bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.02.18.950337

INFLUENCE OF ENERGY DEFICIENCY ON THE MOLECULAR PROCESSES OF SUBSTANTIA NIGRA PARS COMPACTA CELL FOR UNDERSTANDING PARKINSONIAN NEURODEGENERATION: A COMPREHENSIVE BIOPHYSICAL COMPUTATIONAL MODEL

Muddapu, V. R.·Chakravarthy, V. S.
View original source ↗Export metadata ↓

Abstract

Parkinsons disease (PD) is the second most prominent neurodegenerative disease around the world. Although it is known that PD is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that "Energy deficiency at a sub-cellular/cellular/systems level can be a common underlying cause for SNc cell loss in PD." Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular level in PD. The proposed model incorporates a rich vein of molecular dynamics related to SNc neurons such as ion channels, active pumps, ion exchangers, dopamine turnover processes, energy metabolism pathways, calcium buffering mechanisms, alpha-synuclein aggregation, Lewy body formation, reactive oxygen species (ROS) production, levodopa uptake, and apoptotic pathways. The proposed model was developed and calibrated based on experimental data. The influx of glucose and oxygen into the model was controlled, and the consequential ATP variations were observed. Apart from this, the dynamics of other molecular players (alpha-synuclein, ROS, calcium, and dopamine) known to play an important role in PD pathogenesis are also studied. The aim of the study was to see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD.

Source connections

Explore related subjects

neurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Muddapu, V. R., Chakravarthy, V. S.. 2020-02-19. INFLUENCE OF ENERGY DEFICIENCY ON THE MOLECULAR PROCESSES OF SUBSTANTIA NIGRA PARS COMPACTA CELL FOR UNDERSTANDING PARKINSONIAN NEURODEGENERATION: A COMPREHENSIVE BIOPHYSICAL COMPUTATIONAL MODEL. https://doi.org/10.1101/2020.02.18.950337

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↗