Metabotype and genotype mapping of KORA and TwinsUK cohorts

A network view of data published in this study. 

Human metabolic individuality in biomedical and pharmaceutical research

Abstract

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Network Graph

It is an output of KEGG reaction pair (RED), Tanimoto Similarity Distances (Blue) and SNP-metabolite associations (yellow). KRP and Tanimoto provide biochemical/biochemical backbone to the network, whereas SNP link are indicating probable genetic link for the variability. Bigger node size reflect that the metabolite is associated with many SNPs. Many SNPs reported in this table  can be easily pointed out here. But, the table contain only 37 links and we see here more than that, suggesting that many links cannot be mapped to a fully annotated loci. Also, many links does not look realistic due to chemistry constraints, therefore these graphs are very useful to visualize/find gene-metabolite associations that could be supported by chemical/biochemical relationships. 261 compounds out of 275 metabolites are included in this network. A p-value threshold of less than P-08 was used to find significant associations. 

Reporting metabolomics data

Metabolomcis data should be published in a format that is reliable and useful. There are guidelines/SOPs to achieve this. Also, MetaMapp mapping and visualization essentially need data in standardized format. 

Below papers would give you an idea what must be included in the final data matrix before sending it out for publications. Check the supplement sections. 

http://www.nature.com/nbt/journal/v25/n8/full/nbt0807-846b.html MSI metabolomics standard initiative, 2007 

http://www.ncbi.nlm.nih.gov/pubmed/18269577 by O Fiehn, Quality controls in plant metabolomics. 2008

http://www.plantcell.org/content/23/7/2477.full  Recommendations for reporting metabolite profiling data. 2011. Plant Cell. 

These efforts are highly appreciated, but unless the Journal editors adopt it as an mandatory guideline, only a small number of labs will be reporting metabolomics data according to MSI guidelines. One of obstacles is having a really good mass spectrometer but not the right informatics that generate the MSI compliance data. But, MetaboAnalyst.ca and MetabolomeExpress  can significantly assist you to convert raw MS data into useful data matrices. 

Small molecule metabolome in the lung under acrolein-induced acute lung injury

Acrolein is a pulmonary irritant and its exposure can cause damage in lungs. Scientists from Pittsburgh university have published the metabolome of sensitive and resistant mice under acrolein exposure.

Abstract: 


Integrative metabolome and transcriptome profiling reveals discordant energetic stress between mouse strains with differential sensitivity to acrolein-induced acute lung injury.

Scope: This investigation sought to better understand the metabolic role of the lung and to generate insights into the pathogenesis of acrolein-induced acute lung injury. A respiratory irritant, acrolein is generated by overheating cooking oils or by domestic cooking using biomass fuels, and is in environmental tobacco smoke, a health hazard in the restaurant workplace. Methods and results: Using SM/J (sensitive) and 129X1/SvJ (resistant) inbred mouse strains, the lung metabolome was integrated with the transcriptome profile before and after acrolein exposure. A total of 280 small molecules were identified and mean values (log 2 >0.58 or <-0.58, p<0.05) were considered different for between-strain comparisons or within-strain responses to acrolein treatment. At baseline, 24 small molecules increased and 33 small molecules decreased in the SM/J mouse lung as compared to 129X1/SvJ mouse lung. Notable among the increased compounds was malonylcarnitine. Following acrolein exposure, several molecules indicative of glycolysis and branched chain amino acid metabolism increased similarly in both strains, whereas SM/J mice were less effective in generating metabolites related to fatty acid ß-oxidation. Conclusion: These findings suggest management of energetic stress varies between these strains, and that the ability to evoke auxiliary energy generating pathways rapidly and effectively may be critical in enhancing survival during acute lung injury in mice.

Comments

If you navigate through the network graphs below, the impact of acrolein can be observed on ROS scavenging mechanisms, supporting previously well established observation that acrolein causes damages in mitochonrial respiratory chain. Top network graph highlights the metabolic alterations why the mice is resistant, notice the abundance of carnitine and low level of many fatty acids. Graphs were created using 273 compounds, 7 compounds could not be mapped to any chemical structure in PubChem, database.

Metabolomic Profile of Hepatitis C Virus-Infected Hepatocytes

Abstract:- 
Hepatitis C virus (HCV) is capable of disrupting different facets of lipid metabolism and lipids have been shown to play a crucial role in the viral life cycle. The aim of this study was to examine the effect HCV infection has on the hepatocyte metabolome. Huh-7.5 cells were infected using virus produced by the HCV J6/JFH1 cell culture system and cells were harvested 24, 48, and 72-hours following infection. Metabolic profiling was performed using a non-targeted multiple platform methodology combining ultrahigh performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS2) and gas chromatography/mass spectrometry (GC/MS). There was a significant increase in a number of metabolites involved in nucleotide synthesis and RNA replication during early HCV infection. NAD levels were also significantly increased along with several amino acids. A number of lipid metabolic pathways were disrupted by HCV infection, resulting in an increase in cholesterol and sphingolipid levels, altered phospholipid metabolism and a possible disruption in mitochondrial fatty acid transport. Fluctuations in 5'-methylthioadenosine levels were also noted, along with alterations in the glutathione synthesis pathway. These results highlight a number of previously unreported metabolic interactions and give a more in depth insight into the effect HCV has on host cell biochemical processes.

