found after cell death. Sepsis is a major public health concern due to its high mortality and morbidity. Protein biomarkers are commonly used in the diagnosis of sepsis, but several studies utilizing precursor biomarker mRNA are promising in the pursuit of more rapid diagnostics. Combinations of biomarkers are likely needed for sepsis diagnosis and treatment due to its high complexity in the patient. Combinatorial biomarker assessments can likely be performed more rapidly using transcriptional analyses in the future.

Tobacco and Risk Continuum Since 2009, the US FDA Center for Tobacco Products has had authority to regulate the manufacture, distribution, and marketing of tobacco products with the goal of reducing death/disease from consumer use. Biomarkers could play an important role, including transcriptomics, across a number of FDA regulatory activities, including assessing new and modified risk tobacco products and identifying and evaluating potential product standards. The concept that not all tobacco and nicotine products present the same risks to human health is not a novel concept as there is clearly a large difference in the health risks associated with cigarette smoking when compared to medicinal nicotine products. There have been attempts to describe a “risk continuum” placing various nicotine product categories within a paradigm (Fig. 63.2). On July 28, 2017, FDA noted, “A key piece of the FDA’s approach is demonstrating a greater awareness that nicotine d while highly addictive d is delivered through products that represent a continuum of risk and is most harmful when delivered through smoke particles in combustible cigarettes” (FDA, 2017). Smoking is a major risk factor for developing oral squamous cell carcinoma, but less attention has been paid to the effects of smokeless tobacco products. Using oral cell lines, potential biomarkers distinguishing biological effects of combustible tobacco products from

Tobacco Product Risk Continnum

CombusƟble CigareƩes

Tobacco Heat Products

FIGURE 63.2

Smokeless Tobacco

Electonic CigareƩes

NicoƟne Replacement Therapy

Hypothetical example of a tobacco product risk

continuum.

those of noncombustible tobacco products have been identified (Woo et al., 2017). Microarrays revealed xenobiotic metabolism and steroid biosynthesis as two pathways upregulated by combustible but not by noncombustible products. Notably, aldo-keto reductase genes, AKR1C1 and AKR1C2, were the genes in the top enriched pathways and were statistically upregulated more than eightfold by combustibles. AKR1C1 was further supported by qPCR as a potential biomarker for differentiating the biological effects of combustible from noncombustible tobacco products. Electronic Nicotine Delivery Systems (ENDS, electronic cigarettes or vapor products) are gaining in global popularity and generate a heated aerosol comprised of purified nicotine and generally recognized as safe ingredients. An expert review concludes that e-cigarettes are around 95% safer than smoked tobacco and they can help smokers to quit (Public Health England, 2015). RNA-Seq analysis has been used to examine the transcriptomes of differentiated human bronchial epithelial cells exposed to air, tobacco smoke, and ENDS aerosol in an in vitro aireliquid interface model for cellular exposure. Results indicate that ENDS do not elicit many of the cell toxicity responses observed from combustible cigarette smoke exposure. These transcriptomic studies establish a baseline for future analysis that can better inform the FDA of the risks and future regulation of these products while comparing them to the greater health-risk combustible cigarettes. Currently, there is a lack of systematic effort or FDA guidance by the Center for Tobacco Products to identify transcriptomic biomarkers that would have utility and validity for evaluating tobacco products. Omics research could further strengthen the

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

REFERENCES

applicability for tobacco regulatory science to better understand the risk continuum for overall public health protection.

