Biomarker responses of the estuarine brown shrimp Crangon crangon L. to non-toxic stressors: Temperature, salinity and handling stress effects

Biochemical biomarkers in common estuarine species, such as the brown shrimp Crangon crangon, have the potential to provide early warning of contaminant exposure from field collected samples and through the development of in situ tests. The biomarkers acetylcholinesterase (AChE), lactate dehydrogenase (LDH) and glutathione S-transferase (GST) have been shown to provide evidence of exposure to contaminants in a number of species and field situations. As they may naturally respond to the marked physicochemical changes found in estuaries (thus confounding contaminant-induced effects), this work aims to determine the effects of salinity, temperature and handling stressors on these biomarkers in C. crangon.AChE recovery in field-collected shrimp transplanted to clean laboratory conditions suggests the presence of inhibiting factors at the sampling site (River Minho estuary). Maintenance time in stock tanks had effects that led to the choice of a minimum 15-day maintenance period of C. crangon in the laboratory before subsequent use of the enzymes as effect criterions in toxicity assays. Field levels of biomarker activity were unaffected following field-laboratory transportation of C. crangon, making this factor unlikely to jeopardize detection of contaminant associated effects. LDH levels were significantly lower under conditions that mimic a diurnal salinity fluctuation, increasing under low salinity conditions; this potentially indicates increased energy costs associated with raised osmoregulatory demands. It is recommended that a lower limit to field exposure of in situ tests should be based on salinity. Higher temperatures led to higher AChE activities and this is in agreement with the existing evidence of increases of endogenous AChE levels as a function of temperature (within a certain range). To avoid misinterpretation of biomarker responses, studies such as this are an important contribution to the establishment of reference activity levels against which biomarker changes can be estimated and are therefore essential preliminary steps in the development of in situ bioassays using biomarkers.     

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.     

Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers

In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses challenges to conducting dynamic prediction at any post-baseline time. A simple solution is the last-value-carry-forward method, but this may result in bias for the risk model estimation and prediction. Another option is to jointly model the longitudinal and survival processes with a shared random effects model. However, when dealing with multiple biomarkers, this approach often results in high-dimensional integrals without a closed-form solution, and thus the computational burden limits its software development and practical use. In this article, we propose to process the longitudinal data by functional principal component analysis techniques, and then use the processed information as predictors in a class of flexible linear transformation models to predict the distribution of residual time-to-event occurrence. The measurement schemes for multiple biomarkers are allowed to be different within subject and across subjects. Dynamic prediction can be performed in a real-time fashion. The advantages of our proposed method are demonstrated by simulation studies. We apply our approach to the African American Study of Kidney Disease and Hypertension, predicting patients' risk of kidney failure or death by using four important longitudinal biomarkers for renal functions.     

Design issues in cross-sectional biomarkers studies: urinary biomarkers of PAH exposure and oxidative stress

Cross-sectional biomarker studies can provide a snapshot of the frequency and characteristics of exposure/disease in a population at a particular point in time and, as a result, valuable insights for delineating the multi-step association between exposure and disease occurrence. Three major issues should be considered when designing biomarker studies: selection of appropriate biomarkers, the assay (laboratory validity), and the population validity of the selected biomarkers. Factors related to biomarker selection include biological relevance, specificity, sensitivity, biological half-life, stability, and so on. The assay attributes include limit of detection, reproducibility/reliability, inter-laboratory variation, specificity, time, and cost. Factors related to the population validity include the frequency or prevalence of markers, greater inter-individual variation than intra-individual variation, intra-class correlation coefficients (ICC), association with potential confounders, invasiveness of specimen collection, and subject selection. Three studies are selected to demonstrate different features of cross-sectional biomarker studies: (1) characterizing the determinants of the biomarkers (study I: urinary PAH metabolites and environmental particulate exposure), (2) relationship of multiple biomarkers of exposure and effect (study II: relationship between urinary PAH metabolites and oxidative stress), and (3) evaluating gene-environmental interaction (study III: effect of genetic polymorphisms of GSTM1 on the association of green tea consumption and urinary 1-OHPG levels in shipbuilding workers).     

The use of biomarkers in biomonitoring: a 2-tier approach assessing the level of pollutant-induced stress syndrome in sentinel organisms

The paper outlines a 2-tier approach for wide-scale biomonitoring programmes. To obtain a high level of standardization, we suggest the use of caged organisms (mussels or fish). An "early warning", highly sensitive, low-cost biomarker is employed in tier 1 (i.e. lysosomal membrane stability (LMS) and survival rate, a marker for highly polluted sites). Tier 2 is used only for animals sampled at sites in which LMS changes are evident and there is no mortality, with a complete battery of biomarkers assessing the levels of pollutant-induced stress syndrome. Possible approaches for integrating biomarker data in a synthetic index are discussed, along with our proposal to use a recently developed Expert System. The latter system allows a correct selection of biomarkers at different levels of biological organisation (molecular/cellular/tissue/organism) taking into account trends in pollutant-induced biomarker changes (increasing, decreasing, bell-shape). A selection of biomarkers of stress, genotoxicity and exposure usually employed in biomonitoring programmes is presented, together with a brief overview of new biomolecular approaches.     

蚯蚓生物标记物在土壤生态风险评价中的应用

蚯蚓在土壤中行使了很多重要的生态功能,蚯蚓生物标记物常用作土壤污染风险评价研究。这篇综述的目的是探讨当前蚯蚓生物标记物研究是否可以应用到实际的土壤污染风险评价。1)讨论了蚯蚓生物标记物在土壤污染风险评价体系中的重要性,认为它是化学分析方法的有益补充,可以提供更为全面和客观的土壤污染信息;2)综述了相关研究中所使用的蚯蚓类型,土壤类型和生物标记物类型,及其它试验设计要素和最后结果的变异,认为目前蚯蚓生物标记物研究以实验室基础研究为主,筛选出了大量的生物标记物,一定程度上揭示了生物标记物的对各类典型污染物及其组合的应答机制;同时也认为,未来的蚯蚓生物标记物研究应该重点探讨将其应用到实际的土壤污染风险评价中的可行性及如何应用;3)目前不同研究之间从试验设计到结果都具有很大的变异,难以通过综合比较获得完全可靠的具有实践意义的结论和成果,因此,有必要通过建立标准化的蚯蚓生物标记物研究方法,推动生物标记物的研究工作;4)提出了蚯蚓生物标记物研究方法标准化的具体建议,推荐了蚯蚓生物标记物走向实际应用所需要解决的问题。     

Inconvenient truth: Cancer biomarker development by using proteomics

A biomarker is a crucial tool for measuring the progress of disease and the effects of treatment for better clinical outcomes in cancer patients. Diagnostic, predictive, and prognostic biomarkers are required in various clinical settings. The proteome, a functional translation of the genome, is considered a rich source of biomarkers; therefore, sizable time and funding have been spent in proteomics to develop biomarkers. Although significant progress has been made in technologies toward comprehensive protein expression profiling, and many biomarker candidates published, none of the reported biomarkers have proven to be beneficial for cancer patients. The present deceleration in biomarker research can be attributed to technical limitations. Additional efforts are required to further technical progress; however, there are many examples demonstrating that problems in biomarker research are not so much with the technology but in the study design. In the study of biomarkers for early diagnosis, candidates are screened and validated by comparing cases and controls of similar sample size, and the low prevalence of disease is often ignored. Although it is reasonable to take advantage of multiple rather than single biomarkers when studying diverse disease mechanisms, the annotation of individual components of reported multiple biomarkers does not often explain the variety of molecular events underlying the clinical observations. In tissue biomarker studies, the heterogeneity of disease tissues and pathological observations are often not considered, and tissues are homogenized as a whole for protein extraction. In addition to the challenge of technical limitations, the fundamental aspects of biomarker development in a disease study need to be addressed. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

Use of Biomarkers in Oil Spill Risk Assessment in the Marine Environment

Numerous molecular, cellular, and physiological biomarkers have been used to assess the responses of marine animals to petroleum compounds. To be used in ecological risk assessment after an oil spill, a biomarker response needs to be linked to petroleum exposure and not strongly influenced by internal and external confounding factors. Biomarker responses to petroleum PAH, dominated by alklated two-and three-ringed aromatics, can be quite different than responses to pyrogenic PAH, dominated by four-and five-ringed aromatics. In many field sites there is a mixture of petrogenic and pyrogenic PAH, along with other contaminants, making it difficult to relate biomarker responses to a particular contaminant class. Biomarkers used to assess marine animal responses in the field include the cytochrome P450 system, heat stress protein, histopathology, and bile fluorescent compounds (FAC). Other biomarkers, including DNA/chromosomal damage and phase 2 enzymes, have been shown to respond after laboratory exposure, but more work needs to be done to demonstrate their usefulness in the field. One of the most useful biomarkers of petroleum exposure are the FAC responses in fish, which can be used to distinguish between petrogenic and pyrogenic PAH exposure. Few of the presently used biomarkers are linked to higher order biological effects, e.g. toxicity, reproductive failure.     

The use of plant stress biomarkers in assessing the effects of desiccation in zygotic embryos from recalcitrant seeds: challenges and considerations

Zygotic embryos from recalcitrant seeds are sensitive to desiccation. In spite of their sensitivity, rapid partial dehydration is necessary for their successful cryopreservation. However, dehydration to water contents (WCs) that preclude lethal ice crystal formation during cooling and rewarming generally leads to desiccation damage. This study investigated the effects of rapid dehydration on selected stress biomarkers (electrolyte leakage, respiratory competence, rate of protein synthesis, superoxide production, lipid peroxidation, antioxidant activity and degree of cellular vacuolation) in zygotic embryos of four recalcitrant‐seeded species. Most biomarkers indicated differences in the levels of stress/damage incurred by embryos dried to WCs < and >0.4 g·g^?1, within species; however, these changes were often unrelated to viability and percentage water loss when data for the four species were pooled for regression analyses. Dehydration‐induced electrolyte leakage was, however, positively related with percentage water loss, while biomarkers of cellular vacuolation were positively related with both percentage water loss and viability. This suggests that electrolyte leakage and degree of cellular vacuolation can be used to quantify dehydration‐induced stress/damage. Biomarkers such as superoxide production, whilst useful in establishing the nature of the dehydration stress incurred may not be able to distinguish the effects of different WCs/drying times. Irrespective of which biomarker is used, the data suggest that understanding differences in desiccation sensitivity across recalcitrant‐seeded species will remain a challenge unless these biomarkers are related to a generic desiccation stress index that integrates the effects of percentage water loss and drying time.     

The three-spined stickleback as an environmental sentinel: effects of stressors on whole-body physiological indices

Indicators of a generalized stress response (changes in cortisol, glucose, RNA: DNA ratio and total protein) when measured in whole-body preparations of individual three-spined sticklebacks Gasterosteus aculeatus displayed significant alterations in response to acute (hours) and chronic (days) disturbances and food withdrawal. In addition, changes in alkali-labile phosphorous, a specific biomarker of exposure to oestrogenic contaminants, could be detected in whole-body preparations of oestrogen-exposed three-spined sticklebacks confirming that the measurement of biomarkers normally assessed in a specific tissue can be equally possible in whole fish.     

