Current Metabolomics - Volume 6, Issue 2, 2018

Background: Vibrational spectroscopy has shown the capacity to provide rapid, accurate and relevant information on microorganism's classification and identification. In fact Fourier Transform Infrared Spectroscopy is a sensitive and effective methodology in food analysis, clinical diagnosis and environmental evaluation, with successful identification at genus and species level. Focus: The interest in this technique lies not only in the identification and classification of bacteria, but there is also relevance in using FTIR to follow cellular responses to stress in unicellular models, with particular interest in yeasts and so far results show FTIR is able to evaluate specific cellular changes related to stress stimulus, mainly in protein region. Prospect: This work gathered information that demonstrates that it is possible to overcome signal overlapping and use Fourier Transform Infrared Spectroscopy and multivariate analysis tools to obtain biochemical signatures of microorganisms and corresponding metabolome that can provide inexpensive and highly specific alternatives to conventional typing methods. Also, it presents a spectrum representative of a microbial cell and summarizes in a table the spectral assignments of main biological contributors.

Background: Domestic chickens are a valuable yet underutilized set of model organisms for studies relevant to human obesity and adipose metabolism. Chickens and humans share similarities in adipose tissue lipid metabolism, and the in ovo development of the chick enables studies of adipose development that are difficult to perform in other organisms. Transcriptomic and metabolomic studies have begun to characterize adipose metabolism in this model organism and provide important insight into mechanisms that control adipose deposition and of leanness. Methods: Recent studies that have used transcriptomics and metabolomics to understand mechanisms that control fat mass in chickens are reviewed. Results: Genetically distinct pairs of relatively lean and fat lines of chickens have been compared through transcriptomics and metabolomics. Despite differences in genetic background and in the means used to select for divergent fatness, some common metabolic pathways have been found to regulate adipose deposition in these studies. Conclusion: Mechanisms that are implicated in these studies provide valuable insight into adipose tissue expansion and highlight the utility of chickens as a model for studies of obesity.

Globally, cancer is one of the leading causes of death, so the development of strategies for an early diagnosis of cancer is of great importance. Biochemical alterations precede morphological changes in cells and tissues, so studying cancer metabolome seems like a reasonable approach for early diagnosis, prognosis and to follow treatment progression. Fourier-Transform Infrared (FTIR) spectroscopy is a valuable tool for studying the metabolome of biological samples, such as cancer cell lines. Unlike staining procedures and other histopathologic approaches, this technique is rapid, nondestructive and does not require reagents. The spectral differences that result from probing the biochemical composition of cancer and normal cells are indicative of distinct metabolic profiles, which allow the discrimination of different cells. Using FTIR spectroscopy and multivariate statistical analysis, several alterations concerning the content of lipids, proteins, nucleic acids and carbohydrates have been identified in cancer cells, some of which can be regarded as potential biomarkers. This review focuses on FTIR spectroscopy as a metabolomics tool to study and characterize cancer cell lines.

