Shipei Xing

In our next public ATeam group presentation, Shipei Xing from Pieter… | Theodore Alexandrov In our next public ATeam group presentation, Shipei Xing from Pieter Dorrestein lab at UC San Diego will present on how one can annotate metabolites in imaging MS beyond molecular formula. This is a ...

I will be presenting our new insights into imaging MS data annotation. Welcome to join and discuss!

A study using an n=1 experiment with single standards at 0V CID reported that 70% of detected ions were in-source fragments (ISFs). www.nature.com/articles/s42... This finding was extrapolated to suggest that ISFs affect all metabolomics experiments to this degree. A counterpoint. rdcu.be/ebFwc 1/n

It’s not every day one gets to publish an article with NASA astronauts. This journey started in 2019 and withe the goal to understand the microbial and chemical make-up of the space station. We had lots of experience with analysis from swabs on earth.

A guide to reverse metabolomics—a framework for big data discovery strategy Nature Protocols - In this reverse metabolomics protocol, a tandem mass spectrometry spectrum is used as a search term to probe public metabolomic data. Analysis of the metadata connected with...

Reverse metabolomics made these two papers a reality. www.nature.com/articles/s41... and www.cell.com/cell/fulltex... but hard for others to do similar analysis as the data science is at a high level. So @vincentlamoureux.bsky.social wanted to enable others with this powerful strategy rdcu.be/ebIF2

A Systematic Approach to Discover New Natural Product Scaffolds Using Database-Derived Relative Mass Spectral Defects and Molecular Networking Rapid advances in mass spectrometry (MS) data analysis have accelerated the identification of natural products from complex mixtures such as natural product extracts. However, limitations in MS data in metabolite libraries and dereplication strategies are still lacking for assigning structures to known compounds and searching for unidentified compounds. To overcome these limitations, we present an approach that combines molecular networking with MS database-derived mass defect analysis to preferentially discover new compounds with high structural novelty in the initial stage of a discovery workflow. Specifically, unknown metabolites or clusters generated from molecular networking are assigned to a compound class based on their relative mass defects (RMDs) calculated using open-source databases. If ancillary data such as ultraviolet and MS/MS spectra of the unknown clusters are incongruent with the RMD-assigned compound class, metabolites are considered to have a new skeleton that exhibits a large difference in RMD value due to structural changes. Here, we applied this RMD-assisted method to a desert-derived bacterial strain library and validated it through the discovery of brasiliencin A (1), a new 18-membered macrolide from Nocardia brasiliensis. A putative biosynthetic pathway of brasiliencin A was proposed through whole-genome sequence analysis, and an additional 29 analogs were detected using absolute mass defect filtering (AMDF) based on plausible biosynthetic products. This led to the isolation of three additional macrolides, brasiliencins B–D (2–4). The structures of the brasiliencins (1–4) were fully elucidated through spectroscopic data analysis and quantum chemical calculations including ROE distance and 13C NMR chemical shift calculations, and experimental and theoretical electronic circular dichroism (ECD). Brasiliencin A showed strong activity against Mycobacterium smegmatis and Streptococcus australis (MIC = 31.3 nM and 7.81 μM, respectively) compared to brasiliencin B (MIC = 1000 nM and 62.5 μM, respectively) that differs at a single stereocenter.

I really love the creative uses of molecular networking. Here they combined with mass defect analysis to prioritize discovery of new molecules. pubs.acs.org/doi/10.1021/...

Two-layer homolog network approach for PFAS nontarget screening and retrospective data mining - Nature Communications PFAS are a wide class of ubiquitous chemically persistent environmental pollutants. Here the authors combine nontarget screening with a two-layer network approach to identify large numbers of PFAS fro...

Here the authors used molecular networking to discover PFASs and reanalyze public data sets to show they are observed in data from seven countries. Such a nice reuse of public data - but also alarming as they are seen in data going back to 2005. www.nature.com/articles/s41...

Times are changing but order matters: Transferable prediction of small molecule liquid chromatography retention times

Authors: Fleming Kretschmer, Eva-Maria Harrieder, Michael Witting, Sebastian Böcker
DOI: 10.26434/chemrxiv-2024-wd5j8

This is only achieved due to the creativity of people that we get to work and collaborate with. Thanks to all.

Empirically establishing drug exposure records directly from untargeted metabolomics data Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based...

Ok a third bleetorial or skeetorial of another preprint. www.biorxiv.org/content/10.1... in this case Nina, our exposome expert, Kine, our expert pharmacist, Corinna, the MS/MS guru, wanted to identify medication exposures from untargeted metabolomics data. Why? - aren’t there good medical records?

So glad that we are one step forward to reuse public MS1 data, for both LC-MS and MS imaging. This would not be possible without teamwork. @pieterdorrestein.bsky.social @vincentlamoureux.bsky.social @yelabiead.bsky.social