Environmental Chemical Risk Assessment The US EPA, the National Toxicology Program (NTP), and the National Research Council recommend the use of pathway analysis as a basis for toxicity testing and chemical risk assessment (Collins, 2008; Thomas et al., 2012). To date, transcriptomics have not yet translated to a toxicity value derivation for regulatory purposes. Transcriptomic data in risk assessment have focused on supportive roles, elucidating a chemical’s mechanism or mode of action (Thomas et al., 2012) that underlies chemically induced noncancer and cancer endpoints. Female B6C3F1 mice were previously exposed to multiple concentrations of five chemicals (1,4-dichlorobenzene, propylene glycol mono-t-butyl ether, 1,2,3-trichloropropane, methylene chloride, and naphthalene) that had tested positive for lung or liver tumor formation in 2-year rodent cancer bioassays conducted by the NTP (Thomas et al., 2011). The doseeresponse changes in gene expression were analyzed using a standard benchmark dose modeling (BMD) approach and then grouped based on cellular/biological processes. The transcriptional points of departure for the five chemicals were compared with both cancer and noncancer points of departure based on apical endpoints. Overall, the transcriptomic responses corresponded closely with both noncancer and cancer apical responses as a function of dose, and their use as a point of departure is beginning to look promising. As the field continues to evolve, additional studies have demonstrated that transcriptional points of departure correlate with those derived from apical endpoint changes, resulting in a suitable transcriptomic biomarker; however, there is currently no consensus on the process. Eleven approaches have been identified from apical data showing promise for chemical risk assessment (Farmahin et al., 2017). This was accomplished using published microarray data from doseeresponse studies across several chemicals in rats exposed orally for up to 90 days. It is promising for this field that transcriptional BMD values for all approaches aligned well with corresponding apical points of departure. Cancer risk assessment from exposure to environmental chemicals has also benefited from transcriptomic biomarkers. Positive genotoxicity findings are a major challenge to industry and regulatory agencies when these tests produce irrelevant positive findings (i.e., positive results in vitro that are not relevant to human cancer hazard). In vitro transcriptomic biomarker-based approaches have also been developed that provide biological relevance to positive

1131

genotoxicity assay data, particularly for in vitro chromosome damage assays (Li et al., 2017). Transcriptomic changes in canonical pathways are a promising approach to identify biomarkers to estimate the noncancer and cancer points of departure for use in quantitative risk assessments. The high correlation between these pathways and rodent toxicity indicates the need for further investigation of their role in these endpoints and examination of their relevance to human responses. If they are relevant, these pathways could be considered toxicity pathways and used as the basis for future high-throughput toxicity screen.

CONCLUDING REMARKS AND FUTURE DIRECTIONS Transcriptomics as a discipline is still evolving, and although there are no clearly defined methods for the discovery of biomarkers using gene expression technologies, the potential of this new field seems promising. There is a growing demand for transcriptional biomarker development in a variety of applications, including the following: clinical diagnosis, pathological processes, biomarkers for environmental chemical exposure or response, and safety evaluation. The discovery of predictive markers that precede pathology should be able to lessen the adverse impact to health. Because gene transcription is a very dynamic process able to adapt rapidly to environmental, physiological, or pathological changes, the transcriptome is a preferred data set for the identification of sensitive and predictive biomarkers. It is conceivable that both a single biomarker and the concept of multimarker panels will become the standard in biomarker research (Ky et al., 2011). To validate an identified transcribed biomarker set with high significance, the application of bioinformatics is necessary. Considering that there are different levels at which biomarkers can be measured and evaluated, and as technologies advance, new categories of transcriptomic biomarkers will continue to emerge and offer opportunities to significantly improve risk assessment while reducing cost.

References Ahn, S., Kim, W.Y., Kim, S.H., et al., 2011. Role of procalcitonin and Creactive protein in differentiation of mixed bacterial infection from 2009 H1N1 viral pneumonia. Influenza Respir. Viruses. 5 (6), 398e403. Angelini, A., Li, Z., Mericskay, M., et al., 2015. Regulation of connective tissue growth factor and cardiac fibrosis by an SRF/MicroRNA133a axis. PLoS One 10, e0139858. https://doi.org/10.1371/ journal.pone.0139858. Ashwal-Fluss, R., Meyer, M., Pamudurti, N.R., et al., 2014. circRNA biogenesis competes with pre-mRNA splicing. Mol. Cell. 56 (1), 55e66.

