A simple and efficient seed-based approach to induce callus production from B73 maize genotype

The wild type maize genotype, B73, is not amenable for callus production and an efficient protocol for B73 maize callus induction has never been reported up-to-date. Scientific efforts in producing B73 maize callus using all known callus inducible media have been unsatisfactory. Here we developed and described an efficient protocol for callus induction from B73 maize seedlings. The protocol is based on well known callus inducible media CM4C where we have sequentially subtracted some chemical compounds and added some new compounds mediating cell proliferations. This newly described protocol was able to induce callus production in a wide range of crop species including rice and soybean. We found that cell proliferation factors, NAA (auxin analog) and 2,4 D (auxin influx carrier) were not only very crucial but required for positive B73 maize callus induction. The absence of one or the other will lead to the failure of B73 maize callus production. The well known CM4C callus induction composition lacks NAA. Our findings will advance genetic studies of maize mutants generated from B73 genotype background.

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms

Computational modeling has emerged as a time-saving and cost-effective alternative to traditional animal testing for assessing chemicals for their potential hazards.However,few computational modeling studies for immunotoxicity were reported,with few models available for predicting toxicants due to the lack of training data and the complex mechanisms of immunotoxicity.In this study,we employed a data-driven quantitative structure–activity relationship(QSAR)modeling workflow to extensively enlarge the limited training data by revealing multiple targets involved in immunotoxicity.To this end,a probe data set of 6,341 chemicals was obtained from a high-throughput screening(HTS)assay testing for the activation of the aryl hydrocarbon receptor(AhR)signaling pathway,a key event leading to immunotoxicity.Searching this probe data set against PubChem yielded 3,183 assays with testing results for varying proportions of these 6,341 compounds.100 assays were selected to develop QSAR models based on their correlations to AhR agonism.Twelve individual QSAR models were built for each assay using combinations of four machine-learning algorithms and three molecular fingerprints.5-fold cross-validation of the resulting models showed good predictivity(average CCR=0.73).A total of 20 assays were further selected based on QSAR model performance,and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals.This study provides a computational modeling strategy that can utilize large public toxicity data sets for modeling immunotoxicity and other toxicity endpoints,which have limited training data and complicated toxicity mechanisms.