The use of biomarkers in surveillance, medical screening, and intervention

Building on mechanistic information, much of molecular epidemiologic research has focused on validating biomarkers, that is, assessing their ability to accurately indicate exposure, effect, disease, or susceptibility. To be of use in surveillance, medical screening, or interventions, biomarkers must already be validated so that they can be used as outcomes or indicators that can serve a particular function. In surveillance, biomarkers can be used as indicators of hazard, exposure, disease, and population risk. However, to obtain rates for these measures, the population at risk will need to be assessed. In medical screening, biomarkers can serve as early indicators of disease in asymptomatic people. This allows for the identification of those who should receive diagnostic confirmation and early treatment. In intervention (which includes risk assessment and communication, risk management, and various prevention efforts), biomarkers can be used to assess the effectiveness of a prevention or control strategy as well as help determine whether the appropriate individuals are assigned to the correct intervention category. Biomarkers can be used to provide group and individual risk assessments that can be the basis for marshalling resources. Critical for using biomarkers in surveillance, medical screening, and intervention is the justification that the biomarkers can provide information not otherwise accessible by a less expensive and easier-to-obtain source of information, such as medical records, surveys, or vital statistics. The ability to use validated biomarkers in surveillance, medical screening, and intervention will depend on the extent to which a strategy for evidence-based procedures for biomarker knowledge transfer can be developed and implemented. This will require the interaction of researchers and decision-makers to collaborate on public health and medical issues.     

Reduction in the urinary aflatoxin M1 biomarker as an early indicator of the efficacy of dietary interventions to reduce exposure to aflatoxins

Abstract

Aflatoxin B₁ is a persistent public health issue in Ghana. Assessment of AFB₁ intervention efficacy is currently dependent on long-term biomarkers. This study was designed to determine whether daily AFM₁ biomarker levels could be utilized as an early detection method for intervention efficacy. Participants were treated with a refined calcium montmorillonite clay (UPSN) or a placebo (calcium carbonate) in a crossover study. Urine samples were assessed for AFM₁ levels daily. UPSN treatment reduced AFM₁ biomarkers by 55% compared to the placebo. This is the first study to show that daily urinary AFM₁ levels can be used as a biomarker of internal aflatoxin B₁ exposure in short-term intervention trials to determine efficacy.     

A weight of evidence approach for selecting exposure biomarkers for biomonitoring

Context: It is known that there are usually several biomarkers and/or medium combinations that can be applied to answer a specific exposure question. To help determine an appropriate combination for the specific question, we have developed a weight-of-evidence Framework that provides a relative appropriateness score for competing combinations.

Methods: The Framework is based on an expert assessor’s evaluation of the relevance and suitability of the biomarker and medium for the question based on a set of criteria. We provide a computer based modeling tool to guide the researcher through the process.

Results: We present an example with six biomarkers of benzene exposure in one matrix; the six are either the most commonly used biomarkers and/or have recent widespread usage. The example clearly demonstrates the usefulness of the Framework for scoring the choices, as well as the transparency of the method that provides the basis for discussion.

Conclusions: The Framework provides for the first time a method to transparently document the rationale behind selecting, from among a set of alternatives, the most scientifically supportable exposure biomarker to address a specific biomonitoring question, thus providing a reproducible account of expert opinions on the suitability of a biomarker.     

Exposure reconstruction for reducing uncertainty in risk assessment: example using MTBE biomarkers and a simple pharmacokinetic model

Adverse health risks from environmental agents are generally related to average (long-term) exposures. Because a given individual's contact with a pollutant is highly variable and dependent on activity patterns, local sources and exposure pathways, simple ‘snapshot’ measurements of surrounding environmental media may not accurately assign the exposure level. Furthermore, susceptibility to adverse effects from contaminants is considered highly variable in the population so that even similar environmental exposure levels may result in differential health outcomes in different individuals. The use of biomarker measurements coupled to knowledge of rates of uptake, metabolism and elimination has been suggested as a remedy for reducing this type of uncertainty. To demonstrate the utility of such an approach, we invoke results from a series of controlled human exposure tests and classical first-order rate kinetic calculations to estimate how well spot measurements of methyl tertiary butyl ether and the primary metabolite, tertiary butyl alcohol, can be expected to predict different hypothetical scenarios of previous exposures. We found that blood and breath biomarker measurements give similar results and that the biological damping effect of the metabolite production gives more stable estimates of previous exposure. We also explore the value of a potential urinary biomarker, 2-hydroxyisobutyrate suggested in the literature. We find that individual biomarker measurements are a valuable tool in reconstruction of previous exposures and that a simple pharmacokinetic model can identify the time frames over which an exogenous chemical and the related chemical biomarker are useful. These techniques could be applied to broader ranges of environmental contaminants to assess cumulative exposure risks if ADME (Absorption, Distribution, Metabolization and Excretion) is understood and systemic biomarkers can be measured.     