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

1132

63. TRANSCRIPTOMIC BIOMARKERS IN SAFETY AND RISK ASSESSMENT OF CHEMICALS

Auburn, R.P., Kreil, D.P., Meadows, L.A., et al., 2005. Robotic spotting of cDNA and oligonucleotide microarrays. Trends Biotechnol. 23 (7), 374e379. Bang, C., Batkai, S., Dangwal, S., et al., 2014. Cardiac fibroblast-derived microRNA passenger strand-enriched exosomes mediate cardiomyocyte hypertrophy. J. Clin. Invest. 124, 2136e2146. https:// doi.org/10.1172/JCI70577. Barbulovic-Nad, I., Lucente, M., Sun, Y., et al., 2006. Bio-microarray fabrication techniquesda review. Crit. Rev. Biotechnol. 26 (4), 237e259. Becker, K.L., Nylen, E.S., White, J.C., et al., 2004. Procalcitonin and the calcitonin gene family of peptides in inflammation, infection, and sepsis: a journey from calcitonin back to its precursors. J. Clin. Endocrinol. Metab. 89 (4), 1512e1525. Benz, F., Roy, S., Trautwein, C., et al., 2016. Circulating microRNAs as biomarkers for sepsis. Int. J. Mol. Sci. 17 (1), 78. Boon, R.A., Jae´, N., Holdt, L., et al., 2016. Long noncoding RNAs: from clinical genetics to therapeutic targets? J. Am. Coll. Cardiol. 67 (10), 1214e1226. Cantiello, M., Carletti, M., Cannizzo, F.T., et al., 2007. Effects of an illicit cocktail on serum immunoglobulins, lymphocyte proliferation and cytokine gene expression in the veal calf. Toxicology 242 (1e3), 3951. Care`, A., Catalucci, D., Felicetti, F., 2007. MicroRNA-133 controls cardiac hypertrophy. Nat. Med. 13, 613e618. Chang, L.C., Madsen, S.A., Toelboell, T., et al., 2004. Effects of glucocorticoids on Fas gene expression in bovine blood neutrophils. J. Endocrinol. 183 (3), 569e583. Chwalibog, A., Jensen, K., Thorbek, G., 1996. Quantitative protein and fat metabolism in bull calves treated with beta-adrenergic agonist. Arch Tierernahr. 49 (2), 159167A. Collins, F., 2008. Collins interview. Departing U.S. genome institute director takes stock of personalized medicine. Interview by Jocelyn Kaiser. Science 320 (5881), 1272. Conn, S.J., Pillman, K.A., Toubia, J., et al., 2015. The RNA binding protein quaking regulates formation of circRNAs. Cell 160 (6), 1125e1134. Corsten, M.F., Dennert, R., Jochems, S., et al., 2010. Circulating microRNA-208b and microRNA-499 reflect myocardial damage in cardiovascular disease/clinical perspective. Circ Cardiovasc Genet 3, 499e506. Das, U., 2014. HLA-DR expression, cytokines and bioactive lipids in sepsis. Arch Med Sci. AMS 10 (2), 325. Daxenberger, A., Ibarreta, D., Meyer, H.H., 2001. Possible health impact of animal oestrogens in food. Hum. Reprod. Update 7 (3), 340e355. D’Alessandra, Y., Devanna, P., Limana, F., et al., 2010. Circulating micro-RNAs are new and sensitive biomarkers of myocardial infarction. Eur. Heart J. 31, 2765e2773. Domenech, V.S., Nylen, E.S., White, J.C., et al., 2001. Calcitonin generelated peptide expression in sepsis: postulation of microbial infection-specific response elements within the calcitonin I gene promoter. J. Invest. Med. 49 (6), 514e521. Ellinger-Ziegelbauer, H., Gmuender, H., Bandenburg, A., et al., 2009. Prediction of a carcinogenic potential of rat hepatocarcinogens using toxicogenomics analysis of short-term in vivo studies. Mutat. Res. 637, 2339. Eun, J.W., Ryu, S.Y., Noh, J.H., et al., 2008. Discriminating the molecular basis of hepatotoxicity using the large-scale characteristic molecular signatures of toxicants by expression profiling analysis. Toxicology 249 (2e3), 176183. Faix, J.D., 2013. Biomarkers of sepsis. Crit. Rev. Clin. Lab Sci. 50 (1), 23e36. Fan, R., Cao, C., Zhao, X., et al., 2017. Downregulated long noncoding RNA ALDBGALG0000005049 induces inflammation in chicken muscle suffered from selenium deficiency by regulating stearoylCoA desaturase. Oncotarget 8 (32), 52761.

Farazi, T.A., Spitzer, J.I., Morozov, P., et al., 2011. miRNAs in human cancer. J. Pathol. 223 (2), 102115. Farmahin, R., Williams, A., Kuo, B., et al., 2017. Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment. Arch. Toxicol. 91 (5), 2045e2065. FDA, 2017. FDA News Release FDA Announces Comprehensive Regulatory Plan to Shift Trajectory of Tobacco-related Disease, Death. https://www.fda.gov/NewsEvents/Newsroom/Press Announcements/ucm568923.htm. Fielden, M.R., Brennan, R., Gollub, J., 2007. A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by non-genotoxic chemicals. Toxicol. Sci. 99 (1), 90e100. Fielden, M.R., Nie, A., McMillian, M., et al., 2008. Predictive safety testing consortium, carcinogenicity working group: interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci. 103, 28e34. Gibb, E.A., Brown, C.J., Lam, W.L., 2011. The functional role of long non-coding RNA in human carcinomas. Mol. Canc. 13, 1038. Ha, M., Kim, V.N., 2014. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 15 (8), 509. Haider, D.G., Leuchten, N., Schaller, G., et al., 2006. C-reactive protein is expressed and secreted by peripheral blood mononuclear cells. Clin. Exp. Immunol. 146 (3), 533e539. Hansen, T.B., Jensen, T.I., Clausen, B.H., et al., 2013. Natural RNA circles function as efficient microRNA sponges. Nature 495 (7441), 384. Harbarth, S., Holeckova, K., Froidevaux, C., et al., 2001. Diagnostic value of procalcitonin, interleukin-6, and interleukin-8 in critically ill patients admitted with suspected sepsis. Am. J. Respir. Crit. Care Med. 164 (3), 396e402. Hashimshony, T., Wagner, F., Sher, N., et al., 2012. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2 (3), 666e673. He, H.W., Liu, D.W., Long, Y., et al., 2016. High central venous-to-arterial CO2 difference/arterial-central venous O2 difference ratio is associated with poor lactate clearance in septic patients after resuscitation. J. Crit. Care 31 (1), 76e81. Heller, M.J., 2002. DNA microarray technology: devices, systems, and applications. Annu. Rev. Biomed. Eng. 4 (1), 129e153. Hofer, N., Zacharias, E., Mu¨ller, W., et al., 2012. An update on the use of C-reactive protein in early-onset neonatal sepsis: current insights and new tasks. Neonatology 102 (1), 25e36. Hu, G., Dong, B., Zhang, J., et al., 2017. The long noncoding RNA HOTAIR activates the Hippo pathway by directly binding to SAV1 in renal cell carcinoma. Oncotarget 8 (35), 58654e58667. Ilyin, S.E., Belkowski, S.M., Plata-Salama´n, C.R., 2004. Biomarker discovery and validation: technologies and integrative approaches. Trends Biotechnol. 22 (8), 411416. Irizarry, R.A., Hobbs, B., Collin, F., et al., 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4 (2), 249e264. Jeck, W.R., Sharpless, N.E., 2014. Detecting and characterizing circular RNAs. Nat. Biotechnol. 32 (5), 453. Jeck, W.R., Sorrentino, J.A., Wang, K., et al., 2013. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19 (2), 141e157. Ji, X., Takahashi, R., Hiura, Y., et al., 2009. Plasma mir-208 as a biomarker of myocardial injury. Clin. Chem. 55, 1944e1949. Johnson, B.J., White, M.E., Hathaway, M.R., et al., 1998. Effect of a combined trenbolone acetate and estradiol implant on steady-state IGFI mRNA concentrations in the liver of wethers and the longissimus muscle of steers. J. Anim. Sci. 76 (2), 491e497. Johnsson, P., Lipovich, L., Grande´r, D., et al., 2014. Evolutionary conservation of long non-coding RNAs; sequence, structure, function. Biochim. Biophys. Acta Gen. Subj. 1840 (3), 1063e1071.

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

REFERENCES

Kakkar, R., Lee, R.T., 2008. The il-33/st2 pathway: therapeutic target and novel biomarker. Nat. Rev. Drug Discov. 7, 827e840. Kosaka, N., Iguchi, H., Ochiya, T., 2010. Circulating microRNA in body fluid: a new potential biomarker for cancer diagnosis and prognosis. Cancer Sci. 101 (10), 2087e2092. Kroh, E.M., Parkin, R.K., Mitchell, P.S., et al., 2010. Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 50 (4), 298e301. Krol, J., Loedige, I., Filipowicz, W., 2010. The widespread regulation of microRNA biogenesis, function and decay. Nat. Rev. Genet. 11 (9), 597. Kussmann, M., Raymond, F., Affolter, M., 2006. OMICSdriven biomarker discovery in nutrition and health. J. Biotechnol. 124 (4), 758e787. Ky, B., French, B., McCloskey, K., et al., 2011. High-sensitivity ST2 for prediction of adverse outcomes in chronic heart failure. Circ. Heart. Fail. 4, 180e187. Lange, I.G., Daxenberger, A., Meyer, H.H.D., 2001. Hormone contents in peripheral tissues after correct and off-label use of growth promoting hormones in cattle: effect of the implant preparations Finaplix-HÒ, RalgroÒ, Synovex-HÒ and Synovex PlusÒ. APMIS 109 (1), 53e65. Laterza, O.F., Lim, L., Garrett-Engele, P.W., et al., 2009. Plasma microRNAs as sensitive and specific biomarkers of tissue injury. Clin. Chem. 55 (11), 1977e1983. Latronico, M.V.G., Catalucci, D., Condorelli, G., 2007. Emerging role of microRNAs in cardiovascular biology. Circ. Res. 101, 1225e1236. Lee, Y., Ahn, C., Han, J., et al., 2003. The nuclear RNase III Drosha initiates microRNA processing. Nature 425 (6956), 415. Li, S., Zhu, J., Zhang, W., et al., 2011. Signature microRNA expression profile of essential hypertension and its novel link to human cytomegalovirus infection/clinical perspective. Circulation 124, 175e184. Li, Z., Huang, C., Bao, C., et al., 2015. Exon-intron circular RNAs regulate transcription in the nucleus. Nat. Struct. Mol. Biol. 22 (3), 256. Li, H., Chen, R., Hyduke, D.R., et al., 2017. Development and validation of a high-throughput transcriptomic biomarker to address 21st century genetic toxicology needs. Proc. Natl. Acad. Sci. USA 114 (51), E10881eE10889. Lund, E., Gu¨ttinger, S., Calado, A., et al., 2004. Nuclear export of microRNA precursors. Science 303 (5654), 95e98. MacFarlane, L.A., Murphy, P.R., 2010. MicroRNA: biogenesis, function and role in cancer. Curr Gemom 11 (7), 537e561. Mastoridis, S., Martı´nez-Llordella, M., Sanchez-Fueyo, A., 2016. Biomarkers and immunopathology of tolerance. Curr. Opin. Organ Transplant. 21 (1), 81e87. Maume, D., Deceuninck, Y., Pouponneau, K., et al., 2001. Assessment of estradiol and its metabolites in meat. APMIS 109 (1), 3238. Mayer, F., Kra¨mer, B.K., Ress, K.M., et al., 1991. Simplified, rapid and inexpensive extraction procedure for a high-performance liquid chromatographic method for determination of disopyramide and its main metabolite mono-N-dealkylated disopyramide in serum. J. Chromatogr. 572 (12), 339e345. Mayer, G., Muller, J., Lunse, C.E., 2011. RNA diagnostics: real-time RTPCR strategies and promising novel target RNAs. Wiley Interdisciplinary Rev RNA 2, 3241. Medline Trend: Automated Yearly Statistics of PubMed Results for Any Query, 2017. http://dan.corlan.net/medline-trend.html. Memczak, S., Jens, M., Elefsinioti, A., et al., 2013. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 495 (7441), 333. Miglietta, F., Faneschi, M.L., Lobreglio, G., et al., 2015. Procalcitonin, Creactive protein and serum lactate dehydrogenase in the diagnosis of bacterial sepsis, SIRS and systemic candidiasis. Infezioni Med. Le: Rivista Periodica di Eziologia, Epidemiologia, Diagnostica, Clinica e Terapia Delle Patologie Infettive 23 (3), 230e237.

1133

Molloy, T.J., Devriese, L.A., Helgason, H.H., et al., 2011. A multimarker qPCR-based platform for the detection of circulating tumour cells in patients with early-stage breast cancer. Br. J. Canc. 104 (12), 1913e1919. Mutlib, A., Jiang, P., Atherton, J., et al., 2006. Identification of potential genomic biomarkers of hepatotoxicity caused by reactive metabolites of N-methylformamide: application of stable isotope labeled compounds in toxicogenomic studies. Chem. Res. Toxicol. 19 (10), 1270e1283. Nelson, N.J., 2001. Microarrays have arrived: gene expression tool matures. J. Natl. Cancer Inst. 93 (7), 492e494. Nie, A.Y., McMillian, M., Parker, J.B., et al., 2006. Predictive toxicogenomics approaches reveal underlying molecular mechanisms of nongenotoxic carcinogenicity. Mol. Carcinog. 45, 914e933. Novosad, S.A., 2016. Vital signs: epidemiology of sepsis: prevalence of health care factors and opportunities for prevention. Morb. Mortal. Wkly. Rep. 65. Ozsolak, F., Milos, P.M., 2011. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12 (2), 87e98. Pfaffl, M.W., Daxenberger, A., Hageleit, M., et al., 2002. Effects of synthetic progestagens on the mRNA expression of androgen receptor, progesterone receptor, oestrogen receptor alpha and beta, insulin-like growth factor-1 (IGF-1) and IGF-1 receptor in heifer tissues. J. Vet. Med. A Physiol. Pathol. Clin. Med. 49 (2), 57e64. Pie´tu, G., Mariage-Samson, R., Fayein, N.A., et al., 1999. The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics. Genome Res. 9 (2), 195e209. Pinel, G., Rambaud, L., Monteau, F., et al., 2010. Estranediols profiling in calves’ urine after 17 beta-nandrolone laureate ester administration. J. Steroid Biochem. Mol. Biol. 121 (35), 626e632. Public Health England, 2015. E-cigarettes: An Evidence Update a Report Commissioned by Public Health England, p. 260. Reiter, M., Walf, V.M., Christians, A., et al., 2007. Modification of mRNA expression after treatment with anabolic agents and the usefulness for gene expression biomarkers. Anal. Chim. Acta 586 (1e2), 73e81. Rello, J., Valenzuela-Sa´nchez, F., Ruiz-Rodriguez, M., et al., 2017. Sepsis: a review of advances in management. Adv. Ther. 34 (11), 2393e2411. Riedel, S., Melendez, J.H., An, A.T., et al., 2011. Procalcitonin as a marker for the detection of bacteremia and sepsis in the emergency department. Am. J. Clin. Pathol. 135 (2), 182e189. Riedmaier, I., Becker, C., Pfaffl, M.W., Meyer, H.H., 2009a. The use of omic technologies for biomarker development to trace functions of anabolic agents. J. Chromatogr. A 1216, 8192e8199. Riedmaier, I., Tichopad, A., Reiter, M., Pfaffl, M.W., Meyer, H.H., 2009b. Identification of potential gene expression biomarkers for the surveillance of anabolic agents in bovine blood cells. Anal. Chim. Acta. 638, 106e113. Riedmaier, I., Tichopad, A., Reiter, M., Pfaffl, M.W., Meyer, H.H., 2009c. Influence of testosterone and a novel SARM on gene expression in whole blood of Macaca fascicularis. J. Steroid. Biochem. Mol. Biol. 114, 167e173. Romanov, V., Davidoff, S.N., Miles, A.R., et al., 2014. A critical comparison of protein microarray fabrication technologies. Analyst 139 (6), 1303e1326. Salzman, J., Gawad, C., Wang, P.L., et al., 2012. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS One 7 (2), e30733. Sanada, S., Hakuno, D., Higgins, L.J., et al., 2007. Il-33 and st2 comprise a critical biomechanically induced and cardioprotective signaling system. J. Clin. Invest. 117, 1538e1549. Sandvik, A.K., Alsberg, B.K., Nørsett, K.G., Yadetie, F., Waldum, H.L., Laegreid, A., 2006 Jan. Gene expression analysis and clinical

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY

1134

63. TRANSCRIPTOMIC BIOMARKERS IN SAFETY AND RISK ASSESSMENT OF CHEMICALS

diagnosis. Clin. Chim. Acta. 363 (1-2), 157e164. Epub 2005 Oct 5. Review. Shalon, D., Smith, S.J., Brown, P.O., 1996. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Res. 6 (7), 639e645. Singer, M., Deutschman, C.S., Seymour, C.W., et al., 2016. The third international consensus definitions for sepsis and septic shock (sepsis-3). J. Am. Med. Assoc. 315 (8), 801e810. Soga, T., Sugimoto, M., Honma, M., et al., 2002. Expression and regulation of st2, an interleukin-1 receptor family member, in cardiomyocytes and myocardial infarction. Circulation 106, 2961e2966. Swan, S.H., Liu, F., Overstreet, J.W., et al., 2007. Semen quality of fertile US males in relation to their mothers’ beef consumption during pregnancy. Hum. Reprod. 22, 1497e1502. Thomas, R.S., Clewell III, H.J., Allen, B.C., et al., 2011. Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment. Toxicol. Sci. 120, 194e205. Thomas, R.S., Clewell III, H.J., Allen, B.C., et al., 2012. Integrating pathway-based transcriptomic data into quantitative chemical risk assessment: A five chemical case study. Mutat. Res. 746, 135e143. Tijsen, A.J., Creemers, E.E., Moreland, P.D., et al., 2010. Mir423-5p as a circulating biomarker for heart failure. Circ. Res. 106, 1035e1039. Toffolatti, L., Rosa Gastaldo, L., Patarnello, T., et al., 2006. Expression analysis of androgen-responsive genes in the prostrate of veal calves treated with anabolic hormones. Domest. Anim. Endocrinol. 30, 38e55. Tugendreich, S., Pearson, C.I., Sagartz, J., et al., 2006. NSAID-induced acute phase response is due to increased intestinal permeability and characterized by early and consistent alterations in hepatic gene expression. Toxicol. Pathol. 34, 168e179. Velculescu, V.E., Zhang, L., Zhou, W., et al., 1997. Characterization of the yeast transcriptome. Cell 88 (2), 243e251. Volders, P.J., Helsens, K., Wang, X., et al., 2012. LNCipedia: a database for annotated human lncRNA transcript sequences and structures. Nucleic Acids Res. 41 (D1), D246eD251.

Volders, P.J., Verheggen, K., Menschaert, G., et al., 2014. An update on LNCipedia: a database for annotated human lncRNA sequences. Nucleic Acids Res. 43 (D1), D174eD180. Vincent, J.L., 2016. The clinical challenge of sepsis identification and monitoring. PLoS Med. 13 (5), e1002022. Vincent, J.L., Rello, J., Marshall, J., et al., 2009. International study of the prevalence and outcomes of infection in intensive care units. J. Am. Med. Assoc. 302 (21), 2323e2329. Wahlquist, C., Jeong, D., Rojas-Mun˜oz, A., et al., 2014. Inhibition of miR-25 improves cardiac contractility in the failing heart. Nature 508, 531e535. Wang, Z., Gerstein, M., Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10 (1), 57e63. Wang, K., Long, B., Liu, F., et al., 2016. A circular RNA protects the heart from pathological hypertrophy and heart failure by targeting miR-223. Eur. Heart J. 37 (33), 2602e2611. Watt, D.G., Horgan, P.G., McMillan, D.C., 2015. Routine clinical markers of the magnitude of the systemic inflammatory response after elective operation: a systematic review. Surgery 157 (2), 362e380. Widera, C., Gupta, S.K., Lorenzen, J.M., et al., 2011. Diagnostic and prognostic impact of six circulating micro RNAs in acute coronary syndrome. J. Mol. Cell Cardiol. 51, 872e875. Winkler, M.S., Rissiek, A., Priefler, M., et al., 2017. Human leucocyte antigen (HLA-DR) gene expression is reduced in sepsis and correlates with impaired TNFa response: a diagnostic tool for immunosuppression? PLoS One 12 (8), e0182427. Woo, S., Gao, H., Henderson, D., et al., 2017. AKR1C1 as a biomarker for differentiating the biological effects of combustible from noncombustible tobacco products. Genes 8 (5) pii: E132. Zhang, X., Zhou, Y., Mehta, K.R., et al., 2003. A pituitary-derived MEG3 isoform functions as a growth suppressor in tumor cells. J. Clin. Endocrinol. Metab. 88, 5119e5126. Zheng, Q., Bao, C., Guo, W., et al., 2016. Circular RNA profiling reveals an abundant circHIPK3 that regulates cell growth by sponging multiple miRNAs. Nat. Commun. 7, 11215.

X. APPLICATIONS OF BIOMARKERS IN TOXICOLOGY