Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Variation of solar absorptance of CNT coating with incident angle

Color poster with text, graphs, and imagesUniversity of Wisconsin--Stout Research Service

Using Accurate Mass Gas Chromatography–Mass Spectrometry with the MINE Database for Epimetabolite Annotation

Mass spectrometry-based untargeted metabolomics often detects statistically significant metabolites that cannot be readily identified. Without defined chemical structure, interpretation of the biochemical relevance is not feasible. Epimetabolites are produced from canonical metabolites by defined enzymatic reactions and may represent a large fraction of the structurally unidentified metabolome. We here present a systematic workflow for annotating unknown epimetabolites using high resolution gas chromatography–accurate mass spectrometry with multiple ionization techniques and stable isotope labeled derivatization methods. We first determine elemental formulas, which are then used to query the “metabolic in-silico expansion” database (MINE DB) to obtain possible molecular structures that are predicted by enzyme promiscuity from canonical pathways. Accurate mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER to derive the best candidates. We validated the workflow by correctly identifying 10 methylated nucleosides and 6 methylated amino acids. We then employed this strategy to annotate eight unknown compounds from cancer studies and other biological systems

Dissecting the human gut microbiome to better decipher drug liability: A once-forgotten organ takes center stage

Background: The gut microbiome is a diverse system within the gastrointestinal tract composed of trillions of microorganisms (gut microbiota), along with their genomes. Accumulated evidence has revealed the significance of the gut microbiome in human health and disease. Due to its ability to alter drug/xenobiotic pharmacokinetics and therapeutic outcomes, this once-forgotten “metabolic organ” is receiving increasing attention. In parallel with the growing microbiome-driven studies, traditional analytical techniques and technologies have also evolved, allowing researchers to gain a deeper understanding of the functional and mechanistic effects of gut microbiome. Aim of review: From a drug development perspective, microbial drug metabolism is becoming increasingly critical as new modalities (e.g., degradation peptides) with potential microbial metabolism implications emerge. The pharmaceutical industry thus has a pressing need to stay up-to-date with, and continue pursuing, research efforts investigating clinical impact of the gut microbiome on drug actions whilst integrating advances in analytical technology and gut microbiome models. Our review aims to practically address this need by comprehensively introducing the latest innovations in microbial drug metabolism research— including strengths and limitations, to aid in mechanistically dissecting the impact of the gut microbiome on drug metabolism and therapeutic impact, and to develop informed strategies to address microbiome-related drug liability and minimize clinical risk. Key scientific concepts of review: We present comprehensive mechanisms and co-contributing factors by which the gut microbiome influences drug therapeutic outcomes. We highlight in vitro, in vivo, and in silico models for elucidating the mechanistic role and clinical impact of the gut microbiome on drugs in combination with high-throughput, functionally oriented, and physiologically relevant techniques. Integrating pharmaceutical knowledge and insight, we provide practical suggestions to pharmaceutical scientists for when, why, how, and what is next in microbial studies for improved drug efficacy and safety, and ultimately, support precision medicine formulation for personalized and efficacious therapies

Tracking Polymicrobial Metabolism in Cystic Fibrosis Airways: Pseudomonas aeruginosa Metabolism and Physiology Are Influenced by Rothia mucilaginosa-Derived Metabolites.

Due to a lack of effective immune clearance, the airways of cystic fibrosis patients are colonized by polymicrobial communities. One of the most widespread and destructive opportunistic pathogens is Pseudomonas aeruginosa; however, P. aeruginosa does not colonize the airways alone. Microbes that are common in the oral cavity, such as Rothia mucilaginosa, are also present in cystic fibrosis patient sputum and have metabolic capacities different from those of P. aeruginosa Here we examine the metabolic interactions of P. aeruginosa and R. mucilaginosa using stable-isotope-assisted metabolomics. Glucose-derived 13C was incorporated into glycolysis metabolites, namely, lactate and acetate, and some amino acids in R. mucilaginosa grown aerobically and anaerobically. The amino acid glutamate was unlabeled in the R. mucilaginosa supernatant but incorporated the 13C label after P. aeruginosa was cross-fed the R. mucilaginosa supernatant in minimal medium and artificial-sputum medium. We provide evidence that P. aeruginosa utilizes R. mucilaginosa-produced metabolites as precursors for generation of primary metabolites, including glutamate.IMPORTANCEPseudomonas aeruginosa is a dominant and persistent cystic fibrosis pathogen. Although P. aeruginosa is accompanied by other microbes in the airways of cystic fibrosis patients, few cystic fibrosis studies show how P. aeruginosa is affected by the metabolism of other bacteria. Here, we demonstrate that P. aeruginosa generates primary metabolites using substrates produced by another microbe that is prevalent in the airways of cystic fibrosis patients, Rothia mucilaginosa These results indicate that P. aeruginosa may get a metabolic boost from its microbial neighbor, which might contribute to its pathogenesis in the airways of cystic fibrosis patients

