Non-targeted chemical analysis of consumer botanical products labeled as blue cohosh (Caulophyllum thalictroides), goldenseal (Hydrastis canadensis), or yohimbe bark (Pausinystalia yohimbe) by NMR and MS.

Consumers have unprecedented access to botanical dietary supplements through online retailers, making it difficult to ensure product quality and authenticity. Therefore, methods to survey and compare chemical compositions across botanical products are needed. Nuclear magnetic resonance (NMR) spectroscopy and non-targeted mass spectrometry (MS) were used to chemically analyze commercial products labeled as containing one of three botanicals: blue cohosh, goldenseal, and yohimbe bark. Aqueous and organic phase extracts were prepared and analyzed in tandem with NMR followed by MS. We processed the non-targeted data using multivariate statistics to analyze the compositional similarity across extracts. In each case, there were several product outliers that were identified using principal component analysis (PCA). Evaluation of select known constituents proved useful to contextualize PCA subgroups, which in some cases supported or refuted product authenticity. The NMR and MS data reached similar conclusions independently but were also complementary.

Reverse metabolomics for the discovery of chemical structures from humans.

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

Pubertal Girls With Overweight/Obesity Have Higher Androgen Levels-Can Metabolomics Tell us Why?

CONTEXT: Pubertal girls with higher total body fat (TBF) demonstrate higher androgen levels. The cause of this association is unknown but is hypothesized to relate to insulin resistance. OBJECTIVE: This work aimed to investigate the association between higher TBF and higher androgens in pubertal girls using untargeted metabolomics. METHODS: Serum androgens were determined using a quantitative mass spectrometry (MS)-based assay. Metabolomic samples were analyzed using liquid chromatography high-resolution MS. Associations between TBF or body mass index (BMI) z score (exposure) and metabolomic features (outcome) and between metabolomic features (exposure) and serum hormones (outcome) were examined using gaussian generalized estimating equation models with the outcome lagged by one study visit. Benjamini-Hochberg false discovery rate (FDR) adjusted P values were calculated to account for multiple testing. RaMP-DB (relational database of metabolomic pathways) was used to conduct enriched pathway analyses among features nominally associated with body composition or hormones. RESULTS: Sixty-six pubertal, premenarchal girls (aged 10.9 ± 1.39 SD years; 60% White, 24% Black, 16% other; 63% normal weight, 37% overweight/obese) contributed an average of 2.29 blood samples. BMI and TBF were negatively associated with most features including raffinose (a plant trisaccharide) and several bile acids. For BMI, RaMP-DB identified many enriched pathways related to bile acids. Androstenedione also showed strong negative associations with raffinose and bile acids. CONCLUSION: Metabolomic analyses of samples from pubertal girls did not identify an insulin resistance signature to explain the association between higher TBF and androgens. Instead, we identified potential novel signaling pathways that may involve raffinose or bile acid action at the adrenal gland.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Profiling whole-tissue metabolic reprogramming during cutaneous poxvirus infection and clearance.

IMPORTANCE: Human poxvirus infections have caused significant public health burdens both historically and recently during the unprecedented global Mpox virus outbreak. Although vaccinia virus (VACV) infection of mice is a commonly used model to explore the anti-poxvirus immune response, little is known about the metabolic changes that occur in vivo during infection. We hypothesized that the metabolome of VACV-infected skin would reflect the increased energetic requirements of both virus-infected cells and immune cells recruited to sites of infection. Therefore, we profiled whole VACV-infected skin using untargeted mass spectrometry to define the metabolome during infection, complementing these experiments with flow cytometry and transcriptomics. We identified specific metabolites, including nucleotides, itaconic acid, and glutamine, that were differentially expressed during VACV infection. Together, this study offers insight into both virus-specific and immune-mediated metabolic pathways that could contribute to the clearance of cutaneous poxvirus infection.

Loss of CD4+ T cell-intrinsic arginase 1 accelerates Th1 response kinetics and reduces lung pathology during influenza infection.

Arginase 1 (Arg1), the enzyme catalyzing the conversion of arginine to ornithine, is a hallmark of IL-10-producing immunoregulatory M2 macrophages. However, its expression in T cells is disputed. Here, we demonstrate that induction of Arg1 expression is a key feature of lung CD4+ T cells during mouse in vivo influenza infection. Conditional ablation of Arg1 in CD4+ T cells accelerated both virus-specific T helper 1 (Th1) effector responses and its resolution, resulting in efficient viral clearance and reduced lung pathology. Using unbiased transcriptomics and metabolomics, we found that Arg1-deficiency was distinct from Arg2-deficiency and caused altered glutamine metabolism. Rebalancing this perturbed glutamine flux normalized the cellular Th1 response. CD4+ T cells from rare ARG1-deficient patients or CRISPR-Cas9-mediated ARG1-deletion in healthy donor cells phenocopied the murine cellular phenotype. Collectively, CD4+ T cell-intrinsic Arg1 functions as an unexpected rheostat regulating the kinetics of the mammalian Th1 lifecycle with implications for Th1-associated tissue pathologies.

Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability.

Feature finding is a common way to process untargeted mass spectrometry (MS) data to obtain a list of chemicals present in a sample. Most feature finding algorithms naïvely search for patterns of unique descriptors (e.g., m/z, retention time, and mobility) and provide a list of unannotated features. There is a need for solutions in processing untargeted MS data, independent of chemical or origin, to assess features based on measurement quality with the aim of improving interpretation. Here, we report the signal response evaluation as a method by which to assess the individual features observed in untargeted MS data. The basis of this method is the ubiquitous relationship between the amount and response in all MS measurements. Three different metrics with user-defined parameters can be used to assess the monotonic or linear relationship of each feature in a dilution series or multiple injection volumes. We demonstrate this approach in metabolomics data obtained from a uniform biological matrix (NIST SRM 1950) and a variable biological matrix (murine kidney tissue). The code is provided to facilitate implementation of this data processing method.

Ferroptosis-Mediated Cell Death Induced by NCX4040, The Non-Steroidal Nitric Oxide Donor, in Human Colorectal Cancer Cells: Implications in Therapy.

Our recent studies show that the treatment of human ovarian tumor cells with NCX4040 results in significant depletions of cellular glutathione, the formation of reactive oxygen/nitrogen species and cell death. NCX4040 is also cytotoxic to several human colorectal cancer (CRC) cells in vitro and in vivo. Here, we examined the ferroptosis-dependent mechanism(s) of cytotoxicity of NCX4040 in HT-29 and K-RAS mutant HCT 116 colon cell lines. Ferroptosis is characterized by the accumulation of reactive oxygen species (ROS) within the cell, leading to an iron-dependent oxidative stress-mediated cell death. However, its relevance in the mechanism of NCX4040 cytotoxicity in CRCs is not known. We found that NCX4040 generates ROS in CRC cells without any depletion of cellular GSH. Combinations of NCX4040 with erastin (ER) or RSL3 (RAS-selective lethal 3), known inducers of ferroptosis, enhanced CRC death. In contrast, ferrostatin-1, an inhibitor of ferroptosis, significantly inhibited NCX4040-induced cell death. Treatment of CRC cells with NCX4040 resulted in the induction of lipid peroxidation in a dose- and time-dependent manner. NCX4040 treatment induced several genes related to ferroptosis (e.g., CHAC1, GPX4 and NOX4) in both cell lines. Metabolomic studies also indicated significant increases in both lipid and energy metabolism following the drug treatment in HT-29 and HCT 116 cells. These observations strongly suggest that NCX4040 causes the ferroptosis-mediated cell death of CRC cells. Furthermore, combinations of NCX4040 and ER or RSL3 may contribute significantly to the treatment of CRC, including those that are difficult to treat due to the presence of Ras mutations in the clinic. NCX4040-induced ferroptosis may also be a dynamic form of cell death for the treatment of other cancers.

Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study.

BACKGROUND: Antimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription. OBJECTIVE: The aim of this paper is to identify and characterize "no prescription" online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys. METHODS: We first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants. RESULTS: A total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants. CONCLUSIONS: Our study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients.

Enhancing untargeted metabolomics using metadata-based source annotation.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

Non-invasive skin sampling detects systemically administered drugs in humans.

Clinical testing typically relies on invasive blood draws and biopsies. Alternative methods of sample collection are continually being developed to improve patient experience; swabbing the skin is one of the least invasive sampling methods possible. To show that skin swabs in combination with untargeted mass spectrometry (metabolomics) can be used for non-invasive monitoring of an oral drug, we report the kinetics and metabolism of diphenhydramine in healthy volunteers (n = 10) over the course of 24 hours in blood and three regions of the skin. Diphenhydramine and its metabolites were observed on the skin after peak plasma levels, varying by compound and skin location, and is an illustrative example of how systemically administered molecules can be detected on the skin surface. The observation of diphenhydramine directly from the skin supports the hypothesis that both parent drug and metabolites can be qualitatively measured from a simple non-invasive swab of the skin surface. The mechanism of the drug and metabolites pathway to the skin's surface remains unknown.

Mass spectrometry-based metabolomics in microbiome investigations.

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

Physicochemical properties determining drug detection in skin.

Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the receiver operating characteristic curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations (SHAP) model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2561 US Food and Drug Administration (FDA)-approved drugs, we predict that therapeutic drug classes, such as nervous system drugs, are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.

Advancements in capturing and mining mass spectrometry data are transforming natural products research.

Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.

Chemical Proportionality within Molecular Networks.

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here, we report the Chemical Proportionality (ChemProp) contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series (e.g., temporal or spatial relationships), which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of (biosynthetically) related compounds. We tested the ChemProp workflow on a ground truth data set of a defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism, and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking (GNPS) environment.

Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment.

Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

Contribution of the Gut Microbiome to Drug Disposition, Pharmacokinetic and Pharmacodynamic Variability.

The trillions of microbes that make up the gut microbiome are an important contributor to health and disease. With respect to xenobiotics, particularly orally administered compounds, the gut microbiome interacts directly with drugs to break them down into metabolic products. In addition, microbial products such as bile acids interact with nuclear receptors on host drug-metabolizing enzyme machinery, thus indirectly influencing drug disposition and pharmacokinetics. Gut microbes also influence drugs that undergo enterohepatic recycling by reversing host enzyme metabolic processes and increasing exposure to toxic metabolites as exemplified by the chemotherapy agent irinotecan and non-steroidal anti-inflammatory drugs. Recent data with immune checkpoint inhibitors demonstrate the impact of the gut microbiome on drug pharmacodynamics. We summarize the clinical importance of gut microbe interaction with digoxin, irinotecan, immune checkpoint inhibitors, levodopa, and non-steroidal anti-inflammatory drugs. Understanding the complex interactions of the gut microbiome with xenobiotics is challenging; and highly sensitive methods such as untargeted metabolomics with molecular networking along with other in silico methods and animal and human in vivo studies will uncover mechanisms and pathways. Incorporating the contribution of the gut microbiome to drug disposition, pharmacokinetics, and pharmacodynamics is vital in this era of precision medicine.