Identification of bioactive metabolites in human iPSC-derived dopaminergic neurons with PARK2 mutation: altered mitochondrial and energy metabolism.

Justyna Okarmus; Jesper F. Havelund; Matias Ryding; Sissel I. Schmidt; Helle Bogetofte; Nils J. Faergeman; Poul Hyttel; Morten Meyer

doi:10.1101/2020.07.10.151902

bioRxiv · 10.1101/2020.07.10.151902

Identification of bioactive metabolites in human iPSC-derived dopaminergic neurons with PARK2 mutation: altered mitochondrial and energy metabolism.

Justyna Okarmus·Jesper F. Havelund·Matias Ryding·Sissel I. Schmidt·Helle Bogetofte·Nils J. Faergeman·Poul Hyttel·Morten Meyer

View original source ↗Export metadata ↓

Abstract

PARK2 (parkin) mutations cause early onset of autosomal recessively inherited Parkinson’s disease (PD). Parkin is an ubiquitin E3 ligase and has been reported to participate in several cellular functions, including mitochondrial homeostasis. However, the specific metabolomic changes caused by parkin depletion remain largely unknown. Human induced pluripotent stem cells (iPSCs) with PARK2 knockout (KO) provide a valuable model for studying parkin dysfunction in dopaminergic neurons. In the current study, we used isogenic iPSCs to investigate the effect of parkin loss-of-function by comparative metabolomics analysis. The metabolomic profile of the PARK2 KO neurons differed substantially from that of healthy controls. We found increased tricarboxylic acid (TCA) cycle activity, perturbed mitochondrial ultrastructure connected with ATP depletion, glycolysis dysregulation with lactate accumulation, and elevated levels of short- and long-chain carnitines. These mitochondrial and energy perturbations in the PARK2 KO neurons were combined with increased levels of oxidative stress and a decreased anti-oxidative response. In conclusion, our data describe a unique metabolomic profile associated with parkin dysfunction, demonstrating several PD-related cellular defects. Our findings support and expand previously described PD phenotypic features and show that combining metabolomic analysis with an iPSC-derived dopaminergic neuronal model of PD is a valuable approach to obtain novel insight into the disease pathogenesis.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Source connections

Published article ↗

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Justyna Okarmus, Jesper F. Havelund, Matias Ryding, Sissel I. Schmidt, Helle Bogetofte, Nils J. Faergeman, Poul Hyttel, Morten Meyer. 2020-07-10. Identification of bioactive metabolites in human iPSC-derived dopaminergic neurons with PARK2 mutation: altered mitochondrial and energy metabolism.. https://doi.org/10.1101/2020.07.10.151902

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