Search bioRxiv⌕ Search

Biology subjects

Shahar, M.

Publications and source records attributed to Shahar, M..

2 recordsLinked to original sources

A Chaperonin Complex Regulates Organelle Proteostasis in Malaria Parasites

The apicoplast of Plasmodium parasites serves as a metabolic hub that synthesize essential biomolecules. Like other endosymbiotic organelles, 90% of the apicoplast proteome is encoded by the cell nucleus and transported to the organelle. Evidence suggests that the apicoplast has minimal control over the synthesis of its proteome and therefore it is unclear how organelle proteostasis is regulated. Here, we identified and investigated a large and conserved chaperonin (CPN) complex with a previously unknown function. Using genetic tools, we demonstrated that ablation of the apicoplast CPN60 subunit leads to parasite death due to organellar damage, immediately within its first replication cycle, deviating from the delayed death phenotype commonly observed for apicoplast translation inhibitors. Unlike its close orthologues in other prokaryotic and eukaryotic cells, CPN60 is not upregulated during heat shock (HS) and does not affect HS response in the parasite. Instead, we found that it is directly involved in proteostasis through interaction with the Clp (caseinolytic protease) proteolytic complex. We showed that CPN60 physically binds both the active and inactive forms of the Clp complex, and manipulates its stability. A computational structural model of a possible interaction between these two large complexes suggests a stable interface. Finally, we screened a panel of inhibitors for the bacterial CPN60 orthologue GroEL, to test the potential of chaperonin inhibition as antimalarial. These inhibitors demonstrated an anti-Plasmodium activity that was not restricted to apicoplast function, with additional targets outsides of this organelle. Taken together, this work reveals how balanced activities of proteolysis and refolding safeguard the apicoplast proteome, and is essential for organelle biogenesis. Author SummaryThe cell of the human malaria parasite Plasmodium falciparum has a unique organelle called the apicoplast that produces essential metabolites, but it is unclear how it maintains a stable proteome. Here, we address the question of organelle proteostasis by investigating the function of a large chaperonin complex and its main subunit CPN60. We show that CPN60 mutants die due to organellar damage immediately within the first replication cycle, avoiding the typical apicoplast-delayed cell death. We demonstrate that it binds and stabilizes another large proteolytic complex and use computational predicting tools to demonstrate how a stable interface is attained. We use bacterial inhibitors to explore their potential as an antimalarial drug target. This study reveals how balanced refolding and proteolysis safeguard the apicoplast proteome and opens a new avenue for antimalarial drug discovery.

microbiology↗

Predicting individual skill learning, a cautionary tale

People show vast variability in skill learning. What determines a persons individual learning ability? In this study we explored the possibility to predict participants future learning, based on their behavior during initial skill acquisition. We recruited a large online multi-session sample of participants performing a sequential tapping skill learning task. We trained machine learning models to predict future skill learning from raw data acquired during initial skill acquisition, and from engineered features calculated from the raw data. While the models did not explain learning, strong correlations were observed between initial and final performance. In addition, the results suggest that in correspondence with other empirical fields testing human behavior, canonical experimental tasks developed and selected to detect average effects may constrain insights regarding individual variability, relevant for real-life scenarios. Overall, implementing machine learning tools on large-scale data sets may provide a powerful approach towards revealing what differentiates between high and low innate learning abilities, paving the way for learning optimization techniques which may generalize beyond motor skill learning to broad learning abilities.

neuroscience↗