Covalent protein adduction of nitrogen mustards and related compounds

Chemical warfare agents continue to pose a global threat despite the efforts of the international community to prohibit their use in warfare. For this reason, improvement in the detection of these compounds remains of forensic interest. Protein adducts formed by the covalent modification of an electrophilic xenobiotic and a nucleophilic amino acid may provide a biomarker of exposure that is stable and specific to compounds of interest (such as chemical warfare agents), and have the capability to extend the window of detection further than the parent compound or circulating metabolites. This research investigated the formation of protein adducts of the nitrogen mustard chemical warfare agents mechlorethamine (HN-2) and tris(2-chloroethyl)amine (HN-3) to lysine and histidine residues found on the blood proteins hemoglobin and human serum albumin. Identified adducts were assessed for reproducibility and stability both in model peptide and whole protein assays. Specificity of these identified adducts was assessed using in vitro assays to metabolize common therapeutic drugs containing nitrogen mustard moieties. Results of the model peptide assays demonstrated that HN-2 and HN-3 were able to form stable adducts with lysine and histidine residues under physiological conditions. Results for whole protein assays identified three histidine adducts on hemoglobin, and three adducts (two lysine residues and one histidine residue) on human serum albumin that were previously unknown. These protein adducts were determined to be reproducible and stable at physiological conditions over a three-week analysis period. Results from the in vitro metabolic assays revealed that adducts formed by HN-2 and HN-3 are specific to these agents, as metabolized therapeutic drugs (chlorambucil, cyclophosphamide, and melphalan) did not form the same adducts on lysine or histidine residues as the previously identified adducts formed by HN-2 and HN-3. Results obtained from the model peptide and full protein work were enhanced by comparing experimental data to theoretical calculations for adduct formation, providing further confirmatory data. This project was successful in identifying and characterizing biomarkers of exposure to HN-2 and HN-3 that are specific and stable and which have the potential to be used for the forensic determination of exposure to these dangerous agents.

Identification of characteristic volatile organic compounds released during the decomposition process of human remains and analogues

The manner in which remains decompose has been and is currently being researched around the world, yet little is still known about the generated scent of death. In fact, it was not until the Casey Anthony trial that research on the odor released from decomposing remains, and the compounds that it is comprised of, was brought to light. The Anthony trial marked the first admission of human decomposition odor as forensic evidence into the court of law; however, it was not "ready for prime time" as the scientific research on the scent of death is still in its infancy. This research employed the use of solid-phase microextraction (SPME) with gas chromatography-mass spectrometry (GC-MS) to identify the volatile organic compounds (VOCs) released from decomposing remains and to assess the impact that different environmental conditions had on the scent of death. Using human cadaver analogues, it was discovered that the environment in which the remains were exposed to dramatically affected the odors released by either modifying the compounds that it was comprised of or by enhancing/hindering the amount that was liberated. In addition, the VOCs released during the different stages of the decomposition process for both human remains and analogues were evaluated. Statistical analysis showed correlations between the stage of decay and the VOCs generated, such that each phase of decomposition was distinguishable based upon the type and abundance of compounds that comprised the odor. This study has provided new insight into the scent of death and the factors that can dramatically affect it, specifically, frozen, aquatic, and soil environments. Moreover, the results revealed that different stages of decomposition were distinguishable based upon the type and total mass of each compound present. Thus, based upon these findings, it is suggested that the training aids that are employed for human remains detection (HRD) canines should 1) be characteristic of remains that have undergone decomposition in different environmental settings, and 2) represent each stage of decay, to ensure that the HRD canines have been trained to the various odors that they are likely to encounter in an operational situation.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Sources, Fate and Transformation of Organic Matter in Wetlands and Estuaries

Dissolved organic matter (DOM) is a complex mixture of organic compounds and represents the largest reservoirs of carbon (C) on earth. Particulate organic matter (POM) is another important carbon component in C cycling and controls a variety of biogeochemical processes. Estuaries, as important interfaces between land and ocean, play important roles in retaining and transforming such organic matter (OM) and serve as both sources and sinks of DOM and POM. There is a diverse array of both autochthonous and allochthonous OM sources in wetland/estuarine ecosystems. A comprehensive study on the sources, transformation and fate of OM in such ecosystems is essential in advancing our understanding of C cycling and better constraining the global C budget. In this work, DOM characteristics were investigated in different estuaries. Dissolved organic matter source strengths and dynamics were assessed in a seagrass-dominated subtropical estuarine lagoon. DOM dynamics controlled by hydrology and seagrass primary productivity were confirmed, and the primary source of DOM was quantified using the combination of excitation emission matrix fluorescence with parallel factor analysis (EEM-PARAFAC) and stable C isotope analysis. Seagrass can contribute up to 72% of the DOM in the study area. The spatial and temporal variation of DOM dynamics was also studied in a freshwated dominated estuary fringed with extensive salt marshes. The data showed that DOM was primarily derived from freshwater marshes and controlled by hydrology while salt marsh plants play a significant role in structuring the distribution patterns of DOM quality and quantity. The OM dynamics was also investigated in a mangrove-dominate estuary and a comparative study was conducted between the DOM and POM pools. The results revealed both similarity and dissimilarity in DOM and POM composition. The dynamics of both OM pools are largely uncoupled as a result of source differences. Fringe mangrove swamps are suggested to export similar amounts of DOM and POM and should be considered as an important source in coastal C budgets. Lastly, chemical characterizations were conducted on the featured fluorescence component in OM in an attempt to better understand the composition and origins of the specific PARAFAC component. The traditionally defined ‘protein-like’ fluorescence was found to contain both proteinaceous and phenolic compounds, suggesting that the application of this parameter as a proxy for amino acid content and bioavailability may be limited.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Quorum Sensing and Microbial Interactions in Coral Black Band Disease and Coral-Associated Bacteria

The black band disease (BBD) microbial consortium often causes mortality of reef-building corals. Microbial chemical interactions (i.e., quorum sensing (QS) and antimicrobial production) may be involved in the BBD disease process. Culture filtrates (CFs) from over 150 bacterial isolates from BBD and the surface mucopolysaccharide layer (SML) of healthy and diseased corals were screened for acyl homoserine lactone (AHL) and Autoinducer-2 (AI-2) QS signals using bacterial reporter strains. AHLs were detected in all BBD mat samples and nine CFs. More than half of the CFs (~55%) tested positive for AI-2. Approximately 27% of growth challenges conducted among 19 isolates showed significant growth inhibition. These findings demonstrate that QS is actively occurring within the BBD microbial mat and that culturable bacteria from BBD and the coral SML are able to produce QS signals and antimicrobial compounds. This is the first study to identify AHL production in association with active coral disease.