GCMS-ID: a webserver for identifying compounds from gas chromatography mass spectrometry experiments.

GCMS-ID (Gas Chromatography Mass Spectrometry compound IDentifier) is a webserver designed to enable the identification of compounds from GC-MS experiments. GC-MS instruments produce both electron impact mass spectra (EI-MS) and retention index (RI) data for as few as one, to as many as hundreds of different compounds. Matching the measured EI-MS, RI or EI-MS + RI data to experimentally collected EI-MS and/or RI reference libraries allows facile compound identification. However, the number of available experimental RI and EI-MS reference spectra, especially for metabolomics or exposomics-related studies, is disappointingly small. Using machine learning to accurately predict the EI-MS spectra and/or RIs for millions of metabolomics and/or exposomics-relevant compounds could (partially) solve this spectral matching problem. This computational approach to compound identification is called in silico metabolomics. GCMS-ID brings this concept of in silico metabolomics closer to reality by intelligently integrating two of our previously published webservers: CFM-EI and RIpred. CFM-EI is an EI-MS spectral prediction webserver, and RIpred is a Kovats RI prediction webserver. We have found that GCMS-ID can accurately identify compounds from experimental RI, EI-MS or RI + EI-MS data through matching to its own large library of >1 million predicted RI/EI-MS values generated for metabolomics/exposomics-relevant compounds. GCMS-ID can also predict the RI or EI-MS spectrum from a user-submitted structure or annotate a user-submitted EI-MS spectrum. GCMS-ID is freely available at https://gcms-id.ca/.

MiMeDB: the Human Microbial Metabolome Database.

The Human Microbial Metabolome Database (MiMeDB) (https://mimedb.org) is a comprehensive, multi-omic, microbiome resource that connects: (i) microbes to microbial genomes; (ii) microbial genomes to microbial metabolites; (iii) microbial metabolites to the human exposome and (iv) all of these 'omes' to human health. MiMeDB was established to consolidate the growing body of data connecting the human microbiome and the chemicals it produces to both health and disease. MiMeDB contains detailed taxonomic, microbiological and body-site location data on most known human microbes (bacteria and fungi). This microbial data is linked to extensive genomic and proteomic sequence data that is closely coupled to colourful interactive chromosomal maps. The database also houses detailed information about all the known metabolites generated by these microbes, their structural, chemical and spectral properties, the reactions and enzymes responsible for these metabolites and the primary exposome sources (food, drug, cosmetic, pollutant, etc.) that ultimately lead to the observed microbial metabolites in humans. Additional, extensively referenced data about the known or presumptive health effects, measured biosample concentrations and human protein targets for these compounds is provided. All of this information is housed in richly annotated, highly interactive, visually pleasing database that has been designed to be easy to search, easy to browse and easy to navigate. Currently MiMeDB contains data on 626 health effects or bioactivities, 1904 microbes, 3112 references, 22 054 reactions, 24 254 metabolites or exposure chemicals, 648 861 MS and NMR spectra, 6.4 million genes and 7.6 billion DNA bases. We believe that MiMeDB represents the kind of integrated, multi-omic or systems biology database that is needed to enable comprehensive multi-omic integration.

A fast and accurate colorimetric assay for quantifying hippuric acid in human urine.

Hippuric acid is an abundant metabolite in human urine. Urinary hippuric acid levels change with toxic exposure to aromatic compounds, consumption of fruits and vegetables, cancers, chronic kidney disease, schizophrenia and Crohn's disease. While urinary hippuric acid can be detected and quantified via mass spectrometry or nuclear magnetic resonance spectroscopy, a colorimetric assay would be preferable for a low-cost, point-of care clinical assay. Two colorimetric methods, that use p-dimethylaminobenzaldehyde (DMAB) or benzenesulfonyl chloride (PhSO2Cl), respectively, have been previously developed to detect hippuric acid but these assays have many limitations. We replaced PhSO2Cl with p-toluenesulfonyl chloride (p-TsCl), to create a simpler, faster and more accurate method that works with human urine. This modified colorimetric assay detects from 60 µM to 1000 µM hippuric acid in urine in 2 min. We also corrected for the effects of interfering compounds present in urine such that the assay works across many urine backgrounds. We validated this improved assay on multiple hippurate-spiked urine samples, observing an excellent correlation (R2 > 0.94) between observed and known hippurate concentrations. These data suggest that this colorimetric assay is accurate and should greatly facilitate the measurement of hippuric acid in urine to detect a variety of human conditions.

