Development of a biomarker-based index for assessing the ecotoxic potential of aquatic sites

The use of biochemical or physiological measurements as indicators of ecotoxicity is under constant development and has the advantage of delineating effects before the appearance of disease. However, these biomarkers are often part of a battery of tests, and it is difficult to integrate them together to gain an overall view of an organism's health. The aim of this study was to develop an index that could integrate the data derived from a battery of biomarkers for application to both spatial and temporal studies. Mya arenaria clams were collected at different sites along the Saguenay Fjord (Quebec, Canada). Six biomarkers were measured: metallothioneins, DNA strand breakage, lipid peroxidation, vitellin-like proteins, phagocytosis, and non-specific esterase activity in haemocytes. A biomarker index was obtained by summing the biomarker values expressed in term of classes. Classes were determined by a distribution-free approach derived from the theory of rough sets. The results of the spatial study show that the index values discriminated well between contaminated and uncontaminated sites. The highly polluted sites had the highest index values (18 compared with a reference value of 14). In the temporal study, the index was also able to highlight possible contamination-induced alterations, even though the interpretation of temporal variation is complicated by natural variations occurring throughout the year. A control chart approach is proposed for determining contaminated sites in both spatial and temporal surveys.     

Assessment of the health status of Donax trunculus from the Gulf of Tunis using integrative biomarker indices

For a few years, the littoral of the Gulf of Tunis has been subjected to agricultural activities, industrial, urban and tourist development and consequently submitted to the impact of many chemical and physical stressors. Sub-individual responses (biochemical biomarkers) in bivalves can be used as an early warning system for ecotoxicological risk assessment. However, the influence of many confounding factors such as ecological (e.g. temperature and salinity) or physiological (e.g. reproductive and nutritive status) parameters on biomarker responses must be taken carefully into account. The aim of the present study was to integrate into indices (Integrated Biomarker Response: IBR and Health Status Index: HSI) individual responses of biochemical biomarkers (acetylcholinesterase, catalase, glutathione-S-transferase, thiobarbituric acid reactive substance levels, lactate dehydrogenase and metallothionein-like proteins) and energy reserves (glycogen, lipid levels and total protein concentrations) at sub-individual level, and condition index at individual level in Donax trunculus originating from 4 sites contrasted by their level of contamination in the Gulf of Tunis. Integrated biomarkers indices have been revealed to be efficient and easy tools for environmental managers. Responses of IBR and HSI were compared and their strengths and weaknesses discussed. Results showed that HSI could be an easy tool for risk management whereas IBR results were more informative with regard to the temporal contribution of each biomarker considered in the battery used in the present work. In bivalves from the reference Sidi Jehmi site, the contribution of energy reserves and the condition index reflecting a good general health status clearly influenced the temporal variations of IBR whereas in bivalves from the multi-contaminated Radès Méliane site, temporal variations of IBR were induced mainly by the responses of pollution biomarkers. Previous investigations concerning other level of biological organization (population) are in accordance with the present data (Tlili et al., 2010, Tlili et al., 2011).     

Scale of classification based on biochemical markers in mussels: application to pollution monitoring in Mediterranean coasts and temporal trends.

A battery of biochemical parameters was used to evaluate the response of mussels to a contaminated coastal environment. A multimarker approach was developed, establishing a scale for the classification of the water quality in European coastal sites (BIOMAR European programme). This study allows the evaluation of the temporal trends of this scale when applied to selected sites of European Mediterranean coast (BEEP Biological Effects of Environmental Pollution in Marine Coastal Ecosystems: European programme). Acetylcholinesterase activity (AChE) is highly sensitive to organophosphorus and carbamate insecticides and, to some extent, also to heavy metals. Catalase activity (CAT) and lipid oxidation (evaluated as malonedialdehyde) are markers of oxidative stress, glutathione S-transferase (GST) activity is related to conjugation of organic compounds and benzo(a)pyrene hydroxylase activity (BPH) is a marker of effect of certain planar organic compounds (e.g. polycyclic aromatic hydrocarbons, PAHs). These parameters were measured either in gills (AChE, GST) or digestive gland (BPH, GST, CAT, MDA). For each biomarker, a discriminatory factor was calculated (maximum variation range/confidence interval) and a response index was allocated. For each site, a Multimarker Pollution Index (MPI) was calculated as the sum of the response index of each of the five more discriminating biomarkers. As the result of our calculation method, the quality of the coastal environment at each site can be classified according to a five levels scale. Samples collected for five cruises in May 2001, 2002, 2003, and September 2001 and 2002 showed MPI evolutions. The results show that water quality can be classified from class 1 (clean areas in some sites of France, Italy and Spain) to class 4 (high pollution in main harbours). Results of the use of the biomarker scale in WP3 (Work Package Concernant Biomonitoring Programmes in Mediterranean Sea) during the BEEP programme make a strong contribution to the establishment of standardized strategies and methods for internationally agreed protocols for biomarker-based monitoring programmes. In comparison with scale pollution methodology used in the BIOMAR programme, the main contribution of BEEP was (1) to select from discriminatory analysis the biomarkers to be included in calculation of scale pollution; (2) to improve the use of the biomarker index in order to identify the main contaminants by analysis of individual contributions to the MPI; and (3) to apply methodology for temporal trends at sampled sites.     

