Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

Matrix-assisted laser desorption/ionization time-of-flight/time-of-flight tandem mass spectrometry (negative ion mode) of French Oak lignin: A novel series of lignin and tricin derivatives attached to carbohydrate and shikimic acid moieties

RATIONALE

We report the top-down lignomic analysis of the virgin released lignin (VRL) small oligomers obtained from French Oak wood.

METHODS

We have used MALDI-TOF-MS in the negative ion mode for the analysis of the complex mixture of lignin oligomers extracted from French Oak wood. High-energy CID-TOF/TOF-MS/MS analyses were used to support the postulated precursor ion structures.

RESULTS

Twenty compounds were identified using MALDI-TOF-MS/MS of the VRL extracted from French Oak wood: seven tricin derivatives and/or flavonoids, three syringylglycerol derivatives, two syringol derivatives, two flavonolignin derivatives, and six miscellaneous compounds: luteoferol, lariciresinol isomer, 5-hydroxy guaiacyl derivative, syringyl -C₁₀ H₁₀ O₂ dimer, trihydroxy benzaldehyde derivative, and aryl tetralin lignan derivative. Most of the identified compounds were in the form of carbohydrate and/or shikimic acid complexes.

CONCLUSIONS

The analysis of this complex mixture led to the identification of a series of lignin dimers, novel lignin-carbohydrate complexes (LCC), and unique tricin derivatives linked to different types of carbohydrates and shikimic acid moieties. This finding supports the presence of lignin-carbohydrate complexes in the isolated VRL. These analyses also showed that French Oak lignin is abundant in syringol moieties present in the lignin syringyl units or tricin derivatives. Moreover, the identification of some lignin-carbohydrate and/or flavonoid-shikimic acid complexes could provide new insight into the relationship between the biosynthesis of lignin and tricin.

MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data

Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.

Assessment of substrate biodegradability improvement in anaerobic Co-digestion using a chemometrics-based metabolomic approach

Anaerobic co-digestion (AcoD) can increase methane production of anaerobic digesters in plants treating wastewater sludge by improving the nutrient balance needed for the microorganisms to grow in the digesters, resulting in a faster process stabilization. Substrate mixture proportions are usually optimized in terms of biogas production, while the metabolic biodegradability of the whole mixture is neglected in this optimisation. In this aim, we developed a strategy to assess AcoD using metabolomics data. This strategy was explored in two different systems. Specifically, we investigated the co-digestion of wastewater sludge with different proportions of either grass or fish waste using untargeted High Performance Liquid Chromatography coupled to Mass Spectrometry (HPLC-MS) metabolomics and chemometrics methods. The analysis of these data revealed that adding grass waste did not improve the metabolic biodegradability of wastewater sludge. Conversely, a synergistic effect in the metabolic biodegradability was observed when fish waste was used, this effect being the highest for 25% of fish waste. In conclusion, metabolomics can be regarded as a promising tool both for characterizing the biochemical processes occurring during anaerobic digestion, and for providing a better understanding of the anaerobic digestion processes.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

VenoMS-A Website for the Low Molecular Mass Compounds in Spider Venoms

Spider venoms are highly complex mixtures. Numerous spider venom metabolites are uniquely found in spider venoms and are of interest concerning their potential use in pharmacology, agriculture, and cosmetics. A nontargeted ultra-high performance high-resolution electrospray tandem mass spectrometry (UHPLC-HR-ESI-MS/MS) approach offers a resource-saving way for the analysis of crude spider venom. However, the identification of known as well as the structure elucidation of unknown low molecular mass spider venom compounds based on their MS/MS spectra is challenging because (1) acylpolyamine toxins are exclusively found in spider and wasp venom, (2) reference MS/MS spectra are missing in established mass spectrometry databases, and (3) trivial names for the various toxin metabolites are used in an inconsistent way in literature. Therefore, we introduce the freely accessible MS website for low molecular mass spider venom metabolites, venoMS, containing structural information, MS/MS spectra, and links to related literature. Currently the database contains the structures of 409 acylpolyamine toxins, 36 free linear polyamines, and 81 additional spider venom metabolites. Implemented into this website is a fragment ion calculator (FRIOC) that allows us to predict fragment ions of linear polyamine derivatives. With three metabolites from the venom of the spider Agelenopsis aperta, it was demonstrated how the new website can support the structural elucidation of acylpolyamines using their MS/MS spectra.

Online Nano Solid Phase Extraction Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry Workflow to Analyze Small Scale Gradients of Soil Solution Organic Matter in the Rhizosphere

A new method combining online nano solid phase extraction coupled with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) was developed to extract and analyze organic matter (OM) from microliter volumes of salt containing soil solution samples. This approach allows the reproducible analysis of only minute amounts of organic carbon (down to 10 ng C) without the need of further sample preparation. The new method was applied to unravel developing small-scale patterns of dissolved organic matter (DOM) in soil solutions of a soil column experiment in which Zea mays plants were grown for 3 weeks. Soil solution was sampled by micro suction cups from the undisturbed soil-root system once a week. Growth of the root system and, hence, position of individual roots relative to the suction cups was followed by X-ray computed tomography (X-ray CT). Our method makes it possible to resolve the chemical complexity of soil solution OM (up to 4300 molecular formulas from 2.5 μL sample). This allows to observe chemical gradients in the rhizosphere on a molecular level over time. The increasing influence of roots on soil solution OM is visible from higher molecular masses, an increasing degree of oxygenation and a higher fraction of formulas containing heteroatoms. The online nano solid phase extraction-FT-ICR-MS method provides novel insight into the processes affecting DOM in the rhizosphere, such as root exudation, microbial processes, and soil organic matter stabilization.

