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Synthesis of earthworm trace metal uptake and bioaccumulation data: role of soil concentration, earthworm ecophysiology, and experimental design
Trace metals can be essential for organo-metallic structures and oxidation-reduction in metabolic processes or may cause acute or chronic toxicity at elevated concentrations. The uptake of trace metals by earthworms can cause transfer from immobilized pools in the soil to predators within terrestrial food chains. We report a synthesis and evaluation of uptake and bioaccumulation empirical data across different metals, earthworm genera, ecophysiological groups, soil properties, and experimental conditions (metal source, uptake duration, soil extraction method). Peer-reviewed datasets were extracted from manuscripts published before June 2019. The 56 studies contained 3513 soil-earthworm trace metal concentration paired data sets across 11 trace metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Sb, U, Zn). Across all field and laboratory experiments studied, the median concentrations of Hg, Pb, and Cd in earthworm tissues that were above concentrations known to be hazardous for consumption by small mammals and avian predators but not for Cu, Zn, Cr, Ni, and As. Power regressions show only Hg and Cd earthworm tissue concentrations were well-correlated with soil concentrations with R2 > 0.25. However, generalized linear mixed-effect models reveal that earthworm concentrations were significantly correlated with soil concentrations for log-transformed Hg, Cd, Cu, Zn, As, Sb (p < 0.05). Factors that significantly contributed to these relationships included earthworm genera, ecophysiological group, soil pH, and organic matter content. Moreover, spiking soils with metal salts, shortening the duration of exposure, and measuring exchangeable soil concentrations resulted in significantly higher trace metal uptake or greater bioaccumulation factors. Our results highlight that earthworms are able to consistently bioaccumulate toxic metals (Hg and Cd only) across field and laboratory conditions. However, future experiments should incorporate greater suites of trace metals, broader genera of earthworms, and more diverse laboratory and field settings to generate data to devise universal quantitative relationships between soil and earthworm tissue concentrations
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe