Application of transcriptome profiling to inquire into the mechanism of nanoplastics toxicity during Ciona robusta embryogenesis.

The growing concern on nanoplastics (<1 µm) impact on marine life has stimulated a significant amount of studies aiming to address ecotoxicity and disclose their mechanisms of action. Here, we applied an integrative approach to develop an Adverse Outcome Pathway (AOP) upon acute exposure to amino-modified polystyrene nanoparticles (PS-NH2 NPs, 50 nm), as proxy for nanoplastics, during the embryogenesis of the chordate Ciona robusta. Genes related to glutathione metabolism, immune defense, nervous system, transport by aquaporins and energy metabolism were affected by either concentration tested of 10 or 15 µg mL-1 of PS-NH2. Transcriptomic data and in vivo experiments were assembled into two putative AOPs, identifying as key events the adhesion of PS-NH2 as (molecular) initiating event, followed by oxidative stress, changes in transcription of specific genes, morphological defects, increase in reactive oxygen species level, impaired swimming behavior. As final adverse outcomes, altered larval development, reduced metamorphosis and inhibition of hatching were identified. Our study attempts to define AOPs for PS-NH2 without excluding that chemicals leaching from them might also have a potential role in the observed outcome. Overall data provide new insights into the mechanism of action of PS-NH2 NPs during chordate embryogenesis and offer further keys for a better knowledge of nanoplastics impact on early stages of marine life.

Prediction of amyloid aggregation in vivo.

Many human diseases owe their pathology, to some degree, to the erroneous conversion of proteins from their soluble state into fibrillar, ß-structured aggregates, often referred to as amyloid fibrils. Neurodegenerative diseases, such as Alzheimer and spongiform encephalopathies, as well as type 2 diabetes and both localized and systemic amyloidosis, are among the conditions that are associated with the formation of amyloid fibrils. Several mathematical tools can rationalize and even predict important parameters of amyloid fibril formation. It is not clear, however, whether such algorithms have predictive powers for in vivo systems, in which protein aggregation is affected by the presence of other biological factors. In this review, we briefly describe the existing algorithms and use them to predict the effects of mutations on the aggregation of specific proteins, for which in vivo experimental data are available. The comparison between the theoretical predictions and the experimental data obtained in vivo is shown for each algorithm and experimental data set, and statistically significant correlations are found in most cases.