MyMedR

Displaying all 3 publications

Abstract:

Sort:

Fulltext Computational prediction of changes in brain metabolic fluxes during Parkinson's disease from mRNA expression

Supandi F, van Beek JHGM

PLoS One, 2018;13(9):e0203687.
PMID: 30208076 DOI: 10.1371/journal.pone.0203687

BACKGROUND: Parkinson's disease is a widespread neurodegenerative disorder which affects brain metabolism. Although changes in gene expression during disease are often measured, it is difficult to predict metabolic fluxes from gene expression data. Here we explore the hypothesis that changes in gene expression for enzymes tend to parallel flux changes in biochemical reaction pathways in the brain metabolic network. This hypothesis is the basis of a computational method to predict metabolic flux changes from post-mortem gene expression measurements in Parkinson's disease (PD) brain.
RESULTS: We use a network model of central metabolism and optimize the correspondence between relative changes in fluxes and in gene expression. To this end we apply the Least-squares with Equalities and Inequalities algorithm integrated with Flux Balance Analysis (Lsei-FBA). We predict for PD (1) decreases in glycolytic rate and oxygen consumption and an increase in lactate production in brain cortex that correspond with measurements (2) relative flux decreases in ATP synthesis, in the malate-aspartate shuttle and midway in the TCA cycle that are substantially larger than relative changes in glucose uptake in the substantia nigra, dopaminergic neurons and most other brain regions (3) shifts in redox shuttles between cytosol and mitochondria (4) in contrast to Alzheimer's disease: little activation of the gamma-aminobutyric acid shunt pathway in compensation for decreased alpha-ketoglutarate dehydrogenase activity (5) in the globus pallidus internus, metabolic fluxes are increased, reflecting increased functional activity.
CONCLUSION: Our method predicts metabolic changes from gene expression data that correspond in direction and order of magnitude with presently available experimental observations during Parkinson's disease, indicating that the hypothesis may be useful for some biochemical pathways. Lsei-FBA generates predictions of flux distributions in neurons and small brain regions for which accurate metabolic flux measurements are not yet possible.
Fulltext Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain

Gavai AK, Supandi F, Hettling H, Murrell P, Leunissen JA, van Beek JH

PLoS One, 2015;10(3):e0119016.
PMID: 25806817 DOI: 10.1371/journal.pone.0119016

Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.
Inherited metabolic disorders presenting as hypoxic ischaemic encephalopathy: A case series of patients presenting at a tertiary care hospital in Pakistan

Zahid M, Khan AH, Yunus ZM, Chen BC, Steinmann B, Johannes H, et al.

J Pak Med Assoc, 2019 Mar;69(3):432-436.
PMID: 30890842

In spite of the efforts and interventions by the Government of Pakistan and The World Health Organization, the neonatal mortality in Pakistan has declined by only 0.9% as compared to the global average decline of 2.1% between 2000 and 2010. This has resulted in failure to achieve the global Millennium Development Goal 4. Hypoxic-ischaemic encephalopathy, still birth, sepsis, pneumonia, diarrhoea and birth defects are commonly attributed as leading causes of neonatal mortality in Pakistan. Inherited metabolic disorders often present at the time of birth or the first few days of life. The clinical presentation of the inherited metabolic disorders including hypotonia, seizure and lactic acidosis overlap with clinical features of hypoxic-ischaemic encephalopathy and sepsis. Thus, these disorders are often either missed or wrongly diagnosed as hypoxicischaemic encephalopathy or sepsis unless the physicians actively investigate for the underlying inherited metabolic disorders. We present 4 neonates who had received the diagnosis of hypoxic-ischaemic encephalopathy and eventually were diagnosed to have various inherited metabolic disorders. Neonates with sepsis and hypoxic-ischaemic encephalopathy-like clinical presentation should be evaluated for inherited metabolic disorders.

Filters

Please provide feedback to Administrator ([email protected])

External Links