Low molecular weight chemical (LMW) allergens are commonly referred to as haptens. Haptens must complex with proteins to be recognized by the immune system. The majority of occupationally related haptens are reactive, electrophilic chemicals, or are metabolized to reactive metabolites that form covalent bonds with nucleophilic centers on proteins. Nonelectrophilic protein binding may occur through disulfide exchange, coordinate covalent binding onto metal ions on metalloproteins or of metal allergens, themselves, to the major histocompatibility complex. Recent chemical reactivity kinetic studies suggest that the rate of protein binding is a major determinant of allergenic potency; however, electrophilic strength does not seem to predict the ability of a hapten to skew the response between Th1 and Th2. Modern proteomic mass spectrometry methods that allow detailed delineation of potential differences in protein binding sites may be valuable in predicting if a chemical will stimulate an immediate or delayed hypersensitivity. Chemical aspects related to both reactivity and protein-specific binding are discussed.

Allergic contact dermatitis is the second most commonly reported occupational illness, accounting for 10% to 15% of all occupational diseases. This highlights the importance of developing rapid and sensitive methods for hazard identification of chemical sensitizers. The murine local lymph node assay (LLNA) was developed and validated for the identification of low molecular weight sensitizing chemicals. It provides several benefits over other tests for sensitization because it provides a quantitative endpoint, dose-responsive data, and allows for prediction of potency. However, there are also several concerns with this assay including: levels of false positive responses, variability due to vehicle, and predictivity. This report serves as a concise review which briefly summarizes the progress, advances and limitations of the assay over the last decade.

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New methodologies for surveillance and identification of Mycobacterium tuberculosis are required to stem the spread of disease worldwide. In addition, the ability to discriminate mycobacteria at the strain level may be important to contact or source case investigations. To this end, we are developing MALDI-TOF MS methods for the identification of M. tuberculosis in culture. In this report, we describe the application of MALDI-TOF MS, as well as statistical analysis including linear discriminant and random forest analysis, to 16 medically relevant strains from four species of mycobacteria, M. tuberculosis, M. avium, M. intracellulare, and M. kansasii. Although species discrimination can be accomplished on the basis of unique m/z values observed in the MS fingerprint spectrum, discrimination at the strain level is predicted on the relative abundance of shared m/z values among strains within a species. For the 16 mycobacterial strains investigated in the present study, it is possible to unambiguously identify strains within a species on the basis of MALDI-TOF MS data. The error rate for classification of individual strains using linear discriminant analysis was 0.053 using 37 m/z variables, whereas the error rate for classification of individual strains using random forest analysis was 0.023 using only 18 m/z variables. In addition, using random forest analysis of MALDI-TOF MS data, it was possible to correctly classify bacterial strains as either M. tuberculosis or non-tuberculous with 100% accuracy.