A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics

Abstract

Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.Metabolites 2020, 10, 8 2 of 17 conducted using an LC/MS platform. In addition, while many tools have been developed specifically for plants, natural products, and industrial applications, these are not always applicable for human or translational studies. Therefore, this perspective article focuses on LC/MS-based clinical metabolomics and proposes a strategy to improve identification of compounds in these studies.Identifying compounds in LC/MS studies can be difficult and metabolomics researchers have developed an array of databases, spectral libraries, novel algorithms, and other tools to address the various challenges (for review, please see [8,9]). Generally speaking, compound annotation is conducted using metabolomics databases and/or libraries. Metabolomics databases are searched using mass only or mass plus formula and include descriptors such as chemical and biological information. In contrast, MS/MS spectral libraries include data corresponding to the fragmentation of precursor molecules, whereby experimentally derived spectra are compared to spectra present in the library. Spectral libraries can contain empirical or in silico derived (i.e., computer generated) spectra. Another strategy entails the use of customized databases that may include some MS/MS spectra, including those that focus on specific sample types [10,11]. For the purpose of this perspective article, we are referring to these as sample type specific databases (STSDBs).While the idea of STSDBs is not new, the full potential of STSDBs remains to be determined, in part because they currently exist as simple repositories of data. Our laboratory has recently developed a computationa...

European oak chemical diversity – from ecotypes to herbivore resistance

“…The table contains the exact mass, chemical formula and peak area of compounds if the expected adducts and isotopologues co-occurred at r 2 > 0.7. For the most abundant compounds, we confirmed the annotation by standards and therefore achieved the annotation level 1 (Sumner et al, 2007;Reisdorph et al, 2020). For other compounds, the annotation was putative and achieved using MS/MS spectra, when available, matched by METABOSCAPE to the libraries HMDB (http://www.…”

“…The table contains the exact mass, chemical formula and peak area of compounds if the expected adducts and isotopologues co‐occurred at r 2 > 0.7. For the most abundant compounds, we confirmed the annotation by standards and therefore achieved the annotation level 1 (Sumner et al ., 2007; Reisdorph et al ., 2020). For other compounds, the annotation was putative and achieved using MS/MS spectra, when available, matched by M etaboscape to the libraries HMDB (http://www.hmdb.ca/) (Wishart et al ., 2009), GNPS (Global Natural Product Social Molecular Networking) (https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp), M oNa (Mass Bank of North America), Vaniya/Fiehn Natural Products Library; Fiehn HILIC; R eSpect (http://spectra.psc.riken.jp) (Sawada et al ., 2012); LC‐MS/MS Spectra (https://mona.fiehnlab.ucdavis.edu/downloads).…”

Trichoderma‐Derived Pentapeptides from the Infected Nest Mycobiome of the Subterranean Termite Coptotermes testaceus

“…[54] We also used the freely accessible AntiBase [40] (AB) 2017 and the Dictionary of Natural Products (http://dnp.chemnetbase.com/faces/chemical/Chemi-calSearch.xhtml; accessed Nov 16, 2020) for molecular formula searches as previously described, [59] which implies different degrees of confidence for the obtained annotations. [60] Cytoscape v3.8.2 was used to visualize the data as a network comprising nodes and edges, wherein each node represents a parent ion and its color reflects the sample (see caption Figure 2) from which the MS/MS file was obtained. [54,61] NMR spectroscopy: NMR spectra were recorded on a Bruker AVANCE III 600 spectrometer operating at a proton frequency of 600.05 MHz and a 13 C frequency of 150.88 MHz.…”

Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics—Standardization, Coverage, and Throughput

“…Over recent years a range of different strategies allowing us to apply this idea also in metabolomics has been proposed or implemented. 18 , 144 − 147 While their actual application is still scarce, they definitely pose a step toward increased reliability of annotations. Another point, which needs to be considered in this regard is the nature of reference spectra used for spectral matching.…”