Scale of classification based on biochemical markers in mussels: application to pollution monitoring in Mediterranean coasts and temporal trends

A battery of biochemical parameters was used to evaluate the response of mussels to a contaminated coastal environment. A multimarker approach was developed, establishing a scale for the classification of the water quality in European coastal sites (BIOMAR European programme). This study allows the evaluation of the temporal trends of this scale when applied to selected sites of European Mediterranean coast (BEEP Biological Effects of Environmental Pollution in Marine Coastal Ecosystems: European programme). Acetylcholinesterase activity (AChE) is highly sensitive to organophosphorus and carbamate insecticides and, to some extent, also to heavy metals. Catalase activity (CAT) and lipid oxidation (evaluated as malonedialdehyde) are markers of oxidative stress, glutathione S-transferase (GST) activity is related to conjugation of organic compounds and benzo(a)pyrene hydroxylase activity (BPH) is a marker of effect of certain planar organic compounds (e.g. polycyclic aromatic hydrocarbons, PAHs). These parameters were measured either in gills (AChE, GST) or digestive gland (BPH, GST, CAT, MDA). For each biomarker, a discriminatory factor was calculated (maximum variation range/confidence interval) and a response index was allocated. For each site, a Multimarker Pollution Index (MPI) was calculated as the sum of the response index of each of the five more discriminating biomarkers. As the result of our calculation method, the quality of the coastal environment at each site can be classified according to a five levels scale. Samples collected for five cruises in May 2001, 2002, 2003, and September 2001 and 2002 showed MPI evolutions. The results show that water quality can be classified from class 1 (clean areas in some sites of France, Italy and Spain) to class 4 (high pollution in main harbours). Results of the use of the biomarker scale in WP3 (Work Package Concernant Biomonitoring Programmes in Mediterranean Sea) during the BEEP programme make a strong contribution to the establishment of standardized strategies and methods for internationally agreed protocols for biomarker-based monitoring programmes. In comparison with scale pollution methodology used in the BIOMAR programme, the main contribution of BEEP was (1) to select from discriminatory analysis the biomarkers to be included in calculation of scale pollution; (2) to improve the use of the biomarker index in order to identify the main contaminants by analysis of individual contributions to the MPI; and (3) to apply methodology for temporal trends at sampled sites.     

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.     

Scoring approach based on fish biomarkers applied to French river monitoring

The aim was to apply a multimarker scoring approach as complementary to freshwater monitoring programmes carried out by the Water Agency Adour-Garonne. Fish (chub, barbel and trout) were collected in 11 sites in rivers in south-west France. Five biomarkers of response were measured either in muscle or brain for acetylcholinesterase (AChE) and in liver for glutathione S-transferase, catalase and 7-ethoxyresorufine O-deethylase. As a result of multivariate analysis, sites were clearly discriminated mainly by 7-ethoxyresorufine O-deethylase and acetylcholinesterase activities. According to the scoring approach, a multimarker pollution index was calculated for each sampling site as the sum of the response index of the five measured biomarkers (pollution index). Sorting was established by ranging the sites from lightly to highly contaminated locations.     

Scoring approach based on fish biomarkers applied to French river monitoring

The aim was to apply a multimarker scoring approach as complementary to freshwater monitoring programmes carried out by the Water Agency Adour–Garonne. Fish (chub, barbel and trout) were collected in 11 sites in rivers in south-west France. Five biomarkers of response were measured either in muscle or brain for acetylcholinesterase (AChE) and in liver for glutathione S-transferase, catalase and 7-ethoxyresorufine O-deethylase. As a result of multivariate analysis, sites were clearly discriminated mainly by 7-ethoxyresorufine O-deethylase and acetylcholinesterase activities. According to the scoring approach, a multimarker pollution index was calculated for each sampling site as the sum of the response index of the five measured biomarkers (pollution index). Sorting was established by ranging the sites from lightly to highly contaminated locations.     

Assessing environmental conditions of the Río Champotón (México) using diverse indices and biomarkers in the fish Astyanax aeneus (Günther, 1860)

The upper reaches of the Río Champotón in southwestern Mexico are largely unknown to science. This river is sustaining anthropogenic and natural disturbances by deforestation, substitution of natural vegetation, and agricultural non-point source pollution. Furthermore, hurricanes provoke flooding of these areas, exposing the aquatic biota to pollutants. Several approaches were used to assess the effects of environmental conditions along the freshwater portion of the Río Champotón in the native fish Astyanax aeneus and to determine the feasibility of extrapolating responses between levels of organization, and to link spatial and seasonal fish responses with water quality features. A water quality index (WQI) was employed as an indicator of environmental conditions, a set of sub-organismal biomarkers in A. aeneus (lipid peroxidation, glutathione S-transferase, ethoxyresorufin-O-deethylase and lactate dehydrogenase) was monitored to determine the integrated biomarker response (IBR), and somatic indices (individual and population-level responses; gonadosomatic, hepatosomatic, and condition factor) were characterized. Canonical correspondence analysis revealed spatial and seasonal variations in all the approaches. The dry and the wet season (April and July) exhibited low WQI scores, but decreased hardness and conductivity values as well as increased WQI values occurred during the post-hurricane season when crop fields adjacent to the riverbed were inundated. Although the WQI suggested low levels of contamination, the biomarkers did not remain static; wide fluctuations reflected fish exposure to seasonal stressors that provoked impairments in biological responses. All biomarker values were high, mainly in the post-hurricane season (November). The IBR exhibited two peaks that may be related to stress periods (April and November), with decreasing post-stress values suggesting the existence of homeostatic processes in the fish. The somatic indices reflected some seasonal and spatial differences at the population level – the gonadosomatic and hepatosomatic indices peaked in the middle of July and November, respectively – but the condition factor remained constant among sites and study periods. Future studies are needed to explore the link between the fish biomarker responses and possible causal stressors (natural or anthropogenic).     

Biomarkers of animal health: integrating nutritional ecology,endocrine ecophysiology,ecoimmunology, and geospatial ecology

Diverse biomarkers including stable isotope, hormonal, and ecoimmunological assays are powerful tools to assess animal condition. However, an integrative approach is necessary to provide the context essential to understanding how biomarkers reveal animal health in varied ecological conditions. A barrier to such integration is a general lack of awareness of how shared extraction methods from across fields can provide material from the same animal tissues for diverse biomarker assays. In addition, the use of shared methods for extracting differing tissue fractions can also provide biomarkers for how animal health varies across time. Specifically, no study has explicitly illustrated the depth and breadth of spacial and temporal information that can be derived from coupled biomarker assessments on two easily collected tissues: blood and feathers or hair. This study used integrated measures of glucocorticoids, stable isotopes, and parasite loads in the feathers and blood of fall‐migrating Northern saw‐whet owls (Aegolius acadicus) to illustrate the wealth of knowledge about animal health and ecology across both time and space. In feathers, we assayed deuterium (δD) isotope and corticosterone (CORT) profiles, while in blood we measured CORT and blood parasite levels. We found that while earlier migrating owls had elevated CORT levels relative to later migrating birds, there was also a disassociation between plasma and feather CORT, and blood parasite loads. These results demonstrate how these tissues integrate time periods from weeks to seasons and reflect energetic demands during differing life stages. Taken together, these findings illustrate the potential for integrating diverse biomarkers to assess interactions between environmental factors and animal health across varied time periods without the necessity of continually recapturing and tracking individuals. Combining biomarkers from diverse research fields into an integrated framework hold great promise for advancing our understanding of environmental effects on animal health.     

Scale of classification based on biochemical markers in mussels: application to pollution monitoring in European coasts

A battery of biochemical parameters is used to evaluate the response of mussels to a contaminated coastal environment. In the European BIOMAR programme, a multimarker approach was developed, establishing a scale for the classification of the water quality in European coastal sites. AChE activity is highly sensitive to organophosphorus and carbamate insecticides but also to heavy metals. Catalase activity and lipid oxidation (evaluated as MDA) are markers of oxidative stress, GST activity is related to the conjugation of organic compounds and BPH activity is a marker of planar compounds (e.g. PAHs). These parameters were measured either in gills (AChE, GST) or digestive gland (BPH, GST, CAT, MDA). Contamination levels were estimated by measurement of PAHs and heavy metals in animals. For each biomarker, a discriminatory factor was calculated (maximum variation range/confidence interval) and a response index was allocated. For each site, a global response index was calculated as the sum of the response index of each of the five biomarkers. As a result of our calculation method, the quality of the coastal environment at each site can be classified with a five level scale. Mussels were collected during five cruises in 1995-1996 on the Baltic and Mediterranean coasts. The results show that water quality ranged from class 1 (clean areas in some sites of the French Riviera, Spanish Costa Brava and the Baltic coast) to class 5 (high pollution in main harbours, e.g. Kiel and Toulon). Some areas fall into class 4, e.g. Carteau, Cortiou, Barcelona, Warnemunde, Swinemunde, Ebro delta. The global Biomarker Index was positively correlated with the level of PAHs in mussels in Baltic transects. A number of other contaminants or stressors may be present in the marine environment and the Biomarker Index appeared to be relevant to classify coastal environmental pollution.     

Scale of classification based on biochemical markers in mussels: application to pollution monitoring in European coasts

A battery of biochemical parameters is used to evaluate the response of mussels to a contaminated coastal environment. In the European BIOMAR programme, a multimarker approach was developed, establishing a scale for the classification of the water quality in European coastal sites. AChE activity is highly sensitive to organophosphorus and carbamate insecticides but also to heavy metals. Catalase activity and lipid oxidation (evaluated as MDA) are markers of oxidative stress, GST activity is related to the conjugation of organic compounds and BPH activity is a marker of planar compounds (e.g. PAHs). These parameters were measured either in gills (AChE, GST) or digestive gland (BPH, GST, CAT, MDA). Contamination levels were estimated by measurement of PAHs and heavy metals in animals. For each biomarker, a discriminatory factor was calculated (maximum variation range/confidence interval) and a response index was allocated. For each site, a global response index was calculated as the sum of the response index of each of the five biomarkers. As a result of our calculation method, the quality of the coastal environment at each site can be classified with a five level scale. Mussels were collected during five cruises in 1995-1996 on the Baltic and Mediterranean coasts. The results show that water quality ranged from class 1 (clean areas in some sites of the French Riviera, Spanish Costa Brava and the Baltic coast) to class 5 (high pollution in main harbours, e.g. Kiel and Toulon). Some areas fall into class 4, e.g. Carteau, Cortiou, Barcelona, Warnemunde, Swinemunde, Ebro delta. The global Biomarker Index was positively correlated with the level of PAHs in mussels in Baltic transects. A number of other contaminants or stressors may be present in the marine environment and the Biomarker Index appeared to be relevant to classify coastal environmental pollution.     

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.     

Semiparametric transformation models for multiple continuous biomarkers in ROC analysis

Recent technological advances continue to provide noninvasive and more accurate biomarkers for evaluating disease status. One standard tool for assessing the accuracy of diagnostic tests is the receiver operating characteristic (ROC) curve. Few statistical methods exist to accommodate multiple continuous‐scale biomarkers in the framework of ROC analysis. In this paper, we propose a method to integrate continuous‐scale biomarkers to optimize classification accuracy. Specifically, we develop semiparametric transformation models for multiple biomarkers. We assume that unknown and marker‐specific transformations of biomarkers follow a multivariate normal distribution. Our models accommodate biomarkers subject to limits of detection and account for the dependence among biomarkers by including a subject‐specific random effect. We also propose a diagnostic measure using an optimal linear combination of the transformed biomarkers. Our diagnostic rule does not depend on any monotone transformation of biomarkers and is not sensitive to extreme biomarker values. Nonparametric maximum likelihood estimation (NPMLE) is used for inference. We show that the parameter estimators are asymptotically normal and efficient. We illustrate our semiparametric approach using data from the Endometriosis, Natural History, Diagnosis, and Outcomes (ENDO) study.     

