Molecular Networking as a Dereplication Strategy

The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.metabolomics | mass spectrometry | fragmentation | bioinformatics | topic modeling M ass spectrometry (MS)-based metabolomics aims to capture the entire small-molecule composition of biological systems. Analysis of MS metabolomics data are challenging as many molecules cannot be identified from their mass (e.g., isobaric molecules, and isomers) (1-3). Separation by liquid chromatography before MS (LC-MS) can add discriminatory information but does not solve the problem as isomers can exhibit similar chromatographic behavior, and chromatographic retention time is currently unpredictable.Fragmentation spectra have been used to partially overcome this problem (4-6). Most tools compare individual fragmentation spectra to reference spectra (5, 7) stored in public databases, for example, MassBank (8) or Human Metabolome Database (9), and are thus constrained by the limited number of reference spectra (10-12). Poor identification coverage can result in poor biochemical insight. We propose a method that analyzes all acquired fragmentation spectra to expose underlying biochemistry without relying on metabolite identification, inspired by machine-learning techniques developed initially for text processing (13).The paucity of techniques that share information across fragmentation spectra can be explained by the complexity of fragmentation data (14). One example, "Molecular Networking," clusters MS1 peaks by th...

show abstract

“…The output of Molecular Networking (15,29) is shown on the Right of Fig. 4 (described in SI Appendix, section S2.9).…”

Section: Resultsmentioning

confidence: 99%

“…One example, "Molecular Networking," clusters MS1 peaks by their MS2 spectral similarity such that one structurally annotated metabolite in a cluster facilitates structural annotation of its neighbors (15,16). However, spectral features causing the clustering must be extracted manually, and only MS2 spectra with high overall spectral similarity are grouped.…”

mentioning

confidence: 99%

Topic modeling for untargeted substructure exploration in metabolomics

Hooft

Wandy

Barrett

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

281

259

View full text Add to dashboard Cite

show abstract

“…for genome mining for new RiPPs because of the direct link between final structure and the gene-encoded precursor peptide, the method in principle can also be used for structural analysis of other compounds such as nonribosomal peptides, and is complementary to other tandem MS-based methods that aim to ultimately determine structures in high throughput, including blind search (38), network analysis (39)(40)(41), and fragmentation tree construction (42,43). Moreover, HSEE greatly facilitates investigations of RiPP biosynthetic mechanisms, as illustrated by several examples in this study.…”

Section: Resultsmentioning

confidence: 99%

Structural investigation of ribosomally synthesized natural products by hypothetical structure enumeration and evaluation using tandem MS

Zhang

Ortega

Shi

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Ribosomally synthesized and posttranslationally modified peptides (RiPPs) are a growing class of natural products that are found in all domains of life. These compounds possess vast structural diversity and have a wide range of biological activities, promising a fertile ground for exploring novel natural products. One challenging aspect of RiPP research is the difficulty of structure determination due to their architectural complexity. We here describe a method for automated structural characterization of RiPPs by tandem mass spectrometry. This method is based on the combined analysis of multiple mass spectra and evaluation of a collection of hypothetical structures predicted based on the biosynthetic gene cluster and molecular weight. We show that this method is effective in structural characterization of complex RiPPs, including lanthipeptides, glycopeptides, and azole-containing peptides. Using this method, we have determined the structure of a previously structurally uncharacterized lanthipeptide, prochlorosin 1.2, and investigated the order of the posttranslational modifications in three biosynthetic systems.dehydration | genome mining | lantibiotics | directionality R ibosomally synthesized and posttranslationally modified peptides (RiPPs) are a major class of natural products as revealed by the genome-sequencing efforts of the past decade (1). RiPPs are biosynthesized from genetically encoded and ribosomally produced precursor peptides, which typically consist of a core peptide that is transformed to the final product and an N-terminal extension called the leader peptide that is usually important for recognition by the posttranslational modification (PTM) enzymes (1). Because of the highly diverse PTMs, these compounds possess vast structural diversity and have a wide range of biological activities, thus representing a fertile ground for exploration. Furthermore, the ribosomal origin of RiPPs makes them particularly well suited for genome mining efforts. By using genome mining to explicitly avoid species harboring biosynthetic gene clusters identical to those that produce known compounds, a combination of strain prioritization and mass spectrometry (MS)-based analysis offers a new route to discovering natural products that can overcome the burden of rediscovery that has increasingly hampered discovery efforts (2, 3). One challenging aspect of high-throughput genome mining for new natural products is the difficulty to determine their molecular structures in high throughput. We present here a method that allows automated RiPP structure elucidation.In contrast to nonribosomal peptides that have an average molecular weight of less than 1,000 Da, as documented in the NORINE database (4), RiPPs in many cases have molecular weights larger than 2,500 Da. Molecules of this size are difficult to rapidly analyze by NMR spectroscopy, rendering MS the most convenient tool for RiPP structural characterization. Even when the precursor peptide sequences are known and the types of PTMs can be predicted based on the sequen...

show abstract

“…The metabolomic information, analyzed by molecular networking (Frank et al, 2008;Watrous et al, 2012;Yang et al, 2013), revealed a doubly charged ion at m/z 1107.053 in the wild type. The same peak was absent not only from the conprimycin mutant but also from the ectoine mutant (Supplementary Figure S6a).…”

Section: Disease Suppressiveness and Rhizosphere Colonization By Strementioning

confidence: 99%

Microbial and biochemical basis of a Fusarium wilt-suppressive soil

et al. 2015

View full text Add to dashboard Cite

Crops lack genetic resistance to most necrotrophic pathogens. To compensate for this disadvantage, plants recruit antagonistic members of the soil microbiome to defend their roots against pathogens and other pests. The best examples of this microbially based defense of roots are observed in disease-suppressive soils in which suppressiveness is induced by continuously growing crops that are susceptible to a pathogen, but the molecular basis of most is poorly understood. Here we report the microbial characterization of a Korean soil with specific suppressiveness to Fusarium wilt of strawberry. In this soil, an attack on strawberry roots by Fusarium oxysporum results in a response by microbial defenders, of which members of the Actinobacteria appear to have a key role. We also identify Streptomyces genes responsible for the ribosomal synthesis of a novel heat-stable antifungal thiopeptide antibiotic inhibitory to F. oxysporum and the antibiotic’s mode of action against fungal cell wall biosynthesis. Both classical- and community-oriented approaches were required to dissect this suppressive soil from the field to the molecular level, and the results highlight the role of natural antibiotics as weapons in the microbial warfare in the rhizosphere that is integral to plant health, vigor and development.

show abstract

Molecular Networking as a Dereplication Strategy

Cited by 486 publications

References 60 publications

Topic modeling for untargeted substructure exploration in metabolomics

Topic modeling for untargeted substructure exploration in metabolomics

Structural investigation of ribosomally synthesized natural products by hypothetical structure enumeration and evaluation using tandem MS

Microbial and biochemical basis of a Fusarium wilt-suppressive soil

Contact Info

Product

Resources

About