Cadmium and lead in vegetable and fruit produce selected from specific regional areas of the UK

Cadmium and lead were determined in fruit and vegetable produce (~1300 samples) collected from a field and market basket study of locally grown produce from the South-West of Britain (Devon and Cornwall). These were compared with similarly locally grown produce from the North-East of Britain (Aberdeenshire). The concentrations of cadmium and lead in the market basket produce were compared to the maximum levels (ML) set by the European Union (EU). For cadmium 0.2% of the samples exceeded the ML, and 0.6% of the samples exceeded the ML for lead. The location of cadmium and lead in potatoes was performed using laser ablation ICP-MS. All tested samples exhibited higher lead concentrations, and most exhibited increased concentrations of cadmium in the potato skin compared to the flesh. The concentrations of cadmium and lead found in fruits and vegetables sampled during this study do not increase concern about risk to human health.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.