Practical Aspects of NMR-Based Metabolomics.

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

A fast, sensitive, single-step colorimetric dipstick assay for quantifying ascorbic acid in urine.

The presence of ascorbate in human urine has been shown to be a useful dietary, fruit or vitamin C intake biomarker. More recently it has been discovered that ascorbate levels in urine can be used to facilitate the detection of precancerous colorectal polyps. While there are a number elaborate HPLC, MS or multi-step enzymatic "kit" methods to detect and quantify urinary ascorbate, these are time consuming and expensive. There are also a number of low-cost paper-based ascorbate detection dipsticks. However, the limits of detection and quantification accuracy for these dipsticks are not adequate for applications with human urine. To address these limitations, we have developed a fast, sensitive, single-step colorimetric assay that can be used to quantify ascorbate in urine and other biological fluids. The assay uses the tetrazolium salt, methylthiazolyldiphenyl-tetrazolium bromide (MTT), with the electron carrier phenazine methosulfate (PMS), in a chelated acidic phosphate-buffer to produce a vivid purple color in the presence of ascorbate. Confirmation of the performance of the assay and of its standard curve in human urine was also done using independent LC-MS/MS and NMR analyses. The lower limit of detection of the ascorbate dipstick assay described here was found to be 3.2 µM. The paper dipsticks are stable over a wide range of temperatures and can be stored for up to 150-days.

Preparation and characterization of a highly soluble Aß1-42 peptide variant.

Alzheimer's disease (AD) is a progressive neurological disease marked by the accumulation and deposition of misfolded amyloid beta or Abeta (Aß) peptide. Two species of Aß peptides are found in amyloid plaques, Aß1-40 and Aß1-42, with the latter being the more amyloidogenic of the two. Understanding how and why Aß peptides misfold, oligomerize and form amyloid plaques requires a detailed understanding of their structure and dynamics. The poor solubility and strong aggregation tendencies of Aß1-42 has made the isolation and characterization of its different structural isoforms (monomer, dimer, oligomer, amyloid) exceedingly difficult. Furthermore, while synthetic Aß1-42 peptides (Aß42syn) are readily available, the cost of isotopically labeled peptide is substantial, making their characterization by NMR spectroscopy cost prohibitive. Here we describe the design, cloning, high-level production, isotopic labeling and biophysical characterization of a modified (solubility-tagged) Aß1-42 variant that exhibits excellent water solubility and shares similar aggregation properties as wildtype Aß1-42. Specifically, we attached six lysines (6K) to the C-terminus of native Aß1-42 to create a more soluble, monomeric form of Aß1-42 called Aß42C6K. A gene for the Aß42C6K was designed, synthesized and cloned into Escherichia coli (E. coli) and the peptide was expressed at milligram levels. The Aß42C6K peptide was characterized using circular dichroism (CD), NMR, electron microscopy and thioflavin T fluorescence. Its ability to form stable monomers, oligomers and fibrils under different conditions was assessed. Our results indicate that Aß42C6K stays monomeric at high concentrations (unlike Aß1-42) and can be induced to oligomerize and form fibrils like Aß1-42. Our novel construct could be used to explore the structure and dynamics of Aß1-42 as well as the interaction of ligands with Aß1-42 via NMR.

Chemical Composition of Commercial Cannabis.

