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The complex immune system displays a coordinated transcriptional response to xenobiotic exposure by altering expression of designated transcription factors that, in turn, trigger immune responses. Despite the identification of several transcription factors that contribute to regulatory response, very little is known about the specific role of factors that are triggered due to exposure to obnoxious pesticides. Here, for the first time, alterations in human peripheral blood lymphocyte expression of transcriptional factors - thrombospondin-1 (THBS-1), secretory phospho-protein-1 (SPP-1), glycoprotein non-metastatic-β (GPNMB) and fasciculation and elongation factor ζ-1 (FEZ-1), due to in vitro exposure to the crop protection chemicals cypermethrin and mancozeb are reported. Results revealed significant changes in expression profiles due to mancozeb exposure, supporting its immune dysfunction potential; in contrast, cypermethrin exposure did not cause significant changes. Based on these effects on gene expression across the doses tested, it was likely key components of immune mechanisms such as proliferation, cell adhesion, apoptosis and cell activation in human PBMC were affected. Although these data are from in vitro experiments, the results point out the potential role for changes in these factors in the etiology of defective T-cell immune function seen in humans occupationally exposed to crop protection chemicals like mancozeb. These studies suggest the involvement of transcription factors in regulation of pesticide-induced immune dysfunction; these studies also represent a novel approach for identifying potential immune-related dysfunctions due to exposure to pesticides. Further studies are needed to better understand the functional significance of these in vitro findings.

Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications.