Back

Dysregulation of energy metabolism and calcium homeostasis in iPSC-derived neurons carrying Presenilin-1 M146L gene mutation

Wilson, C.; Galeano, P.; Remedi, M. M.; Novack, G. V.; Campanelli, L.; Gastaldi, L.; Miglietta, E. A.; Rossi, A. H.; Olivar, N.; Brusco, L. I.; Castano, E. M.; Caceres, A.; Morelli, L.

2025-09-14 neuroscience
10.1101/2025.09.08.674945 bioRxiv
Show abstract

Impaired cellular activities, particularly in highly active cells such as neurons, are primarily supported by metabolic abnormalities and failures in Ca{superscript 2} homeostasis. Here, we provide an integrative analysis of human iPSC-derived neurons (iNs) carrying the Presenilin-1 M146L gene mutation (PS1M146L) and control cells (PS1control). PS1M146L iNs exhibited abnormal Ca{superscript 2} dynamics, a significant increase in key parameters of mitochondrial respiration, and higher intracellular ROS levels. KCl-evoked depolarisation was significantly lower in PS1M146L, suggesting a failure in maintaining the electrochemical gradient across the plasma membrane. Following thapsigargin stimulation, mitochondrial Ca{superscript 2} levels ([Ca{superscript 2}]m) were significantly reduced in PS1M146L, while [Ca{superscript 2}]m did not differ significantly between genotypes after treatment with bradykinin, suggesting that impairments in the [Ca{superscript 2}]m homeostasis are particularly evident under stress conditions and do not impact the 1,4,5-triphosphate (IP3) pathway. Since iNs of both genotypes were sensitive to the MCU-1 inhibitor, the deficits observed in PS1M146L could be the consequence of impairments in the ER-mitochondria contacts. Our results illustrate the utility of iNs carrying PS1 mutations in understanding how human neurons alter relevant pathways before neurodegeneration.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.