Cannabis is widely used for medicinal and recreational purposes. As a result, there is increased interest in its chemical components and their physiological effects. However, current information on cannabis chemistry is often outdated or scattered across many books and journals. To address this issue, we used modern metabolomics techniques and modern bioinformatics techniques to compile a comprehensive list of >6000 chemical constituents in commercial cannabis. The metabolomics methods included a combination of high- and low-resolution liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and inductively coupled plasma-MS. The bioinformatics methods included computer-aided text mining and computational genome-scale metabolic inference. This information, along with detailed compound descriptions, physicochemical data, known physiological effects, protein targets, and referential compound spectra, has been made available through a publicly accessible database called the Cannabis Compound Database (https://cannabisdatabase.ca). Such a centralized, open-access resource should prove to be quite useful for the cannabis community.

Identification of urinary biomarkers of colorectal cancer: Towards the development of a colorectal screening test in limited resource settings.

BACKGROUND: African colorectal cancer (CRC) rates are rising rapidly. A low-cost CRC screening approach is needed to identify CRC from non-CRC patients who should be sent for colonoscopy (a scarcity in Africa). OBJECTIVE: To identify urinary metabolite biomarkers that, combined with easy-to-measure clinical variables, would identify patients that should be further screened for CRC by colonoscopy. Ideal metabolites would be water-soluble and easily translated into a sensitive, low-cost point-of-care (POC) test. METHODS: Liquid-chromatography mass spectrometry (LC-MS/MS) was used to quantify 142 metabolites in spot urine samples from 514 Nigerian CRC patients and healthy controls. Metabolite concentration data and clinical characteristics were used to determine optimal sets of biomarkers for identifying CRC from non-CRC subjects. RESULTS: Our statistical analysis identified N1, N12-diacetylspermine, hippurate, p-hydroxyhippurate, and glutamate as the best metabolites to discriminate CRC patients via POC screening. Logistic regression modeling using these metabolites plus clinical data achieved an area under the receiver-operator characteristic (AUCs) curves of 89.2% for the discovery set, and 89.7% for a separate validation set. CONCLUSIONS: Effective urinary biomarkers for CRC screening do exist. These results could be transferred into a simple, POC urinary test for screening CRC patients in Africa.

Applications of Metabolomics to Precision Nutrition.

BACKGROUND: For thousands of years, disabilities due to nutrient deficiencies have plagued humanity. Rickets, scurvy, anemia, stunted growth, blindness, and mental handicaps due to nutrient deficiencies affected up to 1/10 of the world's population prior to 1900. The discovery of essential amino acids, vitamins, and minerals, in the early 1900s, led to a fundamental change in our understanding of food and a revolution in human health. Widespread vitamin and mineral supplementation, the development of recommended dietary allowances, and the implementation of food labeling and testing along with significant improvements in food production and food quality have meant that nutrient-related disorders have almost vanished in the developed world. The success of nutritional science in preventing disease at a population-wide level is one of the great scientific triumphs of the 20th century. The challenge for nutritional science in the 21st century is to understand how to use nutrients and other food constituents to enhance human health or prevent disease at a more personal level. This is the primary goal of precision nutrition. SUMMARY: Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases in that individual. Metabolomics is vital to nearly every aspect of precision nutrition. It can be used to comprehensively characterize the thousands of chemicals in foods, to identify food byproducts in human biofluids or tissues, to characterize nutrient deficiencies or excesses, to monitor biochemical responses to dietary interventions, to track long-term or short-term dietary habits, and to guide the development of nutritional therapies. In this review, we will describe how metabolomics has been used to advance the field of precision nutrition by providing some notable examples or use cases. First, we will describe how metabolomics helped launch the field of precision nutrition through the diagnosis and dietary therapy of individuals with inborn errors of metabolism. Next, we will describe how metabolomics is being used to comprehensively characterize the full chemical complexity of many key foods, and how this is revealing much more about nutrients than ever imagined. Third, we will describe how metabolomics is being used to identify food consumption biomarkers and how this opens the door to a more objective and quantitative assessments of an individual's diet and their response to certain foods. Finally, we will describe how metabolomics is being coupled with other omics technologies to develop custom diets and lifestyle interventions that are leading to positive health benefits. Key Message: Metabolomics is vital to the advancement of nutritional science and in making the dream of precision nutrition a reality.

