Non-targeted tandem mass spectrometry enables the visualization of organic matter chemotype shifts in coastal seawater

Abstract

Urbanization along coastlines alters marine ecosystems including contributing molecules of anthropogenic origin to the coastal dissolved organic matter (DOM) pool. A broad assessment of the nature and extent of anthropogenic impacts on coastal ecosystems is urgently needed to inform regulatory guidelines and ecosystem management. Recently, non-targeted tandem mass spectrometry approaches are gaining momentum for the analysis of global organic matter chemotypes including a wide array of natural and anthropogenic compounds. In line with these efforts, we developed a non-targeted liquid chromatography tandem mass spectrometry workflow that utilizes advanced data analysis approaches such as feature-based molecular networking and repository-scale spectrum searches. This workflow allows the scalable comparison and mapping of seawater chemotypes from large-scale spatial surveys as well as molecular family level annotation of unknown compounds. As a case study, we visualized organic matter chemotype shifts in coastal environments in northern San Diego, USA, after significant rain fall in winter 2017/2018 and highlight potential anthropogenic impacts. The observed seawater chemotype shifted significantly after a major rain event. Molecular drivers of this shift could be attributed to multiple anthropogenic compounds, including pesticides (Imazapyr and Isoxaben), cleaning products (Benzyl-tetradecyl-dimethylammonium) and chemical additives (Hexa(methoxymethyl)melamine) and potential degradation products. By expanding the search of identified xenobiotics to other public tandem mass spectrometry datasets, we further contextualized their possible origin and show their importance in other ecosystems. The mass spectrometry and data analysis pipelines applied here offer a scalable framework for future molecular mapping and monitoring of marine ecosystems, which will contribute to a deliberate assessment of how chemical pollution impacts our oceans.<br>

Illuminating the dark metabolome of Pseudo‐nitzschia–microbiome associations

“…This is most commonly done as a targeted approach, where metabolites of interest are quantified, and their different abundances can provide insights into their role in experiments or the environment. Alternatively, untargeted approaches tend to annotate tens to hundreds of metabolites based on local or public libraries and/ or assign elemental composition to thousands of metabolites (Dawson et al, 2020; Fiorini et al, 2020; Heal et al, 2019, 2021; Johnson et al, 2020, 2021; Longnecker & Kujawinski, 2020; Petras et al, 2021; Vorobev et al, 2018; Weber et al, 2020; Wienhausen et al, 2017). However, even such an approach leaves thousands of detectable compounds uncharacterized as chemical dark matter (da Silva et al, 2015), and these compounds might be crucial for microbial interactions, biogeochemical processes in the ocean or have potential as natural products in drug discovery.…”

The Core Human Fecal Metabolome

“…Public ReDU datasets used for co-analyses are available at: MSV000083559 47 (doi: 10.25345/C5C032; dataset license: CC0 1.0 Universal); MSV000082433 47, 62, 63 (dataset license: CC0 1.0 Universal); MSV000081351 47 (dataset license: CC0 1.0 Universal); MSV000083756 64 (doi: 10.25345/C53S6N; dataset license: CC0 1.0 Universal); MSV000083300 61 (doi: 10.25345/C56C86; dataset license: CC0 1.0 Universal); MSV000081492 47 (dataset license: CC0 1.0 Universal); MSV000082629 47 (dataset license: CC0 1.0 Universal); MSV000082262 (dataset license: CC0 1.0 Universal); MSV000082221 60 (dataset license: CC0 1.0 Universal); and MSV000082374 49 (dataset license: CC0 1.0 Universal).…”

Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

“…19,23−25 Additionally, current structural databases cover only a small fraction of molecular formulas encountered and typically lead to annotation rates <5%. 18,26,27 One way to obtain structure information on isomers and isobars is through collision-induced dissociation (CID; MS 2 , or multistage MS n ). 27−29 The relatively wide isolation window (∼1 Da) of mass filters applied for precursor ion selection commonly hinders the isolation and subsequent fragmentation of single exact masses, leading to mixed "chimeric" MS 2 spectra of co-fragmented precursor ions.…”

“…Although this approach sacrifices the identification of true knowns, it allows for the identification of potential structural analogues via indicative Δm's and may be especially suited when annotation rates are as low as in the case of DOM, i.e., when most compounds are unknown. 18,26,27 Despite the unknown identity of most of the molecules present in DOM, its potential sources can be constrained reasonably well. Plants produce most of the organic matter that sustains food webs in natural ecosystems.…”

“…Data-dependent and data-independent acquisition (DDA, DIA) techniques could be used to cover the whole mass range of precursor ions in DOM mass spectra in future, and are widely employed in LC-MS of complex mixtures. 16,27,97,98 For example, Ludwig et al presented a DIA scheme (SWATH-MS) that employs one precursor ion scan and 32 isolation windows of 25 Da width, covering 800 Da within 3.3 s; similar schemes are likely transferable to acquire full mass range data of directly injected DOM. 99 Kurek et al recently presented such data (m/ z 392−408), 16 Leyva et al discerned fragmentation pathways and structural families (mass range m/z 261−477).…”