China
Africa
Explore chapters and articles related to this topic
“Omics”
Published in Kirk A. Phillips, Dirk P. Yamamoto, LeeAnn Racz, Total Exposure Health, 2020
The metabolome represents a vast array of small molecules spanning a 1011-fold dynamic range of femtomolar (fM) to millimolar (mM) representing both endogenous and exogenous molecules like amino acids, metabolites, carbohydrates, lipids, and dietary and environmental chemicals. The human metabolite database (http://www.hmdb.ca/metabolites) catalogs ~114,156 metabolites as of October 2019, while METLIN has over 1 million (Guijas et al. 2018). Rattray et al. (2018) recently reviewed strategies for exploiting the metabolome for gene environment interaction epidemiological studies.
Characterizing Outdoor Air Using Microbial Volatile Organic Compounds (MVOCs)
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Sonia Garcia-Alcega, Frédéric Coulon
The schematic procedure for the analysis of the samples is represented in Figure 6.5. The procedure of the analysis of MVOCs from environmental air starts with the noise removal of the chromatograms (using SNIP baseline detector for example) followed by peak deconvolution, which allows accurate mass spectra identification from complex chromatograms (e.g., AMDIS). Then, it proceeds with the identification of all the peaks within the chromatograms of the samples, and this can be done with NIST or free databases such as mzCloud or METLIN. The mzCloud uses a new third generation spectra correlation algorithm to search and identify the compounds. METLIN is a metabolomics database useful for the identification of metabolites, which are linked to the KEGG database (Kanehisa Laboratories, 2016) to see the metabolic pathways and the microorganisms producers. The MVOCs analysis for ambient air is complex because not all MVOCs have solely microbial origin; these also can be anthropogenic or produced by fruits or vegetables. To discriminate MVOCs from VOCs there is an approach available looking at metabolical databases such as KEGG database (Kanehisa Laboratories, 2016) or mVOC database (Lemfack et al., 2014). After identifying the chromatogram peaks, the m/z spectra of the MVOCs are analyzed statistically by multivariate analysis (Schenkel et al., 2015), hierarchical Cluster Analysis (HCA) or multidimensional scaling analysis (MDS) and/or principal component analysis (PCA) to study the MVOCs patterns and trends across the samples (Schenkel et al., 2015; Sun et al., 2014). These analysis can be done using chemometric software such as among others SpectConnect, ACD/MS Manager, OpenChrom, Mass Profiler Professional, or commercial statistical software (MATLAB, ADAPT, etc.) (Murphy et al., 2012). These statistical analyses help us to identify the key and more representative compounds per site, giving us a preliminary idea of the potential markers for each site. Then, the microbial identity of these potentially species-specific markers should be verified by correlation with DNA sequencing analysis. Specific microbial markers could be used in the future for the identification of microbes in air ideally with a sampling device that gives real time data.
An in vitro study on the differentiated metabolic mechanism of chloroquine-resistant Plasmodium falciparum using high-resolution metabolomics
Published in Journal of Toxicology and Environmental Health, Part A, 2021
Jinhyuk Na, Jian Zhang, Young Lan Choe, Chae Seung Lim, Youngja Hwang Park
The significant features from paired comparisons 3D7-Con versus Dd2-Con and 3D7-CQ versus Dd2-CQ for RBCs and media were annotated using xMSannotator, an R package (Uppal, Walker, and Jones 2017) that utilizes the databases for metabolites including HMDB, Metlin, and KEGG database (Guijas et al. 2018; Kanehisa 2002; Kanehisa and Goto 2000; Ogata et al. 1998; Smith et al. 2005; Wishart et al. 2018). The package annotates the significant features with a confidence m/z limit of 10 ppm and 4 adducts [M + H]+, [M+ Na]+, [M + K]+, and [M + H-H2O]+. Moreover, the package provides KEGG IDs, and hence, plays a critical role in data interpretation by linking the metabolites to pathway analysis from Metaboanalyst (www.metaboanalyst.ca), which was performed based upon KEGG IDs (Chong, Wishart, and Xia 2019). The analysis revealed the number of metabolites that matched to the pathway database and also presented the calculated p-value and impact for each pathway. The obtained information of significant pathways is depicted in a bubble plot with the list of top pathways based upon p-values. The metabolites belonging to significant pathways (p < .05) were analyzed using GraphPad Prism v 7.03 software (La Jolla, California) to visualize relative intensities as bar graphs.
Short-term metabolic disruptions in urine of mouse models following exposure to low doses of oxygen ion radiation
Published in Journal of Environmental Science and Health, Part C, 2021
Michael Girgis, Yaoxiang Li, Meth Jayatilake, Kirandeep Gill, Sirao Wang, Kepher Makambi, Vijayalakshmi Sridharan, Amrita K Cheema
Mass search was performed using the “cmmr” R package (CEU Mass Mediator RESTful API) for metabolite annotation. The databases searched by the “cmmr” R package included KEGG, HMDB, LipidMaps, METLIN, and PubChem. Next, in order to confirm the identity of significantly dysregulated metabolites, we performed tandem mass spectrometry wherein the MS/MS spectra were matched against the METLIN database. For biomarker panel selection we used a 100-fold cross-validation approach to determine hyper parameters and calibrate the prediction model in the discovery set and testing set. Then, the optimal value of lambda, obtained by the cross-validation procedure, was used to fit the model. Finally, all the features with non-zero coefficients were retained as the candidate biomarker panel. Receiver operator characteristic curves were used to determine the efficiency of biomarker panels using training and validation sets. The ROC curve can be understood as a plot of the probability of classifying the positive samples correctly against the rate of incorrectly classifying true negative samples. Therefore, the AUC measure of an ROC plot is a measure of predictive accuracy.
An overview of the current progress, challenges, and prospects of human biomonitoring and exposome studies
Published in Journal of Toxicology and Environmental Health, Part B, 2019
Mariana Zuccherato Bocato, João Paulo Bianchi Ximenez, Christian Hoffmann, Fernando Barbosa
In metabolomics, raw data are subjected to a pre-processing step according to the type of analytical platform used. For NMR, data processing includes phasing, baseline correction, alignment, and normalization. Commercial software and algorithms such as PERCH (PERCH Solution Company Ltd.), ChenomxRMNSuite (Chenomx Inc.), MestReNova (MestreLab Research), MetaboLab, AutoFit, TopSpin (Bruker Corp.) and MATLAB (The MathWorks Inc.) are routinely employed. On the other hand, using MS techniques, data processing includes spectral deconvolution, dataset creation, grouping, alignment, filling of data gaps, normalization, and transformation of data (Sussulini 2017). The obtained data are preprocessed with the utilization of free tools such as XCMS, MZmine, MAVENeMetaboAnalyst, as well as commercial software SIMCA-P, SAS (Alden et al. 2017). Numerous databases are available to aid in the identification of metabolites (Human metabolome database (HMDB)), METLIN, NIST MS Library, KEGG (Kamburov et al. 2011; Wishart et al. 2007). However, a critical limitation of nontarget analysis of metabolites of exogenous origin such as pollutants is the lack of sufficient sensitivity of instrumentation used to obtain data (MS with higher resolution power, example q-TOF or Orbitrap). These analytes are usually found in clinical specimens in concentrations lower than femtogram.