Cloning and high-level expression of monomeric human superoxide dismutase 1 (SOD1) and its interaction with pyrimidine analogs.

Superoxide dismutase 1 (SOD1) is known to be involved in the pathogenesis of Amyotrophic Lateral Sclerosis (ALS) and is therefore considered to be an important ALS drug target. Identifying potential drug leads that bind to SOD1 and characterizing their interactions by nuclear magnetic resonance (NMR) spectroscopy is complicated by the fact that SOD1 is a homodimer. Creating a monomeric version of SOD1 could alleviate these issues. A specially designed monomeric form of human superoxide dismutase (T2M4SOD1) was cloned into E. coli and its expression significantly enhanced using a number of novel DNA sequence, leader peptide and growth condition optimizations. Uniformly 15N-labeled T2M4SOD1 was prepared from minimal media using 15NH4Cl as the 15N source. The T2M4SOD1 monomer (both 15N labeled and unlabeled) was correctly folded as confirmed by 1H-NMR spectroscopy and active as confirmed by an in-gel enzymatic assay. To demonstrate the utility of this new SOD1 expression system for NMR-based drug screening, eight pyrimidine compounds were tested for binding to T2M4SOD1 by monitoring changes in their 1H NMR and/or 19F-NMR spectra. Weak binding to 5-fluorouridine (FUrd) was observed via line broadening, but very minimal spectral changes were seen with uridine, 5-bromouridine or trifluridine. On the other hand, 1H-NMR spectra of T2M4SOD1 with uracil or three halogenated derivatives of uracil changed dramatically suggesting that the pyrimidine moiety is the crucial binding component of FUrd. Interestingly, no change in tryptophan 32 (Trp32), the putative receptor for FUrd, was detected in the 15N-NMR spectra of 15N-T2M4SOD1 when mixed with these uracil analogs. Molecular docking and molecular dynamic (MD) studies indicate that interaction with Trp32 of SOD1 is predicted to be weak and that there was hydrogen bonding with the nearby aspartate (Asp96), potentiating the Trp32-uracil interaction. These studies demonstrate that monomeric T2M4SOD1 can be readily used to explore small molecule interactions via NMR.

Role of polysaccharide and lipid in lipopolysaccharide induced prion protein conversion.

Conversion of native cellular prion protein (PrPc) from an α-helical structure to a toxic and infectious ß-sheet structure (PrPSc) is a critical step in the development of prion disease. There are some indications that the formation of PrPSc is preceded by a ß-sheet rich PrP (PrPß) form which is non-infectious, but is an intermediate in the formation of infectious PrPSc. Furthermore the presence of lipid cofactors is thought to be critical in the formation of both intermediate-PrPß and lethal, infectious PrPSc. We previously discovered that the endotoxin, lipopolysaccharide (LPS), interacts with recombinant PrPc and induces rapid conformational change to a ß-sheet rich structure. This LPS induced PrPß structure exhibits PrPSc-like features including proteinase K (PK) resistance and the capacity to form large oligomers and rod-like fibrils. LPS is a large, complex molecule with lipid, polysaccharide, 2-keto-3-deoxyoctonate (Kdo) and glucosamine components. To learn more about which LPS chemical constituents are critical for binding PrPc and inducing ß-sheet conversion we systematically investigated which chemical components of LPS either bind or induce PrP conversion to PrPß. We analyzed this PrP conversion using resolution enhanced native acidic gel electrophoresis (RENAGE), tryptophan fluorescence, circular dichroism, electron microscopy and PK resistance. Our results indicate that a minimal version of LPS (called detoxified and partially de-acylated LPS or dLPS) containing a portion of the polysaccharide and a portion of the lipid component is sufficient for PrP conversion. Lipid components, alone, and saccharide components, alone, are insufficient for conversion.