Systems Analysis Reveals Ageing-Related Perturbations in Retinoids and Sex Hormones in Alzheimer’s and Parkinson’s Diseases

Neurodegenerative diseases, including Alzheimer’s (AD) and Parkinson’s diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analysed transcriptomic data from AD and PD patients, and stratified these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identified retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrated that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

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Systems analysis reveals ageing-related perturbations in retinoids and sex hormones in Alzheimer's and Parkinson's diseases

10.1101/2021.06.10.447367 ◽

2021 ◽

Author(s):

Simon Lam ◽

Nils Hartmann ◽

Rui Benfeitas ◽

Cheng Zhang ◽

Muhammad Arif ◽

...

Keyword(s):

Systems Analysis ◽

Metabolic Model ◽

Metabolic Profiles ◽

Transcriptomic Data ◽

A Genome ◽

Parkinson’S Diseases ◽

Ageing Model ◽

Related Feature ◽

Patient Responses ◽

Genome Scale

Neurodegenerative diseases (NDDs), including Alzheimer's (AD) and Parkinson's diseases (PD), are complex heterogeneous diseases with highly variable patient responses to treatment. Due to the growing evidence for ageing-related clinical and pathological commonalities between AD and PD, these diseases have recently been studied in tandem. In this study, we analyse transcriptomic data from AD and PD patients, and stratify these patients into three subclasses with distinct gene expression and metabolic profiles. Through integrating transcriptomic data with a genome-scale metabolic model and validating our findings by network exploration and co-analysis using a zebrafish ageing model, we identify retinoids as a key ageing-related feature in all subclasses of AD and PD. We also demonstrate that the dysregulation of androgen metabolism by three different independent mechanisms is a source of heterogeneity in AD and PD. Taken together, our work highlights the need for stratification of AD/PD patients and development of personalised and precision medicine approaches based on the detailed characterisation of these subclasses.

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Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer

BMC Bioinformatics ◽

10.1186/s12859-019-2685-9 ◽

2019 ◽

Vol 20 (S4) ◽

Cited By ~ 8

Author(s):

Ilaria Granata ◽

Enrico Troiano ◽

Mara Sangiovanni ◽

Mario Rosario Guarracino

Keyword(s):

Breast Cancer ◽

Metabolic Model ◽

Transcriptomic Data ◽

A Genome ◽

Genome Scale

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Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

Toxicology and Applied Pharmacology ◽

10.1016/j.taap.2020.115390 ◽

2021 ◽

Vol 412 ◽

pp. 115390

Author(s):

Kristopher D. Rawls ◽

Bonnie V. Dougherty ◽

Kalyan C. Vinnakota ◽

Venkat R. Pannala ◽

Anders Wallqvist ◽

...

Keyword(s):

Metabolic Model ◽

Renal Metabolism ◽

A Genome ◽

Compound Exposure ◽

Genome Scale

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Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Applied and Environmental Microbiology ◽

10.1128/aem.01795-12 ◽

2012 ◽

Vol 78 (24) ◽

pp. 8735-8742 ◽

Cited By ~ 12

Author(s):

Yilin Fang ◽

Michael J. Wilkins ◽

Steven B. Yabusaki ◽

Mary S. Lipton ◽

Philip E. Long

Keyword(s):

Proteomic Analysis ◽

In Silico ◽

Field Experiments ◽

Metabolic Model ◽

Geobacter Metallireducens ◽

Content Type ◽

Proteomic Data ◽

Subsurface Environment ◽

A Genome ◽

Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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A Genome-Scale Metabolic Model of Anabaena 33047 to Guide Genetic Modifications to Overproduce Nylon Monomers

Metabolites ◽

10.3390/metabo11030168 ◽

2021 ◽

Vol 11 (3) ◽

pp. 168

Author(s):

John I. Hendry ◽

Hoang V. Dinh ◽

Debolina Sarkar ◽

Lin Wang ◽

Anindita Bandyopadhyay ◽

...

Keyword(s):

Electron Transport ◽

Pyruvate Decarboxylase ◽

Metabolic Model ◽

Cell Types ◽

Economic Feasibility ◽

Nitrogen Fixing ◽

Design Algorithm ◽

A Genome ◽

Anabaena Sp ◽

Genome Scale

Nitrogen fixing-cyanobacteria can significantly improve the economic feasibility of cyanobacterial production processes by eliminating the requirement for reduced nitrogen. Anabaena sp. ATCC 33047 is a marine, heterocyst forming, nitrogen fixing cyanobacteria with a very short doubling time of 3.8 h. We developed a comprehensive genome-scale metabolic (GSM) model, iAnC892, for this organism using annotations and content obtained from multiple databases. iAnC892 describes both the vegetative and heterocyst cell types found in the filaments of Anabaena sp. ATCC 33047. iAnC892 includes 953 unique reactions and accounts for the annotation of 892 genes. Comparison of iAnC892 reaction content with the GSM of Anabaena sp. PCC 7120 revealed that there are 109 reactions including uptake hydrogenase, pyruvate decarboxylase, and pyruvate-formate lyase unique to iAnC892. iAnC892 enabled the analysis of energy production pathways in the heterocyst by allowing the cell specific deactivation of light dependent electron transport chain and glucose-6-phosphate metabolizing pathways. The analysis revealed the importance of light dependent electron transport in generating ATP and NADPH at the required ratio for optimal N2 fixation. When used alongside the strain design algorithm, OptForce, iAnC892 recapitulated several of the experimentally successful genetic intervention strategies that over produced valerolactam and caprolactam precursors.

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Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽

10.1099/mic.0.076091-0 ◽

2014 ◽

Vol 160 (6) ◽

pp. 1252-1266 ◽

Cited By ~ 28

Author(s):

Hassan B. Hartman ◽

David A. Fell ◽

Sergio Rossell ◽

Peter Ruhdal Jensen ◽

Martin J. Woodward ◽

...

Keyword(s):

Salmonella Enterica ◽

Energy Demand ◽

Drug Targets ◽

Minimal Medium ◽

Model Organism ◽

Metabolic Model ◽

Scale Model ◽

Glucose Minimal Medium ◽

A Genome ◽

Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

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Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

10.1101/2021.02.18.431926 ◽

2021 ◽

Author(s):

Christopher E. Lawson ◽

Aniela B. Mundinger ◽

Hanna Koch ◽

Tyler B. Jacobson ◽

Coty A. Weathersby ◽

...

Keyword(s):

Wastewater Treatment ◽

Metabolic Networks ◽

Metabolic Model ◽

Scale Model ◽

Nitrifying Bacteria ◽

Metabolomic Analysis ◽

A Genome ◽

Nitrite Oxidizing Bacteria ◽

Genome Scale ◽

Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

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Investigating Moorella thermoacetica metabolism with a genome-scale constraint-based metabolic model

Integrative Biology ◽

10.1039/c5ib00095e ◽

2015 ◽

Vol 7 (8) ◽

pp. 869-882 ◽

Cited By ~ 19

Author(s):

M. Ahsanul Islam ◽

Karsten Zengler ◽

Elizabeth A. Edwards ◽

Radhakrishnan Mahadevan ◽

Gregory Stephanopoulos

Keyword(s):

Metabolic Model ◽

Strictly Anaerobic ◽

Moorella Thermoacetica ◽

A Genome ◽

Acetogenic Bacterium ◽

Genome Scale

Moorella thermoaceticais a strictly anaerobic, endospore-forming, and metabolically versatile acetogenic bacterium capable of conserving energy by both autotrophic (acetogenesis) and heterotrophic (homoacetogenesis) modes of metabolism.

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