Metabolomics and in-silico analysis reveal critical energy deregulations in animal models of Parkinson's disease

Abstract

Parkinson's disease (PD) is a multifactorial disease known to result from a variety of factors. Although age is the principal risk factor, other etiological mechanisms have been identified, including gene mutations and exposure to toxins. Deregulation of energy metabolism, mostly through the loss of complex I efficiency, is involved in disease progression in both the genetic and sporadic forms of the disease. In this study, we investigated energy deregulation in the cerebral tissue of animal models (genetic and toxin induced) of PD using an approach that combines metabolomics and mathematical modelling. In a first step, quantitative measurements of energy-related metabolites in mouse brain slices revealed most affected pathways. A genetic model of PD, the Park2 knockout, was compared to the effect of CCCP, a mitochondrial uncoupler [corrected]. Model simulated and experimental results revealed a significant and sustained decrease in ATP after CCCP exposure, but not in the genetic mice model. In support to data analysis, a mathematical model of the relevant metabolic pathways was developed and calibrated onto experimental data. In this work, we show that a short-term stress response in nucleotide scavenging is most probably induced by the toxin exposure. In turn, the robustness of energy-related pathways in the model explains how genetic perturbations, at least in young animals, are not sufficient to induce significant changes at the metabolite level.

Figure 1. Energy metabolism model for the cerebral tissue.

The states of the model (in capital letters) are defined as follows: GLC, glucose; G6P, glucose-6-phosphate; F6P, fructose-6-phosphate; FBP, fructose-biphosphate; G3P, glyceraldehyde-3-phosphate; PEP, phosphoenolpyruvate; PYR, pyruvate; GLY, glycogen; R5P, ribose-5-phosphate; Cr (PCr), creatine (phosphocreatine); LAC, lactate; ACA, acetyl-coenzyme-A; CIT, citrate; AKG, α-ketoglutarate; SUC, succinate; FUM, fumarate; MAL, malate; OAA, oxaloacetate; GLT, glutamate; GLN, glutamine; NAD (NADH), nicotniamide adenine dinucleotide (reduced); NADP (NADPH), phosphorylated nicotinamide adenine dinucleotide (reduced); ATP, adenosine-triphosphate; ADP, adenosine-diphosphate; AMP, adenosine-monophosphate; O₂, oxygen; ANPs, non-free adenosine-“n”phosphate nucleotides; V_e, extracellular volume; subscript “e” refers to extracellular. Reactions (in italic) are defined in Supplementary Materials. The intracellular volume delimited by the dotted line refers to the mitochondrial volume.

Figure 2. Effect of toxin exposure on energy dynamics.

Experimental data of WT control (♦) and CCCP stressed (▪) brain cells, and model simulations of control (black line) and CCCP stressed (red line) cells. LACe (A), GLCe (B), LACe to LAC transport (C), GLCe to GLC transport (D), LAC production (V_ldh) (E), GLC consumption (V_hk) (F), ATP (G), ATP-to-(ATP+ADP+AMP) (♦,▪, black line, dotted red line) and ATP-to-(ATP+ADP+AMP+ANPs) (thick red line) ratios (H).

Figure 3. Basal indicator for WT and CCCP stressed models.

Model simulation of control (black line) and CCCP stressed (red line) brain cells oxygen-to-glucose-consumption ratio.

Figure 4. Effect of parkin gene knockout on brain cells energy dynamics.

Experimental data of WT control (♦) and parkin knockout (▪) brain cells, and model simulations of control (black line) and parkin knockout (bleue line) cells. LACe (A), GLCe (B), LACe to LAC transport (C), GLCe to GLC transport (D), LAC production (V_ldh) (E), GLC consumption (V_hk) (F), ATP (G), ATP-to-(ATP+ADP+AMP) ratio (H).

Figure 5. Comparison of fluxes and metabolic ratios.

Model simulations of WT control (black line), CCCP stressed (red line) and parkin knockout (bleue line) brain cells. Characteristic pentose phosphate pathway flux rate (V_ppp) (A), Glucose consumption rate (V_hk) –to- characteristic pentose phosphate pathway flux rate (V_ppp) (B).