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High affinity autoreactive IgG antibodies have been implicated in the development of lupus nephritis and other autoimmune disorders. With the discovery of activation-induced deaminase (AID), this question could be finally tested by examining the impact of AID deficiency in autoimmune-prone mice like the MLR/lpr strain. We have recently shown that AID-deficient MRL/lpr mice experienced a complete abrogation of lupus nephritis, and increased survival despite a dramatic increase in autoreactive IgM. Subsequent studies demonstrated that anti-dsDNA IgM is not pathogenic and in fact protects MRL/lpr from glomerulonephritis. AID-deficiency is also associated with decreased antibody-independent B cell-mediated autoimmunity likely through the loss of high affinity receptors through somatic hypermutation. Combined these results directly implicate AID in the development of B cell mediated autoimmunity. However, studies with hyper IgM AID-deficient patients indicate an increase in the incidence of certain autoimmunities. These results, likely the result of the immunodeficiency associated with AID deficiency, suggest caution in therapeutic approaches based in AID inhibition.

This study closely examines six well-known naturally occurring dietary chemicals (estragole, pulegone, aristolochic acid I, lipoic acid, 1-octacosanol, and epicatechin) with known human exposure, chemical metabolism, and mechanism of action (MOA) using in silico screening methods. The goal of this study was to take into consideration the available information on these chemicals in terms of MOA and experimentally determined toxicological data, and compare them to the in silico predictive modeling results produced from a series of computational toxicology software. After these analyses, a consensus modeling prediction was formulated in light of the weight of evidence for each natural product. We believe this approach of examining the experimentally determined mechanistic data for a given chemical and comparing it to in silico generated predictions and data mining is a valid means to evaluating the utility of the computational software, either alone or in combination with each other. We find that consensus predictions appear to be more accurate than the use of only one or two software programs and our in silico results are in very good agreement with the experimental toxicity data for the natural products screened in this study.