Exposomic Biomonitoring of Polyphenols by Non-Targeted Analysis and Suspect Screening

Oesterle, Ian; Pristner, Manuel; Berger, Sabrina; Wang, Mingxun; Hernandes, Vinicius Verri; Rompel, Annette; Warth, Benedikt

doi:10.1021/acs.analchem.3c01393

“…We illustrate specXplore by applying it on real LC–MS/MS untargeted metabolomics data from two experiments: (a) wheat plants LC–MS/MS data , and (b) urine metabolome LC–MS/MS from a polyphenol exposome study . In both cases, the approach and illustrations are similar.…”

Section: Resultsmentioning

confidence: 99%

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Mildau,

Ehlers,

Oesterle

et al. 2024

Anal. Chem.

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Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive Python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two-dimensional t-distributed stochastic neighbor embedding embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of state-of-the-art ms2deepscore pairwise spectral similarities as a quantitative backbone while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part of mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.

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“…We illustrate specXplore by applying it on real LC-MS/MS untargeted metabolomics data from two experiments: a) wheat plants LC-MS/MS data [31,32], and b) urine metabolome LC-MS/MS from a polyphenol exposome study [33]. In both cases the approach and illustrations are similar.…”

Section: Resultsmentioning

confidence: 99%

Tailored mass spectral data exploration using the specXplore interactive dashboard

Mildau,

Ehlers,

Oesterle

et al. 2023

Preprint

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Untargeted metabolomics promises comprehensive characterization of small molecules in biological samples. However, the field is hampered by low annotation rates and abstract spectral data. Despite recent advances in computational metabolomics, manual annotations and manual confirmation of in-silico annotations remain important in the field. Here, exploratory data analysis methods for mass spectral data provide overviews, prioritization, and structural hypothesis starting points to researchers facing large quantities of spectral data. In this research, we propose a fluid means of dealing with mass spectral data using specXplore, an interactive python dashboard providing interactive and complementary visualizations facilitating mass spectral similarity matrix exploration. Specifically, specXplore provides a two dimensional t-SNE embedding as a jumping board for local connectivity exploration using complementary interactive visualizations in the form of partial network drawings, similarity heatmaps, and fragmentation overview maps. SpecXplore makes use of of state of the art ms2deepscore pairwise spectral similarities as a quantitative backbone, while allowing fast changes of threshold and connectivity limitation settings, providing flexibility in adjusting settings to suit the localized node environment being explored. We believe that specXplore can become an integral part in mass spectral data exploration efforts and assist users in the generation of structural hypotheses for compounds of interest.Technical TermsA network is a collection of connected features. In our case, a network consists of MS/MS spectral features connected provided their spectral similarity is high. Networks are represented using node-link-diagrams.Node-link diagram -a term commonly used to refer to the graphical representation of a network via nodes and links (i.e. edges). In this paper, we use node-link diagram and network-view interchangeably.A node is a feature in a network that can be connected to other features via edges. An alternative term for node is vertex.An edge is a connection between two nodes. Other terms for edges are links or vertices.Network layout refers to the spatial arrangement of nodes and edges on an usually two dimensional plotting surface. Network layout is also sometimes referred to as embedding. This term is avoided in this paper to avoid confusion with embedding in the machine learning sense.Given a networkG(V, E), whereVdenotes its nodes andEits (weighted) edges, we define its topology as the relationships between individual (groups of) nodes and edges or the network as a whole, irrespective of the network’s layout.Molecular Networking (MN) is an exploratory data analysis technique merging spectral similarity-based topological clustering and visualization as node-link diagrams.The plain English words group/grouping are wherever appropriate to avoid jargon terms such as clustering (as in k-medoid or k-means clustering), embedding (as in projection of groups of features into a close-by lower dimensional space), or molecular families. The latter are groups of spectral data features clustered and visualized as network-views via traditional MN or feature based molecular networking (FBMN). Molecular families, usually represent smaller, disconnected networks that are part of a larger dataset. When we refer to this disconnected nature, we use the phrasing disjoint sub-network for emphasis.

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“…In brief, features identified with authentic reference standards were labeled as Level 1 and features annotated with spectral libraries as Level 2a. Level 3 was then split in a similar manner as described in Oesterle et al (2023), and features annotated by in silico fragmentation were labeled as Level 3a, and features putatively annotated by their MS 1 with the two databases as Level 3b. The chemical classes of the features were also determined using the ChemRICH MeSH prediction tool (Barupal and Fiehn 2017), the classes listed in MS-Finder and MS-Dial, and the classes listed in the entries of the two online databases (PhytoHub and Exposome-Explorer).…”

Section: Methodsmentioning

confidence: 99%

“…However, to our knowledge, there is limited data on the longitudinal exposomic/metabolomic profiles of neonates and infants via breast milk and matching stool samples, which is necessary for making correlations with the gut microbiome. Thus, the objectives of this study were to apply a recently developed untargeted exposomic biomonitoring workflow (Oesterle et al 2023) with SWATH DIA on breast milk and matching stool collected longitudinally from Nigerian mother-infant pairs. This workflow allowed us to 1) elucidate exposure profiles of polyphenols and other potential beneficial or toxic xenobiotics present in the samples, 2) investigate changes and correlations of xenobiotics as complementary foods are introduced in the diet, and 3) correlate the xenobiotics detected in the infants' stool to the gut microbiome.…”

Section: Several Studiesmentioning

confidence: 99%

“…After quality control, 4347 and 6905 features remained in breast milk and stool, respectively. A suspect screening workflow optimized for polyphenols but also capable of covering many other xenobiotics and endogenous metabolites (Oesterle et al 2023) was then used to extract features of interest in the biological matrices. This workflow involved a suspect list from two databases: PhytoHub, an online database containing 2268 phytochemicals, mainly polyphenols (Giacomoni et al 2017), and Exposome-Explorer, a database containing 1262 chemicals known to be biomarkers for exposure to environmental and lifestyle factors such as diet, pollutants, or contaminants (Neveu et al 2020).…”

Section: Suspect Screening Of Breast Milk and Stool Samplesmentioning

confidence: 99%

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Insights into the early-life chemical exposome of Nigerian infants and potential correlations with the developing gut microbiome

Oesterle,

Ayeni,

Ezekiel

et al. 2023

Preprint

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Early-life exposure to natural and/or synthetic chemicals can impact acute and chronic health conditions. Here, a suspect screening workflow anchored on high-resolution mass spectrometry was applied to elucidate xenobiotics in breast milk and matching stool samples collected from Nigerian mother-infant pairs (n = 11) at three-time points. Potential correlations between xenobiotics and the gut microbiome, as determined by 16S rRNA gene amplicon sequencing, were subsequently explored. Overall, 12,192 and 16,461 features were acquired in the breast milk and stool samples, respectively. Following quality control and suspect screening, 562 and 864 features remained, respectively, with 149 of these features present in both matrices. Taking advantage of 242 authentic reference standards measured for confirmatory purposes of both, potentially beneficial and adverse xenobiotics, 34 features in breast milk and 68 features in stool were unambiguously identified and subsequently semi-quantified. Moreover, 51 and 78 features were annotated with spectral library matching, as well as 416 and 652 within silicofragmentation in breast milk and stool, respectively. Despite that the workflow was originally optimized for polyphenols, a diverse range of other chemical classes were simultaneously identified including mycotoxins, endocrine-disrupting chemicals (EDCs), antibiotics, plasticizers, perfluorinated alkylated substances, and pesticides. Spearman rank correlation of the identified features revealed significant correlations between chemicals of the same classification such as polyphenols. One-way ANOVA and differential abundance analysis of the data obtained from stool samples revealed that features of plant-based origin were elevated when complementary foods were introduced to the infants’ diets. Features in the stool deemed significant by ANOVA, such as tricetin, positively correlated with the genusBlautia. Moreover, vulgaxanthin negatively correlated withEscherichia-Shigella. Despite the limited sample size, this exploratory study provides high-quality exposure data of matched biospecimens obtained from mother-infant pairs in sub-Saharan Africa, and showed potential correlations between the chemical exposome and the gut microbiome.Graphical abstract

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Exposomic Biomonitoring of Polyphenols by Non-Targeted Analysis and Suspect Screening

Cited by 10 publications

References 42 publications

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Tailored Mass Spectral Data Exploration Using the SpecXplore Interactive Dashboard

Tailored mass spectral data exploration using the specXplore interactive dashboard

Insights into the early-life chemical exposome of Nigerian infants and potential correlations with the developing gut microbiome

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