生态过程模型敏感参数最优取值的时空异质性分析——以BIOME-BGC模型为例

生态过程模型是当前研究陆地生态系统水循环、碳循环有力的工具,但此类模型参数众多,参数的合理取值对模型模拟结果有重要影响.以往研究对模型参数的敏感性以及参数的优化取值有诸多的分析和讨论,但有关参数最优取值的时空异质性关注较少.本文以BIOME-BGC模型为例,在常绿阔叶林、落叶阔叶林、C3草地3种植被类型下,通过构建敏感性判别指数,筛选出模型的敏感参数,并在每种植被类型下选取两个试验站点,使用模拟退火算法结合实测通量数据构建目标函数,获取各站点敏感参数逐月的最优取值,然后构建时间异质性判别指数、空间异质性判别指数和时空异质性判别指数对模型敏感参数最优取值的时空异质性进行定量分析.结果表明:BIOME-BGC模型在3种植被类型下遴选出的敏感参数大部分一致,少数有差异,但参数的敏感性强弱在不同植被类型下的表现不尽相同;BIOME-BGC模型敏感参数的最优取值,大都具有不同程度的时空异质性,但不同植被类型下,敏感参数最优取值的时空异质性表现各异;敏感参数中与植被生理、生态相关的参数,其时空异质性相对较小,而与环境、物候相关的参数,其时空异质性普遍较大;在3种植被类型下,模型敏感参数最优取值的时间异质性与空间异质性表现出显著的线性相关性;依据其最优取值的时空异质性,可对BIOME-BGC模型敏感参数进行类型划分,以便在实践应用中采取不同的参数率定策略.本研究结论有助于加深对生态过程模型参数特性及最优取值的理解,可为实践应用中模型参数的合理取值提供一种思路和参考.     

Peptide biomarkers as evidence of perchlorate biodegradation

Perchlorate is a known health hazard for humans, fish, and other species. Therefore, it is important to assess the response of an ecosystem exposed to perchlorate contamination. The data reported here show that a liquid chromatography-mass spectrometry-based proteomics approach for the detection of perchlorate-reducing enzymes can be used to measure the ability of microorganisms to degrade perchlorate, including determining the current perchlorate degradation status. Signature peptides derived from chlorite dismutase (CD) and perchlorate reductase can be used as biomarkers of perchlorate presence and biodegradation. Four peptides each derived from CD and perchlorate reductase subunit A (PcrA) and seven peptides derived from perchlorate reductase subunit B (PcrB) were identified as signature biomarkers for perchlorate degradation, as these sequences are conserved in the majority of the pure and mixed perchlorate-degrading microbial cultures examined. However, chlorite dismutase signature biomarker peptides from Dechloromonas agitata CKB were found to be different from those in other cultures used and should also be included with selected CD biomarkers. The combination of these peptides derived from the two enzymes represents a promising perchlorate presence/biodegradation biomarker system. The biomarker peptides were detected at perchlorate concentrations as low as 0.1 mM and at different time points both in pure cultures and within perchlorate-reducing environmental enrichment consortia. The peptide biomarkers were also detected in the simultaneous presence of perchlorate and an alternate electron acceptor, nitrate. We believe that this technique can be useful for monitoring bioremediation processes for other anthropogenic environmental contaminants with known metabolic pathways.     

某焦化场地土壤中多环芳烃分布的三维空间插值研究

为准确界定污染场地土壤中多环芳烃在3维条件下的污染分布范围和受污染土方量,选择我国某焦化污染场地苯并(a)芘分布为研究对象,对比研究Krig-3D、IDW-Shepard、IDW-(Franke/Nielson)以及Nearest Neighbor 4种3维插值方法对界定污染范围的不确定性影响。结果表明,不同3维插值模型计算结果差异较大,交叉验证结果显示Krig-3D插值模型插值精度最高,插值结果能较真实地反映场地实际污染情况。通过修复目标对比确定进一步表明,基于4种插值模型计算所得的污染土壤土方量分别为8.51×105、5.62×105、7.12×105、1.09×106m3,选择合理的插值模型将对预测污染范围的不确定性产生重要影响。研究结果对分析污染范围和修复治理过程土方量确定提供重要参考。     

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.     

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.     

Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years

Resting-state functional MRI (rs-fMRI) permits study of the brain’s functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures—network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)–was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.     

Introduction to proteomics: tools for the new biology

Towards revolutionary biomarkers, a considerable amount of research funds and time have been dedicated to proteomics. Although the discovery of novel biomarkers at the dawn of proteomics was a promising development, only a few identified biomarkers seemed to be beneficial for cancer patients. We may need to approach this issue differently, instead of only extending the conventional approaches that have been used historically. The study of biomarkers is essentially a study of diseases and the biochemistry relating to peptide, protein and post-translational modifications is only a tool. A problem-oriented approach should be needed in biomarker development. Clinician participation in the study of biomarkers will lead to realistic, practical and interesting biomarker candidates, which justify the time and expense involved in validation studies. Although discussion in this article is focused on cancer biomarkers, it can generally be applied to biomarker studies for other diseases.