Susan Sumner

Personalized exposure assessment : Promising approaches for human environmental health research

New technologies and methods for assessing human exposure to chemicals, dietary and lifestyle factors, infectious agents, and other stressors provide an opportunity to extend the range of human health investigations and advance our understanding of the relationship between environmental exposure and disease. An ad hoc Committee on Environmental Exposure Technology Development was convened to identify new technologies and methods for deriving personalized exposure measurements for application to environmental health studies. The committee identified a “toolbox” of methods for measuring external (environmental) and internal (biologic) exposure and assessing human behaviors that influence the likelihood of exposure to environmental agents. The methods use environmental sensors, geographic information systems, biologic sensors, toxicogenomics, and body burden (biologic) measurements. We discuss each of the methods in relation to current use in human health research; specific gaps in the development, validation, and application of the methods are highlighted. We also present a conceptual framework for moving these technologies into use and acceptance by the scientific community. The framework focuses on understanding complex human diseases using an integrated approach to exposure assessment to define particular exposure–disease relationships and the interaction of genetic and environmental factors in disease occurrence. Improved methods for exposure assessment will result in better means of monitoring and targeting intervention and prevention programs.

Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Funds Metabolomics Program, provides data from the Common Funds Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.

Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice.

The early life microbiome plays important roles in host immunological and metabolic development. Because the incidence of type 1 diabetes (T1D) has been increasing substantially in recent decades, we hypothesized that early-life antibiotic use alters gut microbiota, which predisposes to disease. Using non-obese diabetic mice that are genetically susceptible to T1D, we examined the effects of exposure to either continuous low-dose antibiotics or pulsed therapeutic antibiotics (PAT) early in life, mimicking childhood exposures. We found that in mice receiving PAT, T1D incidence was significantly higher, and microbial community composition and structure differed compared with controls. In pre-diabetic male PAT mice, the intestinal lamina propria had lower Th17 and Treg proportions and intestinal SAA expression than in controls, suggesting key roles in transducing the altered microbiota signals. PAT affected microbial lipid metabolism and host cholesterol biosynthetic gene expression. These findings show that early-life antibiotic treatments alter the gut microbiota and its metabolic capacities, intestinal gene expression and T-cell populations, accelerating T1D onset in non-obese diabetic mice.

Analysis of Human Serum and Whole Blood for Mineral Content by ICP-MS and ICP-OES: Development of a Mineralomics Method

Minerals are inorganic compounds that are essential to the support of a variety of biological functions. Understanding the range and variability of the content of these minerals in biological samples can provide insight into the relationships between mineral content and the health of individuals. In particular, abnormal mineral content may serve as an indicator of illness. The development of robust, reliable analytical methods for the determination of the mineral content of biological samples is essential to developing biological models for understanding the relationship between minerals and illnesses. This paper describes a method for the analysis of the mineral content of small volumes of serum and whole blood samples from healthy individuals. Interday and intraday precision for the mineral content of the blood (250xa0μL) and serum (250xa0μL) samples was measured for eight essential minerals—sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), iron (Fe), zinc (Zn), copper (Cu), and selenium (Se)—by plasma spectrometric methods and ranged from 0.635 to 10.1xa0% relative standard deviation (RSD) for serum and 0.348–5.98xa0% for whole blood. A comparison of the determined ranges for ten serum samples and six whole blood samples provided good agreement with literature reference ranges. The results demonstrate that the digestion and analysis methods can be used to reliably measure the content of these minerals and potentially of other minerals.

Metabolomics of urine for the assessment of microvesicular lipid accumulation in the liver following isoniazid exposure

This study was conducted to develop a noninvasive marker of hepatic microvesicular lipid accumulation (MVLA), a histopathological effect currently diagnosed in humans following liver biopsy. MVLA is detected in animal studies of chemicals and drugs and occurs in some humans exposed to chemicals or pharmaceuticals. Because MVLA is a reversible histopathology, early detection of MVLA using a noninvasive method, could aid clinicians in the treatment of patients taking drugs that are known to induce this injury. Isoniazid (INH) was selected as a model compound for this investigation, because MVLA occurs in tuberculosis (TB) patients treated with a combination therapy, which includes INH. This study used male rats dosed daily with INH at 0, 10, or 300xa0mg/kg/day for up to 8xa0days. Urine, blood, and liver were obtained following 1 and 8xa0days. NMR metabolomics of urine revealed markers that correlated (100%) with the findings of MVLA in the right, left, and median liver lobes in 4/9 rats administered the high dose of INH for 8xa0days. Metabolomics of liver extracts also revealed markers that correlated with the MVLA injury. Serum enzymes that are clinically used to assess liver injury were not consistently correlated to the findings of MVLA. Metabolite changes consistent with the presence of MVLA correlated with interruptions in inositol, carbohydrate, glycerolipid, and glyoxylate metabolism. This study reveals markers that could find pre-clinical use, provides insights into mechanisms involved in MVLA, and demonstrates the need for the validation of noninvasive MVLA markers in human patients.