Expanded coverage of non-targeted LC-HRMS using atmospheric pressure chemical ionization: a case study with ENTACT mixtures

Non-targeted analysis (NTA) is a rapidly evolving analytical technique with numerous opportunities to improve and expand instrumental and data analysis methods. In this work, NTA was performed on eight synthetic mixtures containing 1264 unique chemical substances from the U.S. Environmental Protection Agency's Non-Targeted Analysis Collaborative Trial (ENTACT). These mixtures were analyzed by atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI) using both positive and negative polarities for a total of four modes. Out of the 1264 ENTACT chemical substances, 1116 were detected in at least one ionization mode, 185 chemicals were detected using all four ionization modes, whereas 148 were not detected. Forty-four chemicals were detected only by APCI, and 181 were detected only by ESI. Molecular descriptors and physicochemical properties were used to assess which ionization type was preferred for a given compound. One ToxPrint substructure (naphthalene group) was found to be enriched in compounds only detected using APCI, and eight ToxPrints (e.g., several alcohol moieties) were enriched in compounds only detected using ESI. Examination of physicochemical parameters for ENTACT chemicals suggests that those with higher aqueous solubility preferentially ionized by ESI-. While ESI typically detects a larger number of compounds, APCI offers chromatograms with less background, fewer co-elutions, and additional chemical space coverage, suggesting both should be considered for broader coverage in future NTA research. Graphical abstract.

De novo prediction of the elemental composition of peptides and proteins based on a single mass

Identification of peptides and proteins is a common task in mass spectrometry-based proteomics but often fails to deliver a comprehensive list of identifications. Downstream analysis, quantitative or qualitative, depends on the outcome of this process. Despite continuous improvement of computational methods, a large fraction of the screened peptides and/or proteins remains unidentified. We introduce here pacMASS, a method that de novo predicts the elemental composition of peptides and small proteins based on a single accurate mass, ie, the observed monoisotopic or average mass. This novel approach returns in a fast and memory efficient manner a limited number of elemental compositions per queried peptide or protein.

Application of High Resolution Mass Spectrometric methods coupled with chemometric techniques in olive oil authenticity studies - A review

Extra Virgin Olive Oil (EVOO), the emblematic food of the Mediterranean diet, is recognized for its nutritional value and beneficial health effects. The main authenticity issues associated with EVOO's quality involve the organoleptic properties (EVOO or defective), mislabeling of production type (organic or conventional), variety and geographical origin, and adulteration. Currently, there is an emerging need to characterize EVOOs and evaluate their genuineness. This can be achieved through the development of analytical methodologies applying advanced "omics" technologies and the investigation of EVOOs chemical fingerprints. The objective of this review is to demonstrate the analytical performance of High Resolution Mass Spectrometry (HRMS) in the field of food authenticity assessment, allowing the determination of a wide range of food constituents with exceptional identification capabilities. HRMS-based workflows used for the investigation of critical olive oil authenticity issues are presented and discussed, combined with advanced data processing, comprehensive data mining and chemometric tools. The use of unsupervised classification tools, such as Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), as well as supervised classification techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Orthogonal Projection to Latent Structure-Discriminant Analysis (OPLS-DA), Counter Propagation Artificial Neural Networks (CP-ANNs), Self-Organizing Maps (SOMs) and Random Forest (RF) is summarized. The combination of HRMS methodologies with chemometrics improves the quality and reliability of the conclusions from experimental data (profile or fingerprints), provides valuable information suggesting potential authenticity markers and is widely applied in food authenticity studies.

Isomeric Differentiation and Acidic Metabolite Identification by Piperidine-Based Tagging, LC-MS/MS, and Understanding of the Dissociation Chemistries

We demonstrate a method for facile differentiation of acidic, isomeric metabolites by attaching high proton affinity, piperidine-based chemical tags to each carboxylic acid group. These tags attach with high efficiency to the analytes, increase the signal, and result in the formation of multiply-charged cations. We illustrate the present approach with citrate and isocitrate, which are isomeric metabolites each containing three carboxylic acid groups. We observe a 20-fold increase in signal-to-noise for citrate and an 8-fold increase for isocitrate as compared to detection of the untagged analytes in negative mode. Collision-induced dissociation of the triply tagged, triply charged analytes results in distinct tandem mass spectra. The phenylene spacer groups limit proton mobility and enable access to structurally informative C-C bond cleavage reactions. Modeling of the gas-phase structures and dissociation chemistry of these triply charged analyte ions highlights the importance of hydroxyl proton mobilization in this low proton mobility environment. Tandem mass spectrometric analyses of deuterated congeners and MS3 spectra are consistent with the proposed fragment ion structures and mechanisms of formation. Direct evidence that these chemistries are more generally applicable is provided by subsequent analyses of doubly tagged, doubly charged malate ions. Future work will focus on applying these methods to identify new metabolites and development of general rules for structural determination of tagged metabolites with multiple charges.

Absorption Mode Spectral Processing Improves Data Quality of Natural Organic Matter Analysis by Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry

Natural organic matter (NOM) plays an important role in elemental cycles and ecology. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is an ultrahigh resolution technique used to molecularly resolve the complexity of NOM mixtures. However, even the very high mass resolution of FT-ICR-MS may result in multiple formula assignments to peaks in an NOM spectrum, especially at the high mass-to-charge ratio (m/z). The absorption mode is one option to process raw FT-ICR-MS data that can further increase the resolution of the peaks and has not been widely applied in NOM studies. In this study, we show the advantages of using the absorption mode for the analysis of NOM samples using a reference sample (Suwannee River fulvic acid). The absorption mode increased the precision of peak detection as well as the number (+23%) and accuracy of formula assignment (by 28%) when compared to the magnitude mode, besides achieving three times higher resolution. The results presented here highlight the potential to reduce the error threshold used during molecular formula assignment. In conclusion, the absorption mode shows advantages in the processing of NOM samples and other complex mixtures and should be promoted in the NOM community.

DropMS: Petroleomics Data Treatment Based in Web Server for High-Resolution Mass Spectrometry

We have built an online tool with a user-friendly and browser-based interface to facilitate the processing of high resolution and precision oil mass spectrometry data. DropMS does not require software installations. Mass spectra are sent and processed by the server using various algorithms reported in the literature, such as S/N ratio filters, recalibrations, chemical formula assimilations, and data visualization using graphs and diagrams popularly known in mass spectrometry as Van Krevelen and Kendrick diagrams and DBE vs C#. To validate the algorithms used and the processing results, the same mass spectrum of a typical Brazilian oil sample was analyzed by ESI(+)-FT-ICR/MS and processed using Sierra Analytics DropMS and Composer to obtain good agreement between the heteroatomic classes found and the number of compounds assigned. The MS has chemical information spread over the entire spectrum. The PLS multivariate regression has the main objective of decomposing the most important information into latent variables in order to quantify the most evaluated properties. Finally, 12 processed petroleum FT-ICR MS spectra were used for a partial least-squares regression with seven latent variables for R² = 0.971 and RMSEC of 0.997 for API density property with a reference value range of 21-42.

Petroleomics: Tools, Challenges, and Developments

The detailed molecular characterization of petroleum-related samples by mass spectrometry, often referred to as petroleomics, continues to present significant analytical challenges. As a result, petroleomics continues to be a driving force for the development of new ultrahigh resolution instrumentation, experimental methods, and data analysis procedures. Recent advances in ionization, resolving power, mass accuracy, and the use of separation methods, have allowed for record levels of compositional detail to be obtained for petroleum-related samples. To address the growing size and complexity of the data generated, vital software tools for data processing, analysis, and visualization continue to be developed. The insights gained impact upon the fields of energy and environmental science and the petrochemical industry, among others. In addition to advancing the understanding of one of nature's most complex mixtures, advances in petroleomics methodologies are being adapted for the study of other sample types, resulting in direct benefits to other fields.

Neuroprotective potential of Ayahuasca and untargeted metabolomics analyses: applicability to Parkinson's disease

ETHNOPHARMACOLOGY RELEVANCE

Ayahuasca is a tea produced through decoction of Amazonian plants. It has been used for centuries by indigenous people of South America. The beverage is considered to be an ethnomedicine, and it is traditionally used for the treatment of a wide range of diseases, including neurological illness. Besides, some scientific evidence suggests it may be applicable to Parkinson's disease (PD) treatment. Thus, Ayahuasca deserves in depth studies to clarify its potential role in this disease.

AIM OF THE STUDY

This study aimed to use an untargeted metabolomics approach to evaluate the neuroprotective potential of the Ayahuasca beverage, the extracts from its matrix plants (Banisteriopsis caapi and Psychotria viridis), its fractions and its main alkaloids on the viability of SH-SY5Y neuroblastoma cells in an in vitro PD model.

MATERIAL AND METHODS

The cytotoxicity of Ayahuasca, crude extracts, and fractions of B. caapi and P. viridis, as well as neuroprotection promoted by these samples in a 6-hydroxydopamine (6-OHDA)-induced neurodegeneration model, were evaluated by the MTT assay at two time-points: 48 h (T1) and 72 h (T2). The main alkaloids from Ayahuasca matrix plants, harmine (HRE) and N,N-dimethyltryptamine (DMT), were also isolated and evaluated. An untargeted metabolomics approach was developed to explore the chemical composition of samples with neuroprotective activity. Ultra-Performance Liquid Chromatography coupled to Electrospray Ionisation and Time-of-Flight (UPLC-ESI-TOF) metabolome data was treated and further analysed using multivariate statistical analyses (MSA): principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The metabolites were dereplicated using the Dictionary of Natural Products and an in house database. The main alkaloids were also quantified by UPLC-MS/MS.

RESULTS

The samples did not cause cytotoxicity in vitro and three of samples intensely increased cell viability at T1. The crude extracts, alkaloid fractions and HRE demonstrated remarkable neuroprotective effect at T2 while the hydroalcoholic fractions demonstrated this neuroprotective effect at T1 and T2. Several compounds from different classes, such as β-carbolines and monoterpene indole alkaloids (MIAs) were revealed correlated with this property by MSA. Additionally, a total of 2419 compounds were detected in both ionisation modes. HRE showed potent neuroprotective action at 72 h, but it was not among the metabolites positively correlated with the most efficacious neuroprotective profile at either time (T1 and T2). Furthermore, DMT was statistically important to differentiate the dataset (VIP value > 1), although it did not exhibit sufficient neuroprotective activity by in vitro assay, neither a positive correlation with T1 and T2 neuroprotective profile, which corroborated the MSA results.

CONCLUSION

The lower doses of the active samples stimulated neuronal cell proliferation and/or displayed the most efficacious neuroprotection profile, namely by preventing neuronal damage and improving cell viability against 6-OHDA-induced toxicity. Intriguingly, the hydroalcoholic fractions exhibited enhanced neuroprotective effects when compared to other samples and isolated alkaloids. This finding corroborates the significance of a holistic approach. The results demonstrate that Ayahuasca and its base plants have potential applicability for PD treatment and to prevent its progression differently from current drugs to treat PD.

Metabolomics should be deployed in the identification and characterization of gene-edited crops

Gene-editing techniques are currently revolutionizing biology, allowing far greater precision than previous mutagenic and transgenic approaches. They are becoming applicable to a wide range of plant species and biological processes. Gene editing can rapidly improve a range of crop traits, including disease resistance, abiotic stress tolerance, yield, nutritional quality and additional consumer traits. Unlike transgenic approaches, however, it is not facile to forensically detect gene-editing events at the molecular level, as no foreign DNA exists in the elite line. These limitations in molecular detection approaches are likely to focus more attention on the products generated from the technology than on the process in itself. Rapid advances in sequencing and genome assembly increasingly facilitate genome sequencing as a means of characterizing new varieties generated by gene-editing techniques. Nevertheless, subtle edits such as single base changes or small deletions may be difficult to distinguish from normal variation within a genotype. Given these emerging scenarios, downstream 'omics' technologies reflective of edited affects, such as metabolomics, need to be used in a more prominent manner to fully assess compositional changes in novel foodstuffs. To achieve this goal, metabolomics or 'non-targeted metabolite analysis' needs to make significant advances to deliver greater representation across the metabolome. With the emergence of new edited crop varieties, we advocate: (i) concerted efforts in the advancement of 'omics' technologies, such as metabolomics, and (ii) an effort to redress the use of the technology in the regulatory assessment for metabolically engineered biotech crops.

Nontargeted identification of per- and polyfluoroalkyl substances in human follicular fluid and their blood-follicle transfer

The female reproductive toxicity of per- and polyfluoroalkyl substances (PFAS) has raised concerns, but knowledge about their human preconception exposure is limited. In this study, 15 emerging PFAS were identified in follicular fluid samples from healthy women by using high-resolution mass spectrometry, and Cl-substituted perfluoroalkyl ether sulfonates (Cl-PFESAs) including 4:2, 5:2, 6:2, and 8:2 Cl-PFESAs, 4:4 C₈ perfluoroalkyl ether sulfonate (PFESA), C₈ perfluoroalkyl ether carboxylate (PFECA), and C₈ polyether PFECA (Po-PFECA) were detected in over 50% of 28 follicular fluid samples. Ten legacy PFAS were also detected, and the geometric mean concentration of PFOS was the highest (4.82 ng/mL), followed by PFOA (4.60 ng/mL), 6:2 Cl-PFESA (1.09 ng/mL), PFHxS (0.515 ng/mL), PFNA (0.498 ng/mL), and C₈ PFECA (0.367 ng/mL). The blood-follicle transfer efficiencies for PFCAs decreased with increasing chain length (0.96 for PFHpA, 0.56 for PFTriDA), and the transfer efficiencies of C₈ PFECA (0.78) was significantly higher than that of PFOA (0.76). The transfer efficiencies of 4:2 Cl-PFESA (0.73), 6:2 Cl-PFESA (0.75) and 8:2 Cl-PFESA (0.91) were significantly higher than that (0.70) of PFOS (p = 0.028, 0.026 and 0.002, respectively). This study constitutes the first report of the human oocyte exposure to emerging PFAS and their blood-follicle transfer abilities.

A continental scale spatial investigation of lake sediment organic compositions using sedimentomics

Sedimentomics is a new method used to investigate carbon cycling in sediment organic matter. This untargeted method, based on metabolomics workflows, was used to investigate the molecular composition of sediment organic matter across northern Canada (Nunavut and Northwest Territories). Unique "lake districts" were defined using unsupervised clustering based on changes in sediment organic carbon compositions across space. Supervised machine learning analyses were used to compare the "lake districts" to commonly used regional classification systems like the treeline, ecozones, and/or georegions. Treeline was the best model to explain the compositional variance of sediment organic carbon from lakes across Canada, closely followed by the georegions model. A novel sediment metaphenomics analysis was also applied to determine how well environmental constraints explain the variation of sediment organic matter composition across a continent. We determined that sedimentomics is more informative than traditional measurements (such as total organic carbon) and can be integrated with other "omics" techniques.

Extensive screening of cyclopeptide toxins in mushrooms by ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry

A non-target screening method of cyclopeptide toxins and their analogues in mushroom was developed, using ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap mass spectrometry (UHPLC-Q-Orbitrap MS) followed by mass spectrometry databases retrieval and software tools analysis for the candidate analogues. Three cyclopeptide toxins in the toxic mushroom Amanita rimosa were firstly screened without standard, and two of them were unknown analogues which were tentatively identified by the accurate masses, isotopic patterns and characteristic fragments. A validated quantitative method was performed to rapidly quantify three major cyclopeptide toxins in the Amanita rimosa sample including α-manitin, β-amanitin and phalloidin, and their contents were detected to be 4.52 mg/kg, 2.37 mg/kg and 2.53 mg/kg, respectively. The developed method has good selectivity and sensitivity for rapid and comprehensive screening the cyclopeptide toxins and their analogues in mushrooms at trace levels. Successful non-target screening of trace cyclopeptide toxin analogues will guarantee the food safety in mushrooms consumption.

CycloBranch 2: Molecular Formula Annotations Applied to imzML Data Sets in Bimodal Fusion and LC-MS Data Files

Natural product chemistry, microbiology, and food, human, and plant metabolomics represent a few sources of complex metabolomics data generated by mass spectrometry. Among the medley of software tools used to handle these data sets, no universal tool can qualitatively, quantitatively, or statistically address major biological questions or tasks. CycloBranch 2, an open and platform-free software, at least now provides the de novo generation of molecular formulas of unknown compounds in both liquid chromatography/mass spectrometry and mass spectrometry imaging datafiles. For imaging files, this database-free approach was documented in the bimodal image fusion and characterization of three small molecules, including metallophores. The fine isotope ratio data filtering step distinguished ³⁴S/¹³C₂ and ⁴¹K/¹³C₂ features. The standalone software package is implemented in C++ and can be downloaded from https://ms.biomed.cas.cz/cyclobranch/ and used under GNU General Public License.

Anti-urolithiatic and anti-inflammatory activities through a different mechanism of actions of Cissus gongylodes corroborated its ethnopharmacological historic

ETHNOPHARMACOLOGICAL RELEVANCE

Species Cissus gongylodes has been used in the traditional medicine in South America and India for the treatment of urolithiasis, biliary and inflammatory problems without any scientific evidence.

AIM OF THE STUDY

This work was developed to investigate for the first time the anti-inflammatory and anti-urolithiatic activities of leaf decoction of C. gongylodes.

MATERIALS AND METHODS

Decoction was subjected to anti-inflammatory evaluation by the in vivo assay of ear oedema and quantification of the main mediators of inflammation PGE₂ and LTB₄, and the cytokine TNF-α. The decoction's anti-urolithiatic activity was determined by different in vitro assays to evaluate the inhibition and dissolution of the most prevalent types of kidney stones: calcium oxalate (CaOx) and struvite. Diffusion in gel technique and fresh urine of a patient with renal stone were used to investigate the inhibition and dissolution of CaOx, respectively, and the single diffusion gel growth technique was used to evaluate the inhibition and dissolution of struvite crystals. The decoction was chemically characterized by UHPLC-ESI-HRMS analysis.

RESULTS

Decoction showed in vivo anti-inflammatory activity by potent decreasing the level of both the main mediators of inflammation and dose-dependent in vitro anti-urolithiatic action by inhibition and dissolution of both type of crystals, CaOx and struvite.

CONCLUSIONS

Results obtained corroborate the reports of the traditional use of the decoction of Cissus gongylodes. Besides, it showed multi-target mechanisms actions, inhibition of the main inflammatory pathways, and inhibition/dissolution of the most prevalent types of crystals on urolithiasis. These actions make the decoction a promissory source to the development of new and more efficient drugs.

A fungal pathogen induces systemic susceptibility and systemic shifts in wheat metabolome and microbiome composition

Yield losses caused by fungal pathogens represent a major threat to global food production. One of the most devastating fungal wheat pathogens is Zymoseptoria tritici. Despite the importance of this fungus, the underlying mechanisms of plant–pathogen interactions are poorly understood. Here we present a conceptual framework based on coinfection assays, comparative metabolomics, and microbiome profiling to study the interaction of Z. tritici in susceptible and resistant wheat. We demonstrate that Z. tritici suppresses the production of immune-related metabolites in a susceptible cultivar. Remarkably, this fungus-induced immune suppression spreads within the leaf and even to other leaves, a phenomenon that we term “systemic induced susceptibility”. Using a comparative metabolomics approach, we identify defense-related biosynthetic pathways that are suppressed and induced in susceptible and resistant cultivars, respectively. We show that these fungus-induced changes correlate with changes in the wheat leaf microbiome. Our findings suggest that immune suppression by this hemibiotrophic pathogen impacts specialized plant metabolism, alters its associated microbial communities, and renders wheat vulnerable to further infections.

The fungal plant pathogen Zymoseptoria tritici is a major threat to wheat yield. Here Seybold et al. show that Z. tritici can suppress immune responses not only in infected tissue but also on other leaves, a phenomenon termed “systemic induced susceptibility” that is correlated with systemic changes in metabolite accumulation.

Assessing accuracy, precision and selectivity using quality controls for non-targeted analysis

The benchmarks to assess reproducibility are not well defined for non-target analysis. Parameters to evaluate analytical performance, such as accuracy, precision and selectivity, are well defined for target analysis, but remain elusive for non-target screening analysis. In this study, quality control (QC) guidelines are proposed to assure reliable data in non-target screening methodologies using a simple set of standards. Workflow reproducibility was assessed using an in-house QC mixture containing selected compounds with a wide range of polarity that can be detected either by electrospray ionization (ESI) in positive or negative mode. The analysis was performed by online solid phase extraction (SPE) liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS). Data processing was done by a commercially available software, Compound Discoverer v. 3.0 using an environmental working template, which searched a multitude of databases, including Chemspider, EPA Toxcast, MzCloud among others. We have specifically evaluated method specificity, precision, accuracy and reproducibility in terms of peak area and retention time variability, true positive identification rate, intraday (within days) and interday (consecutive days) variations and the use of QC samples to reduce false positives. The method showed a satisfactory accuracy with an identification rate of ≥70% for most of the QC compounds. Precision estimated based on peak area relative standard deviation (RSD) ranged between 30 and 50% for most of the compounds. Data normalization to a single internal standard did not improve peak area variability. Retention time precision showed great repeatability and reproducibility (RSD ≤ 5%). In addition, a simple model of RT vs log K_ow was designed based on our QC mixtures to efficiently reduced false positives by an average of 49.1%.

Online Aerosol Chemical Characterization by Extractive Electrospray Ionization-Ultrahigh-Resolution Mass Spectrometry (EESI-Orbitrap)

Current mass spectrometry techniques for the online measurement of organic aerosol (OA) composition are subjected to either thermal/ionization-induced artifacts or limited mass resolving power, hindering accurate molecular characterization. Here, we combined the soft ionization capability of extractive electrospray ionization (EESI) and the ultrahigh mass resolution of Orbitrap for real-time, near-molecular characterization of OAs. Detection limits as low as tens of ng m^-3 with linearity up to hundreds of μg m^-3 at 0.2 Hz time resolution were observed for single- and mixed-component calibrations. The performance of the EESI-Orbitrap system was further evaluated with laboratory-generated secondary OAs (SOAs) and filter extracts of ambient particulate matter. The high mass accuracy and resolution (140 000 at m/z 200) of the EESI-Orbitrap system enable unambiguous identification of the aerosol components' molecular composition and allow a clear separation between adjacent peaks, which would be significantly overlapping if a medium-resolution (20 000) mass analyzer was used. Furthermore, the tandem mass spectrometry (MS²) capability provides valuable insights into the compound structure. For instance, the MS² analysis of ambient OA samples and lab-generated biogenic SOAs points to specific SOA precursors in ambient air among a range of possible isomers based on fingerprint fragment ions. Overall, this newly developed and characterized EESI-Orbitrap system will advance our understanding of the formation and evolution of atmospheric aerosols.

Transplacental Transfer of Per- and Polyfluoroalkyl Substances Identified in Paired Maternal and Cord Sera Using Suspect and Nontarget Screening

Novel per- and polyfluoroalkyl substances (PFASs) in various environmental media have attracted increasing attention; however, the information regarding PFASs exposure in pregnant women and fetuses is insufficient. In this study, we built and applied suspect and nontarget screening strategies based on the mass difference of the CF₂, CF₂O, and CH₂CF₂ units to select potential novel PFASs from 117 paired maternal and cord sera. In total, 10 legacy PFASs and 19 novel PFASs from 10 classes were identified to be above confidence levels 3, among which 14 were not previously reported in human serum. Novel PFASs accounted for a considerable percentage of total PFASs in pregnant women and can be transferred to fetuses at non-negligible concentrations (i.e., 27.9% and 30.3% of total PFAS intensities in maternal and cord sera, respectively). The transplacental transfer efficiency (TTE) of PFASs showed a U-shape trend in the series of perfluoroalkyl carboxylic acids, perfluoroalkyl sulfonic acids, and unsaturated perfluorinated alcohols. The TTE of novel PFASs is suggested to be structure-dependent, based on a flexible docking experiment. This study provides comprehensive TTE information on legacy and novel PFASs for the first time, and additional toxicity studies are needed to evaluate the risk of novel PFASs further.

An algorithm to calibrate ionic isotopes using data mining strategy in hyphenated chromatographic datasets from herbal samples

The bottleneck of analytical instrument itself and non-ideal instrumental performance will produce a certain degree of drifts between the measured isotopes and the true values. An AAID-IC algorithm was thereby proposed to keep the isotopic distributions more accurate in hyphenated instruments, e.g. Gas Chromatography (GC)/ Liquid Chromatography (LC) - Mass Spectrometry (MS). During this data mining process, chemical information will be fully used from dozens of data points in retention time (rt) dimension: the target isotopes were firstly re-constructed in mass charge ratio (m/z) dimension; their re-calculation values were then averaged from an interesting rt zone; the calibration functions were followed established based on a well-defined series of calibration ions. It is worth mentioning that natural metabolites in complex samples can be identified as reference materials to amend the target isotopes. Next, the corrected mass axes (m/z values)/isotope abundances were transformed into an ionic isotopic curve using Gaussian box. Taking herbal sample as an example, AAID-IC can better reduce the systematic and random errors of the m/z ions in one run environment, whether it's profile or bar graph from any type of MS and any ionization method employed. Finally, the calibrated values can be utilized to deduce the elemental compositions of molecular (fragment) ions in GC/LC-MS determination.

MS-Derived Isotopic Fine Structure Reveals Forazoline A as a Thioketone-Containing Marine-Derived Natural Product

Forazoline A is a structurally complex PKS-NRPS hybrid produced by marine-derived Actinomadura sp. During the course of studies highlighting the application of IFS analysis as a powerful tool for natural products analysis, we were alerted to an earlier misinterpretation with respect to forazoline A structure elucidation. In particular, IFS reveals that forazoline A contains a thioketone moiety rarely seen in secondary metabolites and, thus, constitutes an even more intriguing structure than originally thought.

A complex matrix characterization approach, applied to cigarette smoke, that integrates multiple analytical methods and compound identification strategies for non-targeted liquid chromatography with high-resolution mass spectrometry

RATIONALE

For the characterization of the chemical composition of complex matrices such as tobacco smoke, containing more than 6000 constituents, several analytical approaches have to be combined to increase compound coverage across the chemical space. Furthermore, the identification of unknown molecules requiring the implementation of additional confirmatory tools in the absence of reference standards, such as tandem mass spectrometry spectra comparisons and in silico prediction of mass spectra, is a major bottleneck.

METHODS

We applied a combination of four chromatographic/ionization techniques (reversed-phase (RP) - heated electrospray ionization (HESI) in both positive (+) and negative (-) modes, RP - atmospheric pressure chemical ionization (APCI) in positive mode, and hydrophilic interaction liquid chromatography (HILIC) - HESI positive) using a Thermo Q Exactive™ liquid chromatography/high-resolution accurate mass spectrometry (LC/HRAM-MS) platform for the analysis of 3R4F-derived smoke. Compound identification was performed by using mass spectral libraries and in silico predicted fragments from multiple integrated databases.

RESULTS

A total of 331 compounds with semi-quantitative estimates ≥100 ng per cigarette were identified, which were distributed within the known chemical space of tobacco smoke. The integration of multiple LC/HRAM-MS-based chromatographic/ionization approaches combined with complementary compound identification strategies was key for maximizing the number of amenable compounds and for strengthening the level of identification confidence. A total of 50 novel compounds were identified as being present in tobacco smoke. In the absence of reference MS2 spectra, in silico MS2 spectra prediction gave a good indication for compound class and was used as an additional confirmatory tool for our integrated non-targeted screening (NTS) approach.

CONCLUSIONS

This study presents a powerful chemical characterization approach that has been successfully applied for the identification of novel compounds in cigarette smoke. We believe that this innovative approach has general applicability and a huge potential benefit for the analysis of any complex matrices.

Advanced identification of global bioactivity hotspots via screening of the metabolic fingerprint of entire ecosystems

Natural products (NP) are a valuable drug resource. However, NP-inspired drug leads are declining, among other reasons due to high re-discovery rates. We developed a conceptual framework using the metabolic fingerprint of entire ecosystems (MeE) to facilitate the discovery of global bioactivity hotspots. We assessed the MeE of 305 sites of diverse aquatic ecosystems, worldwide. All samples were tested for antiviral effects against the human immunodeficiency virus (HIV), followed by a comprehensive screening for cell-modulatory activity by High-Content Screening (HCS). We discovered a very strong HIV-1 inhibition mainly in samples taken from fjords with a strong terrestrial input. Multivariate data integration demonstrated an association of a set of polyphenols with specific biological alterations (endoplasmic reticulum, lysosomes, and NFkB) caused by these samples. Moreover, we found strong HIV-1 inhibition in one unrelated oceanic sample closely matching to HIV-1-inhibitory drugs on a cytological and a chemical level. Taken together, we demonstrate that even without physical purification, a sophisticated strategy of differential filtering, correlation analysis, and multivariate statistics can be employed to guide chemical analysis, to improve de-replication, and to identify ecosystems with promising characteristics as sources for NP discovery.

Dissolved organic matter in the deep TALDICE ice core: A nano-UPLC-nano-ESI-HRMS method

Trace organic compounds in deep ice cores supply important paleoclimatic information. Untargeted analyses of dissolved organic matter provide an overview of molecular species in ice samples however, sample volumes usually required for these analyses are generally not available from deep ice cores. Here, we developed an analytical method using a nano-UPLC-nano-ESI-HRMS to detect major molecular species in ice cores. Samples (4 µL) from the TALos Dome Ice CorE (TALDICE), allowed investigating molecular species across a range of depths including during glacial and interglacial periods. We detected 317 chemical species that were tentatively assigned to fatty acids, hydroxy fatty acids and their degradation products (oxo-fatty acids and dicarboxylic acids), as well as oxidation byproducts of isoprene and monoterpenes. These compounds indicate that the main sources of the organic fraction are microbes as well as primary and secondary aerosols. Interglacial samples encompass a wide range of species including compounds from the oxidation of isoprene and monoterpenes as well as unsaturated fatty acids, while the glacial samples contained less diverse species. This difference may be due to decreased temperatures during the glacial period inhibiting terrestrial vegetation growth and increasing the sea ice extent, thereby weakening the emission sources.

Volatile organic compound breath signatures of children with cystic fibrosis by real-time SESI-HRMS

Early pulmonary infection and inflammation result in irreversible lung damage and are major contributors to cystic fibrosis (CF)-related morbidity. An easy to apply and noninvasive assessment for the timely detection of disease-associated complications would be of high value. We aimed to detect volatile organic compound (VOC) breath signatures of children with CF by real-time secondary electrospray ionisation high-resolution mass spectrometry (SESI-HRMS). A total of 101 children, aged 4-18 years (CF=52; healthy controls=49) and comparable for sex, body mass index and lung function were included in this prospective cross-sectional study. Exhaled air was analysed by a SESI-source linked to a high-resolution time-of-flight mass spectrometer. Mass spectra ranging from m/z 50 to 500 were recorded. Out of 3468 m/z features, 171 were significantly different in children with CF (false discovery rate adjusted p-value of 0.05). The predictive ability (CF versus healthy) was assessed by using a support-vector machine classifier and showed an average accuracy (repeated cross-validation) of 72.1% (sensitivity of 77.2% and specificity of 67.7%). This is the first study to assess entire breath profiles of children with SESI-HRMS and to extract sets of VOCs that are associated with CF. We have detected a large set of exhaled molecules that are potentially related to CF, indicating that the molecular breath of children with CF is diverse and informative.

Phytochelatin database: a resource for phytochelatin complexes of nutritional and environmental metals

Phytochelatins (PyCs) are a diverse set of plant compounds that chelate metals, protect against metal toxicity and function in metal homeostasis. PyCs are present in plants consumed as food by humans and could, in principle, impact absorption and utilization of essential and toxic metals such as selenium and cadmium, respectively. PyCs vary in terminal amino acid composition and chain length, exist in multiple oxidation states and reversibly bind multiple metals; consequently, PyCs include a large set of possible structures. Although individual PyC-metal complexes have been studied, no resource exists to characterize the diversity of PyCs and PyC-metal complexes. We used the scientific literature to develop a database of elemental formulas for polymer forms varying in chain length from 2 to 11 glutamyl-cysteine repeats. Using elemental formulas, we calculated monoisotopic masses using the most abundant isotopes of each element and calculated masses for complexes with 13 metals of nutritional and toxicological significance. The resulting phytochelatin database (PyCDB) contains 46 260 unique elemental formulas for PyC and PyC-metal complexes. The database is available online for download as well as for direct mass queries for mass spectrometry using an accurate mass annotation tool for user-selected PyC types, metals and adducts of interest. We performed studies of a commonly consumed food—onion—to validate the database and test utility of the tool. Onion samples were analyzed using ultra-high resolution mass spectrometry-based metabolomics. Mass spectral features were annotated using the PyCDB web tool and the R package, xMSannotator; annotated features were further validated by collision-induced dissociation mass spectrometry. The results establish use and a workflow for PyCDB as a resource for characterization of PyCs and PyC-metal complexes.

A "Refined Hydrogen Rule" and a "Refined Hydrogen and Halogen Rule" for Organic Molecules

Deriving chemical formulas of organic molecules, based on spectral information, with heuristic rules is a commonly recurring task. The computational effort and the potentially extensive list of candidate formulas put a strain on the downstream analysis. In this paper, we introduce a set of redefined heuristics based on the hydrogen and halogen rules that reduce the computational burden and the number of candidate formulas for organic molecules, such as peptides and lipids.

Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms

Rapid advancements in metabolomics technologies have allowed for application of liquid chromatography mass spectrometry (LCMS)-based metabolomics to investigate a wide range of biological questions. In addition to an important role in studies of cellular biochemistry and biomarker discovery, an exciting application of metabolomics is the elucidation of mechanisms of drug action (Creek et al., Antimicrob Agents Chemother 60:6650-6663, 2016; Allman et al., Antimicrob Agents Chemother 60:6635-6649, 2016). Although it is a very useful technique, challenges in raw data processing, extracting useful information out of large noisy datasets, and identifying metabolites with confidence, have meant that metabolomics is still perceived as a highly specialized technology. As a result, metabolomics has not yet achieved the anticipated extent of uptake in laboratories around the world as genomics or transcriptomics. With a view to bring metabolomics within reach of a nonspecialist scientist, here we describe a routine workflow with IDEOM, which is a graphical user interface within Microsoft Excel, which almost all researchers are familiar with. IDEOM consists of custom built algorithms that allow LCMS data processing, automatic noise filtering and identification of metabolite features (Creek et al., Bioinformatics 28:1048-1049, 2012). Its automated interface incorporates advanced LCMS data processing tools, mzMatch and XCMS, and requires R for complete functionality. IDEOM is freely available for all researchers and this chapter will focus on describing the IDEOM workflow for the nonspecialist researcher in the context of studies designed to elucidate mechanisms of drug action.

FTICR mass spectrometry-based multivariate analysis to explore distinctive metabolites and metabolic pathways: A comprehensive bioanalytical strategy toward time-course metabolic profiling of Thymus vulgaris plants responding to drought stress

In this research, metabolic profiling/pathways of Thymus vulgaris (thyme) plant were assessed during a water deficit stress using an FTICR mass spectrometry-based metabolomics strategy incorporating multivariate data analysis and bioinformatics techniques. Herein, differences of MS signals in specific time courses after water deficit stress and control cases without any timing period were distinguished significantly by common pattern recognition techniques, i.e., PCA, HCA-Heatmap, and PLS-DA. Subsequently, the results were compared with supervised Kohonen neural network (SKN) ones as a non-linear data visualization and capable mapping tool. The classification models showed excellent performance to predict the level of drought stress. By assessing variances contribution on the PCA-loadings of the MS data, the discriminant variables related to the most critical metabolites were identified and then confirmed by ANOVA. Indeed, FTICR MS-based multivariate analysis strategy could explore distinctive metabolites and metabolic pathways/profiles, grouped into three metabolism categories including amino acids, carbohydrates (i.e., galactose, glucose, fructose, sucrose, and mannose), and other metabolites (rosmarinic acid and citrate), to indicate biological mechanisms in response to drought stress for thyme. It was achieved and approved through the MS signals, genomics databases, and transcriptomics factors to interpret and predict the plant metabolic behavior. Eventually, a comprehensive pathway analysis was used to provide a pathway enrichment analysis and explore topological pathway characteristics dealing with the remarkable metabolites to demonstrate that galactose metabolism is the most significant pathway in the biological system of thyme.

Molecular identification guided process design for advanced treatment of electroless nickel plating effluent

It has been long desired but challenging to forward the advanced treatment of wastewater from empirical trials towards scientific design due to the lack of molecular insight into the pollutants of concern. Herein, we first established a systematic methodology to identify the ligands of Ni(II)-complexes in an electroless nickel (EN) plating effluent. The presence of N-containing groups in the ligands of most Ni(II)-complexes was verified by time-aligned ICP-MS and ESI-HRMS, implying the suitability of autocatalytic ozonation for efficient decomplexation. Thereby, a combined process was proposed on the basis of ozonation to achieve over 83% decomplexation of Ni(II) (initially at 0.36 mg/L), followed by selective Ni(II) sequestration for resource recovery. Combinational LC-MS systems revealed the ozonation-induced fragmentation or elimination of most Ni(II)-complexes as well as the structural change of the residual complexed molecules. The released free Ni(II) was further sequestrated by a nanocomposite of hydrated Zr(IV) oxide confined in a polymeric cation exchanger (nHZO@PCE). The fixed-bed working capacity of nHZO@PCE (∼550 BV) for the ozonated EN plating effluent was over 18 times that of the cation exchanger host (∼30 BV) at the breakthrough point of 0.10 mg Ni/L. More attractively, five adsorption-regeneration cycles demonstrated the great potential of the hybrid adsorbent for sustainable utilization. This study is believed to shed new light on how to design rational processes for advanced treatment of real wastewater based on molecular identification.

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

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.

Rapid fingerprinting of source and environmental microplastics using direct analysis in real time-high resolution mass spectrometry

Microplastics are ubiquitous in the aquatic and terrestrial environment. To prevent further contamination, methods to determine their sources are needed. Techniques to quantify and characterize microplastics in the environment are still evolving for polymers and the additives and leachable substances embedded therein, which constitute the "chemical fingerprint" of an environmental microplastic. There is a critical need for analytical methods that yield such diagnostic information on environmental microplastics that enables identification of their composition and sources of pollution. This study reports on a novel approach for rapid fingerprinting of environmental microplastics and the screening of additives using Direct Analysis in Real Time (DART)-high resolution mass spectrometry. A variety of plastic samples were investigated, including virgin pre-production pellets, microbeads from personal care products, microplastics found in the aquatic environment, and synthetic fibers. The resulting mass spectra display ∼10,000 discrete peaks, corresponding to plastic additives released by thermal desorption and polymer degradation products generated by pyrolysis. These were used to characterize differences among plastic types, microplastic source materials, and environmental samples. Multivariate statistics and elemental composition analysis approaches were applied to analyze fingerprints from the mass spectra. This promising analytical approach is sensitive, (potentially) high-throughput, and can aid in the elucidation of possible sources of microplastics and perhaps eventually to the analysis of bulk environmental samples for plastics.

The Kendrick analysis for polymer mass spectrometry

The mass spectrum of a polymer often displays repetitive patterns with peak series spaced by the repeating unit(s) of the polymeric backbones, sometimes complexified with different adducts, chain terminations, or charge states. Exploring the complex mass spectral data or filtering the unwanted signal is tedious whether performed manually or automatically. In contrast, the now 60-year-old Kendrick (mass defect) analysis, when adapted to polymer ions, produces visual two-dimensional maps with intuitive alignments of the repetitive patterns and favourable deconvolution of features overlaid in the one-dimensional mass spectrum. This special feature article reports on an up-to-date and theoretically sound use of Kendrick plots as a data processing tool. The approach requires no prior knowledge of the sample but offers promising dynamic capabilities for visualizing, filtering, and sometimes assigning congested mass spectra. Examples of applications of the approach to polymers are discussed throughout the text, but the same tools can be readily extended to other applications, including the analysis of polymers present as pollutants/contaminants, and to other analytes incorporating a repetitive moiety, for example, oils or lipids. In each of these instances, data processing can benefit from the application of an updated and interactive Kendrick analysis.

Kendrick Mass Defect Approach Combined to NORINE Database for Molecular Formula Assignment of Nonribosomal Peptides

The identification of known (dereplication) or unknown nonribosomal peptides (NRPs) produced by microorganisms is a time consuming, expensive, and challenging task where mass spectrometry and nuclear magnetic resonance play a key role. The first step of the identification process always involves the establishment of a molecular formula. Unfortunately, the number of potential molecular formulae increases significantly with higher molecular masses and the lower precision of their measurements. In the present article, we demonstrate that molecular formula assignment can be achieved by a combined approach using the regular Kendrick mass defect (RKMD) and NORINE, the reference curated database of NRPs. We observed that irrespective of the molecular formula, the addition and subtraction of a given atom or atom group always leads to the same RKMD variation and nominal Kendrick mass (NKM). Graphically, these variations translated into a vector mesh can be used to connect an unknown molecule to a known NRP of the NORINE database and establish its molecular formula. We explain and illustrate this concept through the high-resolution mass spectrometry analysis of a commercially available mixture composed of four surfactins. The Kendrick approach enriched with the NORINE database content is a fast, useful, and easy-to-use tool for molecular mass assignment of known and unknown NRP structures.

Rapid Characterization and Discovery of Chemical Markers for Discrimination of Xanthii Fructus by Gas Chromatography Coupled to Mass Spectrometry

Xanthii Fructus (XF) is known as a medicinal plant. It has been used as a traditional medicine because of its high biological efficacy. However, there have been few comprehensive studies on the specific chemical composition of the plant and consequently, the information is lacking for the mechanism of the natural product metabolites in humans. In this study, an efficient analytical method to characterize and discriminate two species of Xanthii Fructus (Xanthium canadense Mill. and Xanthium sibiricum Patrin ex Widder) was established. Volatile organic compounds (VOCs), polar metabolites, and fatty acids were classified by integrated sample preparation, which allowed a broad range for the detection of metabolites simultaneously. Gas chromatography-mass spectrometry (GC-MS) followed by a multivariate statistical analysis was employed to characterize the chemical compositions and subsequently to discriminate between the two species. The results demonstrate that the two species possess obviously diverse chemical characteristics of three different classifications, and discriminant analysis was successfully applied to a number of chemical markers that could be used for the discrimination of the two species. Additional quantitative results for the selected chemical markers consistently showed significant differences between the two species.

Design and Evaluation of a Gas Chromatograph-Atmospheric Pressure Chemical Ionization Interface for an Exactive Orbitrap Mass Spectrometer

Various separation and mass spectrometric (MS) techniques have furthered our ability to study complex mixtures, and the desire to measure every analyte in a system is of continual interest. For many complex mixtures, such as the total molecular content of a cell, it is becoming apparent that no one single separation technique or analysis is likely to achieve this goal. Therefore, having a variety of tools to measure the complexity of these mixtures is prudent. Orbitrap MSs are broadly used in systems biology studies due to their unique performance characteristics. However, GC-Orbitraps have only recently become available, and instruments that can use gas chromatography (GC) cannot use liquid chromatography (LC) and vice versa. This limits small molecule analyses, such as those that would be employed for metabolomics, lipidomics, or toxicological studies. Thus, a simple, temporary interface was designed for a GC and Thermo Scientific™ Ion Max housing unit. This interface enables either GC or LC separation to be used on the same MS, an Exactive™ Plus Orbitrap, and utilizes an atmospheric pressