Proliferation and T

In recent times, human cell-based assays are gaining attention in assessments of immunomodulatory effects of chemicals. In the study here, the possible effects of cypermethrin and mancozeb on lymphocyte proliferation and proinflammatory (tumor necrosis factor (TNF-) α) and immunoregulatory cytokine (interferon- (IFN-) γ, interleukins (IL) 2, 4, 6, and 10) formation in vitro were investigated. Human peripheral blood mononuclear cells (PBMC) were isolated and exposed for 6 hr to noncytotoxic doses (0.45–30 µM) of cypermethrin or mancozeb in the presence of activating rat S9 fraction. Cultures were then further incubated for 48 or 72 hr in fresh medium containing phytohemagglutinin (10 µg/mL) to assess, respectively, effects on cell proliferation (BrdU-ELISA method) and cytokine formation (flow cytometric bead immunoassays). Mancozeb induced dose-dependent increases in lymphocyte proliferation, inhibition of production of TNFα and the TH2 cytokines IL-6 and IL-10, and an increase in IFNγ (TH1 cytokine) production (at least 2-fold compared to control); mancozeb also induced inhibition of IL-4 (TH2) and stimulated IL-2 (TH1) production, albeit only in dose-related manners for each. In contrast, cypermethrin exposure did not cause significant effects on proliferation or cytokine profiles. Further studies are needed to better understand the functional significance of our in vitro findings

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

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

Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

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