Multi-walled carbon nanotube directed gene and protein expression in cultured human aortic endothelial cells is influenced by suspension medium

The use and production of multi-walled carbon nanotubes (MWCNTs) have significantly increased over the last decade due to their versatility in numerous applications. Their unique physical and chemical properties make them desirable for various biomedical applications, but the same properties also raise concerns about their safety to human health, particularly at the cellular level. The vascular endothelium could be exposed to nanomaterials either by direct intravenous administration in nanomedicine or by translocation following inhalational exposure in an occupational setting. We hypothesized that direct exposure to MWCNTs will increase the expression of inflammatory markers in human aortic endothelial cells (HAEC). We also investigated the effect of the route of exposure on activation by changing the suspension medium of the MWCNTs. HAEC were treated in vitro with MWCNTs (1 or 10 μg/cm(2)) suspended in either cell culture medium [(M)-MWCNTs] or 10% clinical grade pulmonary surfactant [(S)-MWCNTs]. The zeta potential of the (S)-MWCNTs was significantly more negative than the (M)-MWCNTs suggesting a more stable suspension. Treatment of HAEC with (S)-MWCNTs; as compared to (M)-MWCNTs resulted in a significantly higher up-regulation of mRNA transcripts for cell adhesion molecules VCAM1, SELE, ICAM1 and the chemokine CCL2. Time dependent changes in VCAM1 and CCL2 protein levels were confirmed by immunofluorescence, flow cytometry and ELISA. A label free quantitative mass spectrometry proteomic analysis was utilized to compare protein expression patterns between the two suspensions of MWCNTs. We identified significant expression changes in >200 unique proteins in MWCNT treated HAEC. However, the two suspensions of MWCNTs resulted in different protein expression patterns with the eIF2 pathway as the only common pathway identified between the two suspensions. These data suggest that direct exposure to MWCNTs induces acute inflammatory and protein expression changes in HAEC, which is influenced by the type of media used for suspension of MWCNTs and their resulting zeta potential.

Integrating metabolomic signatures and psychosocial parameters in responsivity to an immersion treatment model for adolescent obesity

With childhood obesity tripling in the past two decades alone, developing effective treatment to promote long-term weight loss and maintenance is critical. Unfortunately, while many children respond favorably to behavioral treatments, failure to lose and maintain weight loss is common. There is a critical need to determine non-invasive markers that can serve to predict an individual’s likelihood of having a positive response to treatment, and to monitor the treatment progress for early identification of those who may be at risk for relapse (e.g., weight gain) in order to provide more early, targeted and ongoing intervention of these high risk individuals. An exploratory investigation was conducted to determine: (a) the relevance of using metabolomics in delivering non-invasive markers to predict an individual’s responsiveness to an immersion treatment program, and (b) to integrate psychosocial and metabolomic profiles that can further serve to predict an adolescent’s positive response to weight loss. Obese adolescents (12–18xa0years old, BMI >95th percentile) attending a 3xa0weeks immersion healthy lifestyle camp, Take Off 4-Health (TO4-H), were recruited to provide first morning void urine samples and to complete psychosocial and health-behavior inventories at baseline and at the end of the 3xa0weeks program. Subjects were categorized as responders (decline of ≥0.5xa0BMI units) or non-responders (decline of <0.5xa0BMI units) based on weight loss between baseline and the end of the 3xa0weeks program. Subjects were additionally classified as having either a healthy or impaired self-esteem (impaired ≥20 or healthy <20) and depression (impaired ≥15 or healthy <15) at baseline and the end of the 3xa0weeks camp-based on their responses to psychosocial inventories. NMR based metabolomics was used to generate signatures of low molecular components in urine. Using multivariate analysis of signals, findings demonstrated a unique pattern for treatment responders vs. non-responders. Moreover, inspection of loadings plots and variable importance plots enabled the identification of a subset of metabolites and psychosocial variables that may assist in our understanding and prediction of adolescent responsivity to treatment for weight loss. For the psychosocial variables, improvement in self-esteem and depression did not correlate with weight loss, multivariate analysis of urinary metabolomics data using healthy and impaired classifications for self-esteem and depression did enable the determination of subsets of metabolites that best associated with impaired self-esteem and depression. Integrating the metabolomic and psychosocial data provided a marker profile pointing to both biochemical (metabolite) and psychosocial indexes (self-esteem and depression) that are most relevant to determining an individual’s overall response to treatment. This study demonstrates the utility of metabolomics in providing non-invasive markers that upon validation in a larger sample may be an extremely important tool in the early detection and clinical management of adolescent obesity.

Metabolomics of brain and reproductive organs: characterizing the impact of gestational exposure to butylbenzyl phthalate on dams and resultant offspring

Phthalates are plasticizers finding wide spread use in industrial and household products, with measureable levels of phthalate-derived metabolites in the general US population. Phthalates have endocrine disruption potential and have been implicated as obesogens. Our exploratory investigation to reveal the impact of in utero exposure to a phthalate on the biochemical profiles of the brain, testes, and uterus of prepubertal offspring, and of tissues from dams administered butylbenzyl phthalate (BBP). Pregnant rats (three per group) were administered (on gestation day 14–21) corn oil (control), or 25xa0mg/kg/day or 750xa0mg/kg/day BBP in corn oil. Tissues were collected from each of the dams on postnatal day (pnd) 21 (~3xa0weeks after the end of BBP administration), and from each of the pups on pnd 26 (~4 weeks after birth to dams administered vehicle or BBP during gestation) and processed for metabolomics analysis. Multivariate data analyses revealed metabolites that best distinguished the exposed and control groups. The metabolites most important to distinguishing the study groups were tested for significance using the exact Wilcoxon rank-sum test. Male pups had significant differences (control versus BBP dose groups) in levels of metabolites for both the brain and testes even at the Pxa0<xa00.01 level. However, female pups and dams had significant testing for the uterus only at the Pxa0=xa00.1 level tested. Female pups also had some significant differences for the brain with P values between 0.5 and 0.1. Amino acid metabolism (male and female pups) and phospholipid metabolism (male pups) were perturbed for the brain. Amino acid metabolism, purine metabolism, and TCA cycle were perturbed for tests and uterus. This study demonstrated the use of metabolomics to reveal metabolic perturbations in tissues of offspring following in utero exposures, and suggests the use of this approach for determining the impact of exposure past the time of the presence of the parent compound and metabolites derived from the parent compound.

Preterm neonatal urinary renal developmental and acute kidney injury metabolomic profiling: An exploratory study

BackgroundAcute kidney injury (AKI) staging has been developed in the adult and pediatric populations, but these do not yet exist for the neonatal population. Metabolomics was utilized to uncover biomarkers of normal and AKI-associated renal function in preterm infants. The study comprised 20 preterm infants with an AKI diagnosis who were matched by gestational age and gender to 20 infants without an AKI diagnosis.MethodsUrine samples from pre-term newborn infants collected on day 2 of life were analyzed using broad-spectrum nuclear magnetic resonance (NMR) metabolomics. Multivariate analysis methods were used to identify metabolite profiles that differentiated AKI and no AKI, and to identify a metabolomics profile correlating with gestational age in infants with and without AKI.ResultsThere was a clear distinction between the AKI and no-AKI profiles. Two previously identified biomarkers of AKI, hippurate and homovanillate, differentiated AKI from no-AKI profiles. Pathway analysis revealed similarities to cholinergic neurons, prenatal nicotine exposure on pancreatic β cells, and amitraz-induced inhibition of insulin secretion. Additionally, a pH difference was noted. Both pH and the metabolites were found to be associated with AKI; however, only the metabotype was a significant predictor of AKI. Pathways for the no-AKI group that correlated uniquely with gestational age included aminoacyl-t-RNA biosynthesis, whereas pathways in the AKI group yielded potential metabolite changes in pyruvate metabolism.ConclusionsMetabolomics was able to differentiate the urinary profiles of neonates with and without an AKI diagnosis and metabolic developmental profiles correlated with gestational age. Further studies in larger cohorts are needed to validate these results.

Using Metabolomics to Investigate Biomarkers of Drug Addiction

Drug addiction has been associated with an increased risk for cancer, psychological complications, heart, liver, and lung disease, as well as infection. While genes have been identified that can mark individuals at risk for substance abuse, the initiation step of addiction is attributed to persistent metabolic disruptions occurring following the first instance of narcotic drug use. Advances in analytical technologies can enable the detection of thousands of signals in body fluids and excreta that can be used to define biochemical profiles of addiction. Today, these approaches hold promise for determining how exposure to drugs, in the absence or presence of other environmentally relevant factors, can impact human metabolism. We posit that these can lead to candidate biomarkers of drug dependence, treatment, withdrawal, or relapse.