From iPSCs to NPCs to cortical neurons: bioenergetic, neuronal, and calcium signaling phenotypes in bipolar disorder with and without familial mitochondrial disease
Bibliographic record
Abstract
Abstract Background Psychiatric disorders frequently accompany primary mitochondrial diseases (PMDs), implicating mitochondrial dysfunction as a shared biological substrate for psychiatric vulnerability. To determine how familial mitochondrial risk influences neuronal development in bipolar disorder (BD), we generated induced pluripotent stem cells (iPSCs), neural progenitor cells (NPCs), and cortical neurons (CNs) from three healthy controls (CT), three patients with BD, and three patients with BD and a family history of mitochondrial disease (BD-FMD). Methods Across differentiation, we assessed mitochondrial function (ATP production, mitochondrial membrane potential, ROS generation, cytosolic cell-free mtDNA), metabolomic signatures, calcium imaging, and neuronal electrophysiological activity through multi-electrode arrays (MEA). Results BD neurons uniquely exhibited pronounced hyperexcitability in comparison to CT and BD-FMD groups. The BD-FMD group displayed prolonged mitochondrial calcium transients, altered membrane potential, and aberrant ROS, in comparison to CT and BD, consistent with a sustained energetic deficit. Metabolomic profiling revealed distinct pathway enrichments in BD and BD-FMD, indicating divergent bioenergetic adaptations to mitochondrial burden. Conclusions Our findings reveal that familial mitochondrial liability actively reshapes neuronal differentiation and function, producing distinct trajectories of mitochondrial performance, calcium signaling, and network excitability. These results suggest that mitochondrial dysfunction in BD is not a secondary byproduct of illness, but a mechanistic contributor to altered neuronal energetics and communication. Together, these findings delineate how inherited mitochondrial vulnerability reshapes neuronal excitability and metabolism, revealing bioenergetic phenotypes that may inform precision stratification and therapeutic targeting in BD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".