Bilirubin: An animal pigment in the Zingiberales and diverse angiosperm orders

Strelitziaceae is a tropical monocot family comprising three genera and seven species: Ravenala Adans and Phenkospermum Endl., which are monotypic, and five species of Strelitzia Aiton. All species produce woody capsular fruits that contain vibrantly colored arillate seeds. Arils of the Strelitzia species are orange, those of Phenakospermum are red, and those of Ravenala are blue. Unlike most plant pigments, which degrade after cell death, aril pigments in the family persist for decades. Chemical properties of the compounds are unusual, and do not match those of known pigment classes (carotenoids, flavonoids, betalains, and the chlorophylls). I isolated the orange pigment from the arils of Strelitzia nicolai, and performed HPLC-ESMS, UV-visible, 1H NMR and 13C NMR analyses to determine its chemical structure. These data indicated the pigment was bilirubin-IX, an orange-yellow tetrapyrrole previously known only in mammals and some other vertebrates as the breakdown product of heme. Although related tetrapyrroles are ubiquitous throughout the plant kingdom and include vital biosynthetic products such as chlorophyll and phytochromobilin, this is the first report of bilirubin in a plant, and evidence of an additional biosynthetic pathway producing orange coloration in flowers and fruits. ^ Given the unexpected presence of bilirubin, Iexamined the fruits and flowers of twelve additional angiosperm species in diverse orders for the presence of bilirubin using HPLC and LC-MS. Bilirubin was present in ten species from the orders Zingiberales, Arecales, and Myrtales, indicating its wide distribution in the plant kingdom. Bilirubin was present in low concentrations in all species except those within Strelitziaceae. It was present in particularly high concentrations in S. nicolai, S. reginae and P. guyannense, and is thus responsible for producing color in these species. ^ No studies have examined the evolutionary relationship among all species in the family. Thus, I also constructed a molecular phylogeny of the family. This information, combined with further studies on the distribution and synthesis of bilirubin in plants, will provide a basis for understanding the evolutionary history of this pigment in the plant kingdom.^

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.

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.