Background: Despite cisplatin's effectiveness against ovarian cancer, these cancer cells have shown the ability to resist chemotherapy – a resistance that represents a major obstacle to current therapeutic strategies. Objective: The objective of the study was to determine whether or not cellular resistance could be linked to changes in metabolites. Methods: Liquid Chromatography-mass Spectrometry (LC-MS) hydrophilic interaction chromatography was used to analyze the intracellular metabolomic profile of ovarian cancer cell line A2780 and the cisplatin resistant cell line A2780CR, before and following treatment with cisplatin at inhibitory concentration (IC50). Phenotype MicroArray™ (PM) experiments were also applied in order to test carbon substrate utilization or sensitivity in both cell lines after exposure to cisplatin. Data extraction was carried out with MZmine 2.10 with metabolite searching against an in-house database. The data were analyzed using univariate and multivariate Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) methods. Results: There was clear discrimination between the controls and the cisplatin treated samples on the basis of PCA and OPLS-DA. The cisplatin-sensitive cells were as expected more sensitive to cisplatin than the resistant cells with IC50 values of 4.9 and 10.8 μg/mL, respectively. The results demonstrated that the intracellular metabolomic changes induced by cisplatin in the cisplatin-sensitive cells led to reduced levels of acetylcarnitine, phosphocreatine, arginine, proline and Glutathione Disulfide (GSSG) as well as to increased levels of tryptophan and methionine. While PM experiments showed lowered glucose metabolism in the sensitive cells following treatment which was reflected in decreased levels of ATP. Conclusion: Overall the metabolic changes induced in A2780CR cells by cisplatin were much fewer than those induced in A2780 cells. The sensitive cells had a much quicker onset of apoptosis than the resistant cells as judged by measurement of caspase 3. Increased resistance to oxidative stress in the resistant cells was consistent with higher levels of proline, due to less induction of proline dehydrogenase, and elevated levels of glutathione (GSH) and GSSG following cisplatin treatment.

Background: Non-digestible carbohydrates are degraded by bacterial fermentation in the large intestine to yield Short-Chain Fatty Acids (SCFAs), such as acetate, propionate and butyrate. SCFAs are known to induce beneficial physiological and metabolic effects in the gut and the host. The pathogenesis of Irritable Bowel Disease (IBD) involves alterations or dysbiosis of the normal intestinal bacterial flora with reductions in butyrate producing bacteria noted in several datasets. Objective: The objective of the study was to develop a Liquid Chromatography (LC-MS) method for the analysis of short chain fatty acids. Methods: Short Chain Fatty Acids (SCFAs) were coupled to N,N-dimethyl-p-phenylenediamine using a carbodimide coupling. The analysis of the derivatised SCFAs was carried out by using Hydrophilic Interaction Chromatography (HILIC) coupled with an Orbitrap Exactive mass spectrometer. The method was calibrated in the range 0.05-1.6 μg/ml using stable isotope labelled internal standards. The method was applied to urine samples obtained from patients with active Ulcerative Colitis (UC), patients with UC in remission and healthy controls. Results: Repeat analysis (n=5) of a urine sample gave the following values for concentrations of the SCFAs: acetate 134.7μM (RSD ± 11.2%), propionate 1.68 μM (RSD ±23.9%), butyrate 16.1μM (RSD ± 8.0%). The values obtained for SCFAs in plasma were: acetate 60.3μM(RSD ±9.17%), propionate 6.4 μM (RSD± 30.4%), and butyrate 21.2μM (RSD± 10.7%). The levels of butyrate were higher in patients in remission than in the other two groups. The method was highly sensitive but contamination with SCFAs from the environment, which was below 50 ng, determined the practical LODs. To work at levels < 50 ng, a dedicated laboratory area would be required. Conclusion: The method described was highly sensitive but limited by background levels of SCFAs in the environment. The results suggest its potential future role in the measurment of SCFAs in UC management. However, a larger cohort would be required to validate its usefulness in the diagnosis and monitoring of UC.

Background: Lipidomics profiling of urinary exosomes of patients with diabetes mellitus and diabetic nephropathy was performed. Urinary exosomes are a rich source of lipid molecules, which can be used to identify the important signaling lipid molecules. Objective: The lipids are key molecules involved in various signaling pathways of the pathophysiology of diabetes mellitus. To explore the therapeutic potential of these pathways and to suggest the potential targets for intervention in diabetic nephropathy, we conducted the retrospective lipidomics profiling of diabetic and diabetic nephropathy phenotype. Methods: We used the reverse phase Liquid Chromatography Coupled Mass Spectrometry (LC-MS) method in the negative ionization mode. The diabetic nephropathy (n=10) and Diabetes mellitus (10) were included in the study. The extraction of features was done using “XCMS” package in R. The Bayesian regularized t-test was performed using Cyber-T online tool to identify the significant lipid molecules. The differential density distributions of lipids for two groups were studied using “sm” package in R. Results: The fold change analysis of lipid molecules present in exosomes suggests a significant enrichment of glycerol lipids, which could be involved in phospholipid and sphingolipid pathways. The hypothesis testing of similarity of diabetic nephropathy and diabetes mellitus phenotypes using the regularized Bayesian t-test showed 238 analytical peaks as significant with a p-value less than 0.05. There was no association between fold change and significance found. The density plots of PC, lyso-PC, PIP2, DG, and GM3 lipid showed significant difference in the diabetic nephropathy and diabetes mellitus groups by using bootstrap re-sampling method of hypothesis testing. The involvement of phospholipase C and phospholipase A2 was suggested in the patho-physiology of diabetic nephropathy. Conclusion: This study suggests the potential use of Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC) method together with Bayesian regularized t-test and bootstrap re-sampling hypothesis testing for integrated lipidomics analysis to identify marker lipids of small sample size.

Background: Cultivated wheat exists in two ploidies: allotetraploid pasta wheat and allohexaploid bread wheat. Ploidy is known to modify plant metabolism, yet studies comparing metabolomic profiles of allotetraploid and allohexaploid wheat are scarce. Objective: To characterize the ploidy and metabolomic profiles of selected Afghan wheat landraces collected in the 1960's and preserved in Japan and elite wheat cultivars. Methods: Ploidy was determined in cell nuclei of wheat seedlings using flow cytometry. Metabolic profiles of polar compounds were obtained with 1H NMR. Metabolite identification was performed with 2D NMR and comparison against databases. Results: We identified allotetraploid and allohexaploid variants of wheat landraces collected in Afghanistan half a century ago. 1H NMR metabolomics discovered a separation between the allotetraploid and allohexaploid metabolic profiles. 2D NMR based compound identification showed that the most variable metabolites were low molecular weight sugars, compounds that were more abundant in seeds of allotetraploid wheats. Conclusion: We discovered a correlation between ploidy and metabolomic profiles with higher concentrations of soluble sugars in allotetraploid wheat.

Background: Metabolomics is a relatively new and dominant branch of bioinformatics. Metabolite expression level controls the phenotypic characteristics of any organism. Recently, breast cancer is the leading type of cancer in women across the world, accounting for 25% of all cases. In 2012, it was seen that due to breast cancer, there were 1.68 million cases and 522,000 deaths. Therefore, for drug discovery as well as for early disease status prediction, significant metabolites identification for breast cancer and correct classification of the breast cancer status through classification technique are very important for metabolomics data analysis. Objective: The main objective of this paper is to identify significant metabolites (p-value<0.05) and state of the art classification technique for breast cancer prediction using metabolomics dataset. Methods: Although there are several techniques to identify significant metabolites, here, we took Student's t-test and Kruskal-Wallis test for significant metabolites identification. To classify the breast cancer prediction, we considered five modern classification techniques- (i) Naive Bayes (NB) (ii) Support Vector Machine (SVM) (iii) Linear Discriminant Analysis (LDA) (iv) k-nearest neighbors algorithm (kNN) and (v) Random Forest (RF). We also measured the performances of the classification techniques through accuracy, sensitivity, specificity, Receiver Operating Characteristic (ROC) curve and area under the ROC curve etc. Results: The performance measures of different classification techniques showed that random forest classifier produced higher accuracy, sensitivity, specificity and area under the ROC curve compared to the other classification techniques for breast cancer prediction using metabolomics dataset. The analytical results also showed that there are 24 significant (adjusted p-value < 0.05) metabolites influencing breast cancer. Conclusion: On the basis of the experimental results, we could say that there are 24 breast cancer influencing metabolites and for breast cancer prediction as well as metabolomics data analysis, random forest is the state of the art and outlier-robust classifier among the five classification techniques.