Tracking Polymicrobial Metabolism in Cystic Fibrosis Airways: Pseudomonas aeruginosa Metabolism and Physiology Are Influenced by Rothia mucilaginosa-Derived Metabolites.

Due to a lack of effective immune clearance, the airways of cystic fibrosis patients are colonized by polymicrobial communities. One of the most widespread and destructive opportunistic pathogens is Pseudomonas aeruginosa; however, P. aeruginosa does not colonize the airways alone. Microbes that are common in the oral cavity, such as Rothia mucilaginosa, are also present in cystic fibrosis patient sputum and have metabolic capacities different from those of P. aeruginosa Here we examine the metabolic interactions of P. aeruginosa and R. mucilaginosa using stable-isotope-assisted metabolomics. Glucose-derived 13C was incorporated into glycolysis metabolites, namely, lactate and acetate, and some amino acids in R. mucilaginosa grown aerobically and anaerobically. The amino acid glutamate was unlabeled in the R. mucilaginosa supernatant but incorporated the 13C label after P. aeruginosa was cross-fed the R. mucilaginosa supernatant in minimal medium and artificial-sputum medium. We provide evidence that P. aeruginosa utilizes R. mucilaginosa-produced metabolites as precursors for generation of primary metabolites, including glutamate.IMPORTANCEPseudomonas aeruginosa is a dominant and persistent cystic fibrosis pathogen. Although P. aeruginosa is accompanied by other microbes in the airways of cystic fibrosis patients, few cystic fibrosis studies show how P. aeruginosa is affected by the metabolism of other bacteria. Here, we demonstrate that P. aeruginosa generates primary metabolites using substrates produced by another microbe that is prevalent in the airways of cystic fibrosis patients, Rothia mucilaginosa These results indicate that P. aeruginosa may get a metabolic boost from its microbial neighbor, which might contribute to its pathogenesis in the airways of cystic fibrosis patients

Tracking Polymicrobial Metabolism in Cystic Fibrosis Airways: Pseudomonas aeruginosa Metabolism and Physiology Are Influenced by Rothia mucilaginosa-Derived Metabolites

ABSTRACT Due to a lack of effective immune clearance, the airways of cystic fibrosis patients are colonized by polymicrobial communities. One of the most widespread and destructive opportunistic pathogens is Pseudomonas aeruginosa; however, P. aeruginosa does not colonize the airways alone. Microbes that are common in the oral cavity, such as Rothia mucilaginosa, are also present in cystic fibrosis patient sputum and have metabolic capacities different from those of P. aeruginosa. Here we examine the metabolic interactions of P. aeruginosa and R. mucilaginosa using stable-isotope-assisted metabolomics. Glucose-derived 13C was incorporated into glycolysis metabolites, namely, lactate and acetate, and some amino acids in R. mucilaginosa grown aerobically and anaerobically. The amino acid glutamate was unlabeled in the R. mucilaginosa supernatant but incorporated the 13C label after P. aeruginosa was cross-fed the R. mucilaginosa supernatant in minimal medium and artificial-sputum medium. We provide evidence that P. aeruginosa utilizes R. mucilaginosa-produced metabolites as precursors for generation of primary metabolites, including glutamate. IMPORTANCE Pseudomonas aeruginosa is a dominant and persistent cystic fibrosis pathogen. Although P. aeruginosa is accompanied by other microbes in the airways of cystic fibrosis patients, few cystic fibrosis studies show how P. aeruginosa is affected by the metabolism of other bacteria. Here, we demonstrate that P. aeruginosa generates primary metabolites using substrates produced by another microbe that is prevalent in the airways of cystic fibrosis patients, Rothia mucilaginosa. These results indicate that P. aeruginosa may get a metabolic boost from its microbial neighbor, which might contribute to its pathogenesis in the airways of cystic fibrosis patients

Identification of small molecules using accurate mass MS/MS search

Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives