ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community

Key Role for the 12-Hydroxy Group in the Negative Ion Fragmentation of Unconjugated C24 Bile Acids

Host-gut microbial interactions contribute to human health and disease states and an important manifestation resulting from this cometabolism is a vast diversity of bile acids (BAs). There is increasing interest in using BAs as biomarkers to assess the health status of individuals and, therefore, an increased need for their accurate separation and identification. In this study, the negative ion fragmentation behaviors of C24 BAs were investigated by UPLC-ESI-QTOF-MS. The step-by-step fragmentation analysis revealed a distinct fragmentation mechanism for the unconjugated BAs containing a 12-hydroxyl group. The unconjugated BAs lacking 12-hydroxylation fragmented via dehydration and dehydrogenation. In contrast, the 12-hydroxylated ones, such as deoxycholic acid (DCA) and cholic acid (CA), employed dissociation routes including dehydration, loss of carbon monoxide or carbon dioxide, and dehydrogenation. All fragmentations of the 12-hydroxylated unconjugated BAs, characterized by means of stable isotope labeled standards, were associated with the rotation of the carboxylate side chain and the subsequent rearrangements accompanied by proton transfer between 12-hydroxyl and 24-carboxyl groups. Compared to DCA, CA underwent further cleavages of the steroid skeleton. Accordingly, the effects of stereochemistry on the fragmentation pattern of CA were investigated using its stereoisomers. Based on the knowledge gained from the fragmentation analysis, a novel BA, 3β,7β,12α-trihydroxy-5β-cholanic acid, was identified in the postprandial urine samples of patients with nonalcoholic steatohepatitis. The analyses used in this study may contribute to a better understanding of the chemical diversity of BAs and the molecular basis of human liver diseases that involve BA synthesis, transport, and metabolism

ADAP-GC 2.0: Deconvolution of Coeluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC 2.0 has been developed to deconvolute coeluting metabolites that frequently exist in real biological samples of metabolomics studies. Deconvolution is based on a chromatographic model peak approach that combines five metrics of peak qualities for constructing/selecting model peak features. Prior to deconvolution, ADAP-GC 2.0 takes raw mass spectral data as input, extracts ion chromatograms for all the observed masses, and detects chromatographic peak features. After deconvolution, it aligns components across samples and exports the qualitative and quantitative information of all of the observed components. Centered on the deconvolution, the entire data analysis workflow is fully automated. ADAP-GC 2.0 has been tested using three different types of samples. The testing results demonstrate significant improvements of ADAP-GC 2.0, compared to the previous ADAP 1.0, to identify and quantify metabolites from gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS) data in untargeted metabolomics studies

Supplemental Material, DS1_IJT_10.1177_1091581818760746 - Bile Acids as Potential Biomarkers to Assess Liver Impairment in Polycystic Kidney Disease

<p>Supplemental Material, DS1_IJT_10.1177_1091581818760746 for Bile Acids as Potential Biomarkers to Assess Liver Impairment in Polycystic Kidney Disease by William J. Brock, James J. Beaudoin, Jason R. Slizgi, Mingming Su, Wei Jia, Sharin E. Roth, and Kim L. R. Brouwer in International Journal of Toxicology</p

A phylogenetic tree constructed on the basis of the five copies of 16S rRNA gene in each strain using the minimum evolution method.

<p><b>A.</b> Dots with different colors represent the corresponding <i>Yersinia</i> species; tree branch colors are consistent with triangles in <b>B.</b>, which represent different clustering groups.</p

A. Distribution of the type number of 16S rRNA genes in 768 <i>Yersinia</i> strains. The colours in different sections of the pie chart represent the type number of 16S rRNA gene in strains, one type, two types, three types, four types, five types, respectively. The number in the pie represents the number of strains that have each kind of copies of 16S rRNA gene, the percentage in parentheses represents the proportion of all strains. B. The proportion of each copy appearing in different <i>Yersinia</i> species. C. The identical 16S rRNA patterns that exist in different <i>Yersinia</i> species, except for <i>Y</i>. <i>pestis and Y</i>. <i>pseudotuberculosis</i>.

<p>Numbers in the crossed circle represent the number of identical patterns in the corresponding <i>Yersinia</i> species. Numbers in parentheses represent the amount of total patterns in corresponding species. Specific patterns are not shown.</p

Phylogenetic tree based on single 16S rRNA gene from <i>Y</i>. <i>ferderiksenii</i>/<i>intermedia</i> strains in group 1a, 1b, and 4 and strains of <i>Y</i>. <i>ferderiksenii</i> belonging to three geno-species.

<p>Hollow circles represent all 16S rRNA gene types of <i>Y</i>. <i>ferderiksenii</i> /<i>intermedia</i> strains in group 1a, 1b, and 4; while solid circles represent <i>Y</i>. <i>ferderiksenii</i>s trains of three geno-species [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147639#pone.0147639.ref001" target="_blank">1</a>]. Triangles represent identical 16S rRNA gene patterns of strains in group 1a and group 1b.</p

Metabonomic Profiling of Human Placentas Reveals Different Metabolic Patterns among Subtypes of Neural Tube Defects

Neural tube defects (NTDs) are one of the most common types of birth defects with a complex etiology. We have previously profiled serum metabolites of pregnant women in Lvliang prefecture, Shanxi Province of China, which revealed distinct metabolic changes in pregnant women with NTDs outcome. Here we present a metabonomics study of human placentas of 144 pregnant women with normal pregnancy outcome and 115 pregnant women affected with NTDs recruited from four rural counties (Pingding, Xiyang, Taigu, and Zezhou) of Shanxi Province, the area with the highest prevalence worldwide. A panel of 19 metabolites related to one-carbon metabolism was also quantitatively determined. We observed obvious differences in global metabolic profiles and one-carbon metabolism among three subtypes of NTDs, anencephaly (Ane), spina bifida (SB), and Ane complicated with SB (Ane & SB) via mass-spectrometry-based metabonomics approach. Disturbed carbohydrate, amino acid, lipid, and nucleic acid metabolism were identified. Placental transport of amino acids might be depressed in Ane and Ane & SB group. Deficiency of choline contributes to Ane and Ane & SB pathogenesis via different metabolic pathways. The formation of NTDs seemed to be weakly related to folates. The metabonomic analysis reveals that the physiological and biochemical processes of the three subtypes of NTDs might be different and the subtype condition should be considered for the future investigation of NTDs

Metabonomic Phenotyping Reveals an Embryotoxicity of Deca-Brominated Diphenyl Ether in Mice

Recent studies have demonstrated that polybrominated diphenyl ethers (PBDEs), a group of industrial chemicals, could disrupt thyroid hormone homeostasis and exhibit neurotoxicity, reproductive toxicity, and embryotoxicity. However, clear evidence of embryotoxicity and neurotoxicity of many of these congeners, such as deca-BDE, one of the least bioactive congeners of PBDEs, is still lacking. In the present study, we investigated deca-BDE embryotoxicity by quantitative analysis of two essential thyroid hormones (T4 and T3) and a variety of small-molecule metabolites in the serum of deca-BDE-dosed pregnant mice. Four groups of pregnant C57 mice were administrated with deca-BDE in 20% fat emulsion at a dose of 150, 750, 1 500, or 2 500 mg/kg body weight via gastric intubation on gestation days (g.d.s) 7 to 9, while a control group was given 20% fat emulsion. Maternal mice were euthanized on g.d. 16 and examined for external malformations of the fetus. Maternal serum samples were collected and analyzed by the enzyme linked immunosorbent assay (ELISA) and gas chromatography–time-of-flight mass spectrometry (GC–TOF MS). Using multivariate statistical analysis, we observed a significantly altered metabolic profile associated with deca-BDE embryotoxicity in maternal serum. Our results also demonstrated that deca-BDE at a dose of 2 500 mg/kg body weight induced significant disruption of thyroid hormone metabolism, the TCA cycle, and lipid metabolism in maternal mice, which subsequently led to a significant inhibition of fetal growth and development. We concluded that deca-BDE-induced embryotoxicity closely correlated with global metabolic disruption that can be characterized by thyroid hormone deficiency, disrupted lipid metabolism, and a depleted level of cholesterol in maternal mice

High Throughput and Quantitative Measurement of Microbial Metabolome by Gas Chromatography/Mass Spectrometry Using Automated Alkyl Chloroformate Derivatization

The ability to identify and quantify small molecule metabolites derived from gut microbial–mammalian cometabolism is essential for the understanding of the distinct metabolic functions of the microbiome. To date, analytical protocols that quantitatively measure a complete panel of microbial metabolites in biological samples have not been established but are urgently needed by the microbiome research community. Here, we report an automated high-throughput quantitative method using a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) platform to simultaneously measure over one hundred microbial metabolites in human serum, urine, feces, and <i>Escherichia coli</i> cell samples within 15 min per sample. A reference library was developed consisting of 145 methyl and ethyl chloroformate (MCF and ECF) derivatized compounds with their mass spectral and retention index information for metabolite identification. These compounds encompass different chemical classes including fatty acids, amino acids, carboxylic acids, hydroxylic acids, and phenolic acids as well as benzoyl and phenyl derivatives, indoles, etc., that are involved in a number of important metabolic pathways. Within an optimized range of concentrations and sample volumes, most derivatives of both reference standards and endogenous metabolites in biological samples exhibited satisfactory linearity (<i>R</i>² > 0.99), good intrabatch reproducibility, and acceptable stability within 6 days (RSD < 20%). This method was further validated by examination of the analytical variability of 76 paired human serum, urine, and fecal samples as well as quality control samples. Our method involved using high-throughput sample preparation, measurement with automated derivatization, and rapid GC/TOFMS analysis. Both techniques are well suited for microbiome metabolomics studies