Ionizing radiation biomarkers for potential use in epidemiological studies

Ionizing radiation is a known human carcinogen that can induce a variety of biological effects depending on the physical nature, duration, doses and dose-rates of exposure. However, the magnitude of health risks at low doses and dose-rates (below 100mSv and/or 0.1mSvmin(-1)) remains controversial due to a lack of direct human evidence. It is anticipated that significant insights will emerge from the integration of epidemiological and biological research, made possible by molecular epidemiology studies incorporating biomarkers and bioassays. A number of these have been used to investigate exposure, effects and susceptibility to ionizing radiation, albeit often at higher doses and dose rates, with each reflecting time-limited cellular or physiological alterations. This review summarises the multidisciplinary work undertaken in the framework of the European project DoReMi (Low Dose Research towards Multidisciplinary Integration) to identify the most appropriate biomarkers for use in population studies. In addition to logistical and ethical considerations for conducting large-scale epidemiological studies, we discuss the relevance of their use for assessing the effects of low dose ionizing radiation exposure at the cellular and physiological level. We also propose a temporal classification of biomarkers that may be relevant for molecular epidemiology studies which need to take into account the time elapsed since exposure. Finally, the integration of biology with epidemiology requires careful planning and enhanced discussions between the epidemiology, biology and dosimetry communities in order to determine the most important questions to be addressed in light of pragmatic considerations including the appropriate population to be investigated (occupationally, environmentally or medically exposed), and study design. The consideration of the logistics of biological sample collection, processing and storing and the choice of biomarker or bioassay, as well as awareness of potential confounding factors, are also essential.     

The use of biomarkers for improved retrospective exposure assessment in epidemiological studies: summary of an ECETOC workshop

During a scientific workshop the use of biological monitoring in characterization of retrospective exposure assessment was discussed. The workshop addressed currently available methodology and also novel approaches such as in different fields of ‘omics’. For use in epidemiology requiring retrospective exposure assessment, biomarker levels should not vary too much over time. If variability in exposure over time is large and differences in exposure between individuals are relatively small, this may lead to underestimation of the exposure–response relationship. This means that, for a sound assessment of health risk, biomarkers that reflect cumulative exposure over a long period of time are preferred over biomarkers with short half-lives. Most of the existing biomarkers such as metabolites in body fluids usually have rather short half-lives, typically less than 1–2 days. Some adducts to DNA show somewhat longer half-lives. The current limit to persistence of biomarkers reflecting cumulative exposure over time is from adducts to haemoglobin with a half-life of 4 months. Some specific organic substances may be more persistent due to storage in adipose tissue or metals in kidneys, nails and hair. The metabonomics, proteomics and present gene expression profiling approaches do not provide a perspective to the availability of more persistent biomarkers and most approaches discussed to date show that it is difficult to interpret study outcomes in terms of exposure to a specific xenobiotic factor. Research efforts should focus on improvement and validation of currently available approaches in the field of addition products to DNA and proteins. Promising new developments may be phosphotriester DNA adducts and adducts to more long-lived proteins such as histones.     

Exposure reconstruction for reducing uncertainty in risk assessment: example using MTBE biomarkers and a simple pharmacokinetic model

Adverse health risks from environmental agents are generally related to average (long-term) exposures. Because a given individual's contact with a pollutant is highly variable and dependent on activity patterns, local sources and exposure pathways, simple 'snapshot' measurements of surrounding environmental media may not accurately assign the exposure level. Furthermore, susceptibility to adverse effects from contaminants is considered highly variable in the population so that even similar environmental exposure levels may result in differential health outcomes in different individuals. The use of biomarker measurements coupled to knowledge of rates of uptake, metabolism and elimination has been suggested as a remedy for reducing this type of uncertainty. To demonstrate the utility of such an approach, we invoke results from a series of controlled human exposure tests and classical first-order rate kinetic calculations to estimate how well spot measurements of methyl tertiary butyl ether and the primary metabolite, tertiary butyl alcohol, can be expected to predict different hypothetical scenarios of previous exposures. We found that blood and breath biomarker measurements give similar results and that the biological damping effect of the metabolite production gives more stable estimates of previous exposure. We also explore the value of a potential urinary biomarker, 2-hydroxyisobutyrate suggested in the literature. We find that individual biomarker measurements are a valuable tool in reconstruction of previous exposures and that a simple pharmacokinetic model can identify the time frames over which an exogenous chemical and the related chemical biomarker are useful. These techniques could be applied to broader ranges of environmental contaminants to assess cumulative exposure risks if ADME (Absorption, Distribution, Metabolization and Excretion) is understood and systemic biomarkers can be measured.     

Use of biomarkers in clinical trials of Alzheimer disease: from concept to application

Research progress during the last decades has resulted in an unprecedented accumulation of knowledge regarding the molecular pathogenesis of Alzheimer disease (AD). These important achievements toward clarifying the mechanistic processes underlying AD are being translated into ongoing development of biomarkers and their use in clinical trials. AD biomarkers are biochemical and anatomical variables (e.g. cerebrospinal fluid, positron emission tomography, and structural MRI) that measure AD-related pathologic features (i.e. amyloid deposition and neurodegeneration) in vivo. Biomarkers are utilized as 'diagnostic biomarkers' and/or 'endpoint biomarkers' in symptomatic or disease-modifying clinical trials. Diagnostic biomarkers play an important role in population enrichment by refining selection criteria, stratifying populations, and increasing the statistical power of trials. Endpoint biomarkers may be used as outcome measures to monitor the rate of disease progression and detect treatment effects. AD biomarkers do not reach abnormal levels or peak simultaneously, but do so in a time-dependent order. The choice of biomarkers for a clinical trial must take into consideration the type of therapeutic intervention, the clinical stage of AD, and the time dependence of biomarker changes during disease progression. The combination of amyloid and neurodegeneration biomarkers is highly desirable since they capture different aspects of the disease. Clinical trials for every clinical stage of AD would benefit from quantification and standardization of biomarkers. However, this is still a work in progress.     

The impact of storage effects in biobanks on biomarker discovery in systems biology studies

Sample handling and storage conditions in specimens frozen over long periods of time can severely impact marker levels. If laboratory technologies, practices and related protocols change over time, biomarker studies are potentially biased and report erroneous results. These issues and pitfalls are often overlooked in system biology studies using previously collected and stored materials, and are likely to be one notable cause for biomarker candidates failing to be validated. We present results from simulation studies quantifying the loss in statistical power to detect true biomarkers, due to diminishing concentration of analytes in samples subject to poor handling and storage conditions.     

A framework for the joint modeling of longitudinal diagnostic outcome data and latent infection status: application to investigating the temporal relationship between infection and disease

For many diseases the infection status of individuals cannot be observed directly, but can only be inferred from biomarkers that are subject to measurement error. Diagnosis of infection based on observed symptoms can itself be regarded as an imperfect test of infection status. The temporal relationship between infection and marker outcomes may be complex, especially for recurrent diseases where individuals can experience multiple bouts of infection. We propose an approach that first models the unobserved longitudinal infection status of individuals conditional on relevant covariates, and then jointly models the longitudinal sequence of biomarker outcomes conditional on infection status and covariate information through time, thus resulting in a joint model for longitudinal infection and biomarker sequences. This model can be used to investigate the temporal dynamics of infection, and to evaluate the usefulness of biomarkers for monitoring purposes. Our work is motivated and illustrated by a longitudinal study of bovine digital dermatitis (BDD) on commercial dairy farms in North West England and North Wales, in which the infection of interest is Treponeme spp., and the biomarkers of interest are a continuous enzyme-linked immunosorbent assay test outcome and a dichotomous outcome, foot lesion status. BDD is known to be one of the possible causes of foot lesions in cows.