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Environmentally inducible phenotypic plasticity is a major player in plant responses to climate change. However, metabolic responses and their role in determining the phenotypic plasticity of plants that are subjected to temperature variations remain poorly understood. The metabolomic profiles and metabolite levels in the leaves of three maize inbred lines grown in different temperature conditions were examined with a nuclear magnetic resonance metabolomic technique. The relationship of functional traits to metabolome profiles and the metabolic mechanism underlying temperature variations were then explored. A comparative analysis showed that during heat and cold stress, maize plants shared common plastic responses in biomass accumulation, carbon, nitrogen, sugars, some amino acids and compatible solutes. We also found that the plastic response of maize plants to heat stress was different from that under cold stress, mainly involving biomass allocation, shikimate and its aromatic amino acid derivatives, and other non-polar metabolites. The plastic responsiveness of functional traits of maize lines to temperature variations was low, while the metabolic responsiveness in plasticity was high, indicating that functional and metabolic plasticity may play different roles in maize plant adaptation to temperature variations. A linear regression analysis revealed that the maize lines could adapt to growth temperature variations through the interrelation of plastic responses in the metabolomes and functional traits, such as biomass allocation and the status of carbon and nitrogen. We provide valuable insight into the plastic response strategy of maize plants to temperature variations that will permit the optimisation of crop cultivation in an increasingly variable environment.

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004–17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.

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