Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification

Antifouling activity of Malaysian green seaweed Ulva lactuca and its isolated non-polar compound

Marine natural products especially seaweeds have gained much attention to combat biofouling. Ulva lactuca was determined for its antifouling activity and characterized the isolated non-polar metabolite involved. The methanolic crude extract (MCE) of U. lactuca was screened using crystal violet assay against biofilm-forming bacteria Pseudomonas aeruginosa and was further tested on laboratory and field tests. Then, it was fractionated and isolated using Liquid-Liquid Fractionation (LLE) and Column Chromatography (CC). The isolated compound was characterized using Liquid Chromatography-Mass Spectrometry (LC-MS), Nuclear Magnetic Resonance (NMR), and Fourier Transform-Infrared Spectroscopy (FTIR). The current study showed that the growth of biofilm produced by P. aeruginosa was inhibited by MCE at concentrations of 0.0156 mg/mL. The laboratory test indicated UL5% demonstrated a higher bacterial reduction of bacterial colonies with 1.903 × 106 CFU/mL better than blank paint. According to the field test, crude panels of UL5% were successful in reducing the settlement of fouling organisms due to less macrofouler growth compared to blank paint. The isolated compound A4 was identified as hexadecanoic acid (C16H32O2) through NMR with a molecular mass of 256 g/mol detected using LC-MS. The characterization through FTIR obtained functional groups consisting of CH3, CH2, C=O, and OH. Therefore, U. lactuca produced hexadecanoic acid as one of the promising compounds from the seaweed group as an eco-friendly antifouling agent.

Evaluating the generalizability of graph neural networks for predicting collision cross section

Ion Mobility coupled with Mass Spectrometry (IM-MS) is a promising analytical technique that enhances molecular characterization by measuring collision cross-section (CCS) values, which are indicative of the molecular size and shape. However, the effective application of CCS values in structural analysis is still constrained by the limited availability of experimental data, necessitating the development of accurate machine learning (ML) models for in silico predictions. In this study, we evaluated state-of-the-art Graph Neural Networks (GNNs), trained to predict CCS values using the largest publicly available dataset to date. Although our results confirm the high accuracy of these models within chemical spaces similar to their training environments, their performance significantly declines when applied to structurally novel regions. This discrepancy raises concerns about the reliability of in silico CCS predictions and underscores the need for releasing further publicly available CCS datasets. To mitigate this, we introduce Mol2CCS which demonstrates how generalization can be partially improved by extending models to account for additional features such as molecular fingerprints, descriptors, and the molecule types. Lastly, we also show how confidence models can support by enhancing the reliability of the CCS estimates.Scientific contributionWe have benchmarked state-of-the-art graph neural networks for predicting collision cross section. Our work highlights the accuracy of these models when trained and predicted in similar chemical spaces, but also how their accuracy drops when evaluated in structurally novel regions. Lastly, we conclude by presenting potential approaches to mitigate this issue.

Decoding the Foodome: Molecular Networks Connecting Diet and Health

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

HDPairFinder: A data processing platform for hydrogen/deuterium isotopic labeling-based nontargeted analysis of trace-level amino-containing chemicals in environmental water

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.

Integrated microbiology and metabolomic analysis reveal the improvement of rice straw silage quality by inoculation of Lactobacillus brevis

BACKGROUND

Ensiling technology holds promise for preserving and providing high-quality forage. However, the preservation of rice straw poses challenges due to its high lignocellulosic content and low water-soluble carbohydrate levels. Developing highly effective lactic acid bacteria (LAB) for rice straw silage remains a priority.

RESULTS

This study evaluated the impact of three LAB strains, Lactobacillus brevis R33 (Lac33), L. buchneri R17 (Lac17), and Leuconostoc pseudomesenteroides (Leu), on the fermentation quality of rice straw silage. Rice straw silage inoculated with Lac33 alone or in combination with other strains exhibited significantly lower neutral detergent fiber (NDF) (66.5% vs. 72.3%) and acid detergent fiber (ADF) (42.1% vs. 47%) contents, along with higher lactic acid (19.4 g/kg vs. not detected) and propionic acid (2.09 g/kg vs. 1.54 g/kg) contents compared to control silage. Bacterial community analysis revealed Lactobacillus dominance (> 80%) and suppression of unwanted Enterobacter and Clostridium. Metabolomic analysis highlighted increased carbohydrates and essential amino acids, indicating improved nutrient values in Lac33-inoculated rice straw silage and a potential explanation for Lac33 dominance.

CONCLUSIONS

This research identified a highly efficient LAB candidate for rice straw silage, advancing our comprehension of fermentation from integrated microbiology and metabolomic perspectives.

Predicting Preferences for Adduct Formation in Electrospray Ionization: The Case Study of Succinic Acid

A simple theoretical approach is developed that can be used to predict the preference of ion adduct formation (with alkali Li⁺, Na⁺, K⁺ and alkaline earth Ca²⁺, Mg²⁺ metals) in electrospray ionization mass spectrometry (ESI-MS) of succinic acid, associated with several protonation/deprotonation equilibria. The applied strategy consists of using a vacuum environment as well as both implicit and explicit solvation of reactive sites and density functional theory as the method of choice. These distinct levels of theory mimic the smooth transition between the condensed environment and free ion in the gas phase. Good correlation between the Gibbs free energies for protonation/adduct formation processes with peak observation in the obtained mass spectra provide insight into the physical basis behind adduct preference and selectivity. This signifies the relationship between microscopic interactions, ionization efficiency, and types of ions that reach the detector.

Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Fermentation of NaHCO3-treated corn germ meal by Bacillus velezensis CL-4 promotes lignocellulose degradation and nutrient utilization

Sodium bicarbonate pretreatment and solid-state fermentation (SSF) were used to maximize the nutritional value of corn germ meal (CGM) by inoculating it with Bacillus velezensis CL-4 (isolated from chicken cecal contents and capable of degrading lignocellulose). Based on genome sequencing, B. velezensis CL-4 has a 4,063,558 bp ring chromosome and 46.27% GC content. Furthermore, genes associated with degradation of lignocellulose degradation were detected. Pretreatment of CGM (PCGM) with sodium bicarbonate (optimized to 0.06 g/mL) neutralized low pH. Fermented and pretreated CGM (FPCGM) contained more crude protein (CP), soluble protein of trichloroacetic acid (TCA-SP), and total amino acids (aa) than CGM and PCGM. Degradation rates of cellulose and hemicellulose were reduced by 21.33 and 71.35%, respectively, after 48 h fermentation. Based on electron microscopy, FPCGM destroys the surface structure and adds small debris of the CGM substrate, due to lignocellulose breakdown. Furthermore, 2-oxoadipic acid and dimethyl sulfone were the most important metabolites during pretreatment. Concentrations of adenosine, cytidine, guanosine, S-methyl-5'-thioadenosine, and adenine decreased significantly after 48 h fermentation, whereas concentrations of probiotics, enzymes, and fatty acids (including palmitic, 16-hydroxypalmitic, and linoleic acids) were significantly improved after fermentation. In conclusion, the novel pretreatment of CGM provided a proof of concept for using B. velezensis CL-4 to degrade lignocellulose components, improve nutritional characteristics of CGM, and expand CGM lignocellulosic biological feed production. KEY POINTS: • Sodium bicarbonate (baking soda) can be used as an economical and green additive to pretreat corn germ meal; • Fermentation with B. velezensis degrades the cellulose and hemicellulose component of corn germ meal and improves its feed quality; • As a novel qualified presumption of safety (QPS) strain, B. velezensis should have broad potential applications in food and feed industries.

The Origin and Implications of Artifact Ions in Bioanalytical LC–MS

Liquid chromatography–mass spectrometry (LC–MS) with electrospray ionization (ESI) is a widely used bioanalytical technique with both qualitative and quantitative applications. Ions are created by electrically charging a stream of droplets from the LC system, which evaporate and leave ions that are transferred to the mass spectrometer. Ideally, these are only from the analyte, but background ions, such as metals, impurities and coeluted species, can react with analytes producing adducts, such as [M + Na]+, [M + K]+, and multimers (2M + H+, 3M + H+, and so forth). Although well known, the extent of adduct ion formation and the implications for quantitative analysis and analyte characterization by tandem MS (MS/MS) are not fully appreciated. We summarize the problem and identify areas that should be considered when developing or using electrospray LC–MS.

Spontaneous Water Radical Cation Oxidation at Double Bonds in Microdroplets

Spontaneous oxidation of compounds containing diverse X=Y moieties (e.g., sulfonamides, ketones, esters, sulfones) occurs readily in organic-solvent microdroplets. This surprising phenomenon is proposed to be driven by the generation of an intermediate species [M+H₂O]^+·: a covalent adduct of water radical cation (H₂O^+·) with the reactant molecule (M). The adduct is observed in the positive ion mass spectrum while its formation in the interfacial region of the microdroplet (i.e., at the air-droplet interface) is indicated by the strong dependence of the oxidation product formation on the spray distance (which reflects the droplet size and consequently the surface-to-volume ratio) and the solvent composition. Importantly, based on the screening of a ca. 21,000-compound library and the detailed consideration of six functional groups, the formation of a molecular adduct with the water radical cation is a significant route to ionization in positive ion mode electrospray, where it is favored in those compounds with X=Y moieties which lack basic groups. A set of model monofunctional systems was studied and in one case, benzyl benzoate, evidence was found for oxidation driven by hydroxyl radical adduct formation followed by protonation in addition to the dominant water radical cation addition process. Significant implications of molecular ionization by water radical cations for oxidation processes in atmospheric aerosols, analytical mass spectrometry and small-scale synthesis are noted.

Metabolite discovery through global annotation of untargeted metabolomics data

Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.

Chemical Elicitors Induce Rare Bioactive Secondary Metabolites in Deep-Sea Bacteria under Laboratory Conditions

Bacterial genome sequencing has revealed a vast number of novel biosynthetic gene clusters (BGC) with potential to produce bioactive natural products. However, the biosynthesis of secondary metabolites by bacteria is often silenced under laboratory conditions, limiting the controlled expression of natural products. Here we describe an integrated methodology for the construction and screening of an elicited and pre-fractionated library of marine bacteria. In this pilot study, chemical elicitors were evaluated to mimic the natural environment and to induce the expression of cryptic BGCs in deep-sea bacteria. By integrating high-resolution untargeted metabolomics with cheminformatics analyses, it was possible to visualize, mine, identify and map the chemical and biological space of the elicited bacterial metabolites. The results show that elicited bacterial metabolites correspond to ~45% of the compounds produced under laboratory conditions. In addition, the elicited chemical space is novel (~70% of the elicited compounds) or concentrated in the chemical space of drugs. Fractionation of the crude extracts further evidenced minor compounds (~90% of the collection) and the detection of biological activity. This pilot work pinpoints strategies for constructing and evaluating chemically diverse bacterial natural product libraries towards the identification of novel bacterial metabolites in natural product-based drug discovery pipelines.