Impact of orm knock out on lipids in yeast

It was impossible to associated all the reported lipid entities to lipidmaps and pubchem identifiers. Out of over 300 species only 100 were mapped to IDs. So, I decided to choose an alternative way. First, a chemical similarity network was constructed for different types of lipid classes (diamonds in the figure) later on individual lipid species is connected to these lipid. Other option could be to use fatty acids and degree of un-saturation to connected lipid entities.


The data underlying this figure are coming from a supplementary table from a paper http://www.nature.com/nature/journal/v463/n7284/full/nature08787.html

Orm family proteins mediate sphingolipid homeostasis


Abstract

Despite the essential roles of sphingolipids both as structural components of membranes and critical signalling molecules, we have a limited understanding of how cells sense and regulate their levels. Here we reveal the function in sphingolipid metabolism of the ORM genes (known as ORMDL genes in humans)—a conserved gene family that includes ORMDL3, which has recently been identified as a potential risk factor for childhood asthma. Starting from an unbiased functional genomic approach in Saccharomyces cerevisiae, we identify Orm proteins as negative regulators of sphingolipid synthesis that form a conserved complex with serine palmitoyltransferase, the first and rate-limiting enzyme in sphingolipid production. We also define a regulatory pathway in which phosphorylation of Orm proteins relieves their inhibitory activity when sphingolipid production is disrupted. Changes in ORM gene expression or mutations to their phosphorylation sites cause dysregulation of sphingolipid metabolism. Our work identifies the Orm proteins as critical mediators of sphingolipid homeostasis and raises the possibility that sphingolipid misregulation contributes to the development of childhood asthma.

global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates

Blue nodes : higher in sensitive strains
Red nodes : higher in resistant strains
node size : fold changes
Node without labels : significant but fold change in lower than 2
P value cutoff < 0.05

http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000904

Abstract:
Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.




Half of the metabolites could not be mapped in these graphs. Specially, authors have reported 110 lipids species with names but forgot to report any database identifiers. I tried a lot of methods to convert these names into lipidmaps, pubchem or hmdb identifiers but all in vein. It was not possible to convert a name such as GPC (40:6/2) into any identifier. As a result, these lipids species were left out even though they were significantly altered in the study. 


There were also around 50 compound mainly di and tri peptides those could not associated with any CIDs so they could not be included in the network graph. 


It would be very easy to utilize metabolomics datasets for advanced bioinformatics if they were reported with standard compound database identifiers not with confusing and freaking names such as -- (5Z, 7E, 22E, 24E, 26E)-(1S, 3R)-26a, 26b-dihomo-27-nor-9, 10-seco-5, 7, 10(19), 22, 24, 26(26a)-cholestahexaene-1, 3, 26b-triol) 

Effect of IDH1 mutations on the cellular metabolome

A chemical similarity network visualization of how  point mutations R132 and R172 in IDH1 and IDH2 are affecting the cellular metabolome in cultured cells ?. 

 

Red means : higher in mutants 

Blue means : higher in wild type

no label : significant (p<0.05) but fold was less than 0.50 (+/-)

Node size reflect ; fold change

highest fold change was 160 ; 2HG

http://www.pnas.org/content/early/2011/02/01/1019393108.abstract 

Abstract:- 

Point mutations of the NADP+-dependent isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) occur early in the pathogenesis of gliomas. When mutated, IDH1 and IDH2 gain the ability to produce the metabolite (R)-2-hydroxyglutarate (2HG), but the downstream effects of mutant IDH1 and IDH2 proteins or of 2HG on cellular metabolism are unknown. We profiled >200 metabolites in human oligodendroglioma (HOG) cells to determine the effects of expression of IDH1 and IDH2 mutants. Levels of amino acids, glutathione metabolites, choline derivatives, and tricarboxylic acid (TCA) cycle intermediates were altered in mutant IDH1- and IDH2-expressing cells. These changes were similar to those identified after treatment of the cells with 2HG. Remarkably, N-acetyl-aspartyl-glutamate (NAAG), a common dipeptide in brain, was 50-fold reduced in cells expressing IDH1 mutants and 8.3-fold reduced in cells expressing IDH2 mutants. NAAG also was significantly lower in human glioma tissues containing IDH mutations than in gliomas without such mutations. These metabolic changes provide clues to the pathogenesis of tumors associated with IDH gene mutations.

Around 55 metabolites were could not be mapped into the network graphs because they could not be associated with any PubChem cid which is essential for generating the similarity matrix underlying these network graphs. 

Cystic fibrosis cells compared to non-cystic fibrosis cells on the metabolome level

A  chemical similarity network diagram of a published metabolomics study. In this study, CF cells were compared against non-CF cells for metabolite levels. Each node is a metabolite and node size reflect the p-value. nodes without labels did not passed the p-value cut-off 0.05. Q- value could be mapped to node color but it did not had clear visible appearance. Fold change value and direction of ttest was not provided in the supplement section of the study. Also, they did not reported the metabolites with PubChem identifiers so I had to use other name to id conversion tools to get the CIDs. However, up to 30 metabolites could not be mapped to any identifiers even after manual efforts. 

Abstract can be easily tracked by cross-referencing the compound classes in the diagram. Impact of central energy metabolism, redox metabolism, some amino acids, purine is visible. No alterations in lipids is also visible. 

http://www.jbc.org/content/early/2010/07/30/jbc.M110.140806


METABOLOMIC PROFILING REVEALED BIOCHEMICAL PAHTWAYS AND BIOMARKERS ASSOCIATED WITH PATHOGENSIS IN CYSTIC FIBROSIS CELLS

Cystic fibrosis (CF) is a life-shortening disease caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. In order to gain understanding of the epithelial dysfunction associated with CF mutations and discover biomarkers for therapeutics development, untargeted metabolomic analysis was performed on primary human airway epithelial cell cultures from three separate cohorts of CF patients and non-CF subjects. Statistical analysis revealed a set of reproducible and significant metabolic differences between the CF and non-CF cells. Aside from changes that were consistent with known CF effects, such as diminished cellular regulation against oxidative stress and osmotic stress, new observations on the disease cellular metabolism were generated. In the CF cells, the levels of various purine nucleotides, which may function to regulate cellular responses via purinergic signaling, were significantly decreased. Furthermore, CF cells exhibited reduced glucose metabolism in glycolysis, pentose phosphate pathway, and sorbitol pathway, which may further exacerbate oxidative stress and limit the epithelial cell response to environmental pressure. Taken together, these findings reveal novel metabolic abnormalities associated with CF pathological process and identify a panel of potential biomarkers for therapeutic development using this model system.