Comments:-

After mapping the data using chemical similarity distances (234 out of total 253 metabolites), it seems that on early HCV infection, the increase in many metabolites is less than 20%, which could be errors prone. There is no consistent effect on metabolome over the time series, early phase has different response than late phase. The most prevalent is decrease in organic acid and increase in fatty acids after 72 hours of infection. the highest fold change is 3.7.

Dessication tolerance in Sporobolus stapfianus

A recent paper from Plant Cell reports metabolic alterations in two closely related species of Sporobolus stapfianus for their dessication tolerance. I just posted one diagram, but there are 7 more. 

A Sister Group Contrast Using Untargeted Global Metabolomic Analysis Delineates the Biochemical Regulation Underlying Desiccation Tolerance in Sporobolus stapfianus. 
Understanding how plants tolerate dehydration is a prerequisite for developing novel strategies for improving drought tolerance. The desiccation-tolerant (DT) Sporobolus stapfianus and the desiccation-sensitive (DS) Sporobolus pyramidalis formed a sister group contrast to reveal adaptive metabolic responses to dehydration using untargeted global metabolomic analysis. Young leaves from both grasses at full hydration or at 60% relative water content (RWC) and from S. stapfianus at lower RWCs were analyzed using liquid and gas chromatography linked to mass spectrometry or tandem mass spectrometry. Comparison of the two species in the fully hydrated state revealed intrinsic differences between the two metabolomes. S. stapfianus had higher concentrations of osmolytes, lower concentrations of metabolites associated with energy metabolism, and higher concentrations of nitrogen metabolites, suggesting that it is primed metabolically for dehydration stress. Further reduction of the leaf RWC to 60% instigated a metabolic shift in S. stapfianus toward the production of protective compounds, whereas S. pyramidalis responded differently. The metabolomes of S. stapfianus leaves below 40% RWC were strongly directed toward antioxidant production, nitrogen remobilization, ammonia detoxification, and soluble sugar production. Collectively, the metabolic profiles obtained uncovered a cascade of biochemical regulation strategies critical to the survival of S. stapfianus under desiccation.

Mapping of Ovary metabolome

Metabolic differences between metastatic vs non-metastatic cancer are visualized using a chemical similarity network. I skipped the KEGG RPAIR calculations so many compound were lacking reaction in KEGG database. Most of the compound detected using LC/MS technology were missing entries in KEGG database and HMDB. Even, around 40 metabolite'a name could not be mapped to any chemical structure in the pubchem database. 

Here is the abstract and network 

Identification of Metabolites in the Normal Ovary and Their Transformation in Primary and Metastatic Ovarian Cancer

In this study, we characterized the metabolome of the human ovary and identified metabolic alternations that coincide with primary epithelial ovarian cancer (EOC) and metastatic tumors resulting from primary ovarian cancer (MOC) using three analytical platforms: gas chromatography mass spectrometry (GC/MS) and liquid chromatography tandem mass spectrometry (LC/MS/MS) using buffer systems and instrument settings to catalog positive or negative ions. The human ovarian metabolome was found to contain 364 biochemicals and upon transformation of the ovary caused changes in energy utilization, altering metabolites associated with glycolysis and β-oxidation of fatty acids—such as carnitine (1.79 fold in EOC,p<0.001; 1.88 fold in MOC, p<0.001), acetylcarnitine (1.75 fold in EOC, p<0.001; 2.39 fold in MOC,p<0.001), and butyrylcarnitine (3.62 fold, p<0.0094 in EOC; 7.88 fold, p<0.001 in MOC). There were also significant changes in phenylalanine catabolism marked by increases in phenylpyruvate (4.21 fold; p = 0.0098) and phenyllactate (195.45 fold; p<0.0023) in EOC. Ovarian cancer also displayed an enhanced oxidative stress response as indicated by increases in 2-aminobutyrate in EOC (1.46 fold, p = 0.0316) and in MOC (2.25 fold, p<0.001) and several isoforms of tocopherols. We have also identified novel metabolites in the ovary, specifically N-acetylasparate and N-acetyl-aspartyl-glutamate, whose role in ovarian physiology has yet to be determined. These data enhance our understanding of the diverse biochemistry of the human ovary and demonstrate metabolic alterations upon transformation. Furthermore, metabolites with significant changes between groups provide insight into biochemical consequences of transformation and are candidate biomarkers of ovarian oncogenesis. Validation studies are warranted to determine whether these compounds have clinical utility in the diagnosis or clinical management of ovarian cancer patients.

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.