Identification of potentially toxic compounds in complex extracts of environmental samples using gas chromatography-mass spectrometry and multivariate data analysis.

In this study, we examined 31 samples of varying chemical composition, including samples of soils from gasworks, coke production sites, and sites where wood preservatives were heavily used; ash and soot from municipal solid waste incinerators; antiskid sand; and dust from areas with heavy road traffic. The samples were comprehensively chemically characterized, especially their polycyclic aromatic compound contents, using gas chromatography-time-of-flight mass spectrometry, whereas their biological effects were assessed using dehydrogenase activity, root growth (Hordeum vulgare), reproduction of springtails (Folsomia candida), algal growth (Desmodesmus subspicatus), germinability (Sinapis alba), Vibrio fischeri, DR-CALUX, and Ames Salmonella assays. The number of compounds detected in the samples ranged from 123 to 527. Using the multivariate regression technique of partial-least-squares projections to latent structures, it was possible to find individual compounds that exhibited strong correlations with the different biological responses. Some of the results, however, indicate that a broader chemical characterization may be needed to identify all the compounds that may cause the measured biological responses.

Quantitative structure-activity relationship of peptides binding to the class II major histocompatibility complex molecule Aq associated with autoimmune arthritis.

Presentation of (glyco)peptides by the class II major histocompatibility complex molecule Aq to T cells plays a central role in collagen-induced arthritis, an animal model for the autoimmune disease rheumatoid arthritis. A peptide library was designed using statistical molecular design in amino acid space in which five positions in the minimal mouse collagen type II binding epitope CII260-267 were varied. A substantially reduced peptide library of 24 peptides with diverse and representative molecular characteristics was selected, synthesized, and evaluated for the binding strength to Aq. A multivariate QSAR model was established by correlating calculated descriptors, compressed to its principle properties, with the binding data using partial least-square regression. The model was successfully validated by an external test set. Interpretation of the model provided a molecular property binding motif for peptides interacting with Aq. The information may be useful in future research directed toward new treatments of rheumatoid arthritis.

Characterization and classification of complex PAH samples using GC-qMS and GC-TOFMS.

The aim of this study was to compare the polycyclic aromatic hydrocarbon (PAH) contents in a number of complex samples, including soil samples from industrial sites, anti-skid sand, urban dust and ash samples from municipal solid waste incinerators. The samples were characterized by routine analysis of PAHs (gas chromatography-quadrupole mass spectrometry) and gas chromatography-time of flight mass spectrometry (GC-TOFMS). Classification of the samples by principal component analysis (PCA) according to their composition of PAHs revealed that samples associated with traffic and the municipal incinerator formed homogeneous clusters, while the PAH-contaminated soils clustered in separate groups. Using spectral data to resolve co-eluting chromatographic peaks, 962 peaks could be identified in the GC-TOFMS analysis of a pooled sample and 123-527 peaks in the individual samples. Many of the studied extracts included a unique set of chemicals, indicating that they had a much more diverse contamination profile than their PAH contents suggested. Compared to routine analysis, GC-TOFMS provided more detailed information about each sample and in this study a large number of alkylated PAHs were found to be associated with the corresponding unsubstituted PAHs. The possibility to filter peaks according to different criteria (e.g. to include only peaks that were detected in the analysis of another sample) was explored and used to identify unique as well as common compounds within samples. This procedure could prove to be valuable for obtaining relevant chemical data for use in conjunction with results from various biological test systems.