Systematic investigation of mouse models of Parkinson's disease by transcriptome mapping on a brain-specific genome-scale metabolic network

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

Brain-wide transcriptome-based metabolic alterations in Parkinson's disease: human inter-region and human-experimental model correlations

Alterations in brain metabolism are closely associated with the molecular hallmarks of Parkinson's disease (PD). A clear understanding of the main metabolic perturbations in PD is therefore important. Here, we retrospectively analysed the expression of metabolic genes from 34 PD-control post-mortem human brain transcriptome data comparisons from literature, spanning multiple brain regions. We found high metabolic correlations between the Substantia nigra (SN)- and cerebral cortex-derived tissues. Moreover, three clusters of PD patient cohorts were identified based on perturbed metabolic processes in the SN - each characterised by perturbations in (a) bile acid metabolism (b) omega-3 fatty acid metabolism, and (c) lipoic acid and androgen metabolism - metabolic themes not comprehensively addressed in PD. These perturbations were supported by concurrence between transcriptome and proteome changes in the expression patterns for CBR1, ECI2, BDH2, CYP27A1, ALDH1B1, ALDH9A1, ADH5, ALDH7A1, L1CAM, and PLXNB3 genes, providing a valuable resource for drug targeting and diagnosis. Also, we analysed 58 PD-control transcriptome data comparisons from in vivo/in vitro disease models and identified experimental PD models with significant correlations to matched human brain regions. Collectively, our findings suggest metabolic alterations in several brain regions, heterogeneity in metabolic alterations between study cohorts for the SN tissues and the need to optimize current experimental models to advance research on metabolic aspects of PD.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.