A computational model identifies metabolites associated with Parkinson's disease

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In a recent study posted to the preprint server Research Square* while under review for publication in NPJ Parkinson’s Disease, researchers identify significant metabolites involved in the development and progression of Parkinson’s disease (PD).

Study: Identification of metabolites reproducibly associated with Parkinson’s Disease via meta-analysis and computational modelling. Image Credit: CGN089 / Shutterstock.com

*Important notice: Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Background

As the second most common neurodegenerative disease, PD currently affects about four million people globally, with cases continuing to rise among the growing elderly population. The multifactorial nature of PD increases the complexity of diagnosing this disease, as many neuroimaging, genomics, and biomolecules have been proposed as potential biomarkers.

Enzymatic metabolic reactions lead to the generation of small endogenous molecules otherwise known as metabolites, many of which actively participate in cellular functions ranging from energy metabolism and signal transduction to cell death. Despite the discovery of various metabolic biomarkers for PD, many of these metabolites are highly heterogeneous and segregated, with little data elucidating the functional role of these metabolites in the development and progression of PD.

About the study

To overcome these challenges, the researchers integrated PD metabolites with genome-scale metabolomic modelling.

To this end, the Web of Science and PubMed databases were extensively searched to review metabolomic PD studies published until March 2021. The reliability of these studies was determined using Newcastle-Ottawa Scale (NOS) and QUADOMICS quality assessment tools.

The Virtual Metabolic Human Identifier (VMHID) was used to unify and classify all identified metabolites into a single namespace, with any unidentified metabolites and fragments excluded from the study.

Recon3D is a global genome-scale metabolic model consisting of 4,140 unique metabolites and 13,543 metabolic reactions. This model is frequently used to elucidate gene-protein-reaction associations and their role in various metabolic and enzymatic reactions.

In the current study, Recon3D was used to generate the PD model. Due to the lack of extensive input data, the ‘thermoKernel’ algorithm of the ‘XomicsToModel’ pipeline was used to extract a thermodynamically flux-consistent global subset was generated.

Notable metabolites involved in PD

Many of the reviewed studies identified lipid metabolites to be involved in the pathogenesis of PD. Decreased levels of oleic acid in the blood of PD patients was reported in four studies, whereas increased levels of polyunsaturated fatty acids (PUFAs), particularly arachidonic acid, were identified in both the plasma and cerebrospinal fluid (CSF) of PD patients.

Mitochondrial dysfunction has also been frequently observed in PD patients, as demonstrated by increased levels of acyl-carnitine metabolites and dysregulated fatty acid metabolism. Short-chain fatty acid (SCFA) metabolites including valeric acid and butyric acid have also been observed in the plasma and feces of PD patients.

Many of the reviewed studies also identified altered sphingolipid metabolites. For example, sphingomyelin accumulation in the CSF of PD patients has been observed and likely reflects neurodegeneration in the brain, whereas reduced glycosphingolipid levels have been observed in the plasma of PD patients.

Ten of the reviewed studies identified significantly increased levels of ornithine, a glutamine metabolite involved in the formation of urea, in the blood, urine, and CSF of PD patients. The precise cause for high ornithine levels in PD patients remains unclear, with some studies suggesting dysfunctional mitochondria as the source.

Eight studies reported increased levels of glutamine in the blood, urine, and CSF of PD patients. Comparatively, reduced levels of glutamate in the blood and feces of PD patients were reported in six studies. Altered levels of both glutamine and glutamate in the brain may be a protective mechanism by which neurons attempt to recover from dopamine depletion.

Six of the reviewed studies also reported increased levels of glycine in the plasma, urine, and CSF of PD patients. High levels of glycine, which has been shown to modulate the release of dopamine and glutamate, may reflect an imbalance between dopaminergic and muscarinic cholinergic neurons.

Four studies also identified reduced tryptophan levels in the blood, feces, and CSF of PD patients. Like ornithine, altered tryptophan metabolism may also be related to a dysfunctional mitochondria, as well as impaired brain energy metabolism, both of which may contribute to PD symptoms.

Taken together, about 20% of the metabolites identified from the reviewed studies were reported in more than one study to be involved in the pathogenesis of PD. Most of these metabolites were lipids, which may be due to the ease of studying these structures as compared to other metabolites.

The levels of about 33% of the replicated metabolites were not consistent across studies, which may be due to the different specimens included in the metabolomic studies. Additional factors that may contribute to these inconsistencies include the use of certain drugs that can influence the levels of certain metabolites like ornithine. Different analytical platforms and preparation methods may also contribute to these variations.

The ReconX model

In addition to all reactions obtained from the Recon3D model, 277 new reactions from Human1 were also incorporated into the new ReconX model. Fourteen reactions from a dopaminergic neuronal model, 73 fatty acid oxidation reactions, and 43 caffeine metabolism reactions were also incorporated into ReconX, which led to a final metabolite count of 4,213 and 13,950 reactions.

Within ReconX, the metabolic subsystems that were enriched largely reflected the biological pathways that were found to be altered from the literature review. These pathways included those involved in tyrosine, caffeine, tryptophan, phenylalanine, lysine, and urea cycle metabolism. In addition to these pathways that comprised highly replicated metabolites from the literature review, metabolic markers involved in steroid, polyamine, and dopamine metabolism were also explored in this model.

The PD model developed in this study highlights the classical biosynthesis and degradation pathways of dopamine, in which phenylalanine, tyrosine, and L-Dopa are involved in the generation of dopamine, following which dopamine is degraded to norepinephrine or homovanillic acid. From the literature, norepinephrine was consistently decreased in PD patients, regardless of whether dopamine levels were increased or decreased.

In addition to norepinephrine, the PD model also indicated that certain caffeine downstream metabolites such as theobromine, 1,3,7-trimethyluric acid, 7-methylxanthine, and 5-acetylamino-6-formylamino-3-methyluracil could be used as potential biomarkers for monitoring the progression of PD.

*Important notice: Research Square publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
  • Preliminary scientific report. Fleming, R., Luo, X., Liu, Y., et al. (2023). Identification of metabolites reproducibly associated with Parkinson’s Disease via meta-analysis and computational modelling. Research Square. doi:10.21203/rs.3.rs-3209421/v1
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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