Gene expression in mycorrhizal orchid protocorms suggests a friendly plant-fungus relationship.

Orchids fully depend on symbiotic interactions with specific soil fungi for seed germination and early development. Germinated seeds give rise to a protocorm, a heterotrophic organ that acquires nutrients, including organic carbon, from the mycorrhizal partner. It has long been debated if this interaction is mutualistic or antagonistic. To investigate the molecular bases of the orchid response to mycorrhizal invasion, we developed a symbiotic in vitro system between Serapias vomeracea, a Mediterranean green meadow orchid, and the rhizoctonia-like fungus Tulasnella calospora. 454 pyrosequencing was used to generate an inventory of plant and fungal genes expressed in mycorrhizal protocorms, and plant genes could be reliably identified with a customized bioinformatic pipeline. A small panel of plant genes was selected and expression was assessed by real-time quantitative PCR in mycorrhizal and non-mycorrhizal protocorm tissues. Among these genes were some markers of mutualistic (e.g. nodulins) as well as antagonistic (e.g. pathogenesis-related and wound/stress-induced) genes. None of the pathogenesis or wound/stress-related genes were significantly up-regulated in mycorrhizal tissues, suggesting that fungal colonization does not trigger strong plant defence responses. In addition, the highest expression fold change in mycorrhizal tissues was found for a nodulin-like gene similar to the plastocyanin domain-containing ENOD55. Another nodulin-like gene significantly more expressed in the symbiotic tissues of mycorrhizal protocorms was similar to a sugar transporter of the SWEET family. Two genes coding for mannose-binding lectins were significantly up-regulated in the presence of the mycorrhizal fungus, but their role in the symbiosis is unclear.

Machine Learning in Early Prediction of Metabolism of Drugs.

Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bottleneck in the early identification of toxic metabolites or biotransformation pathways that can affect elimination of the drug and potentially hinder the development of future new drugs. Metabolism prediction can be addressed with the application of ML models trained on large and validated dataset, from early stages of lead optimization to latest stage of drug development. ML methods rely on molecular descriptors that allow to identify and learn chemical and molecular features to predict sites of metabolism (SoMs) or activity associated with mechanism of inhibition (e.g., CYP inhibition). The application of ML methods in the prediction of drug metabolism represents a powerful resource to be exploited during drug discovery and development. ML allows to improve in silico screening and safety assessments of drugs in advance, steering their path to marketing authorization. Prediction of biotransformation reactions and metabolites allows to shorten the time, save the cost, and reduce animal testing. In this context, ML methods represent a technique to fill data gaps and an opportunity to reduce animal testing, calling for the 3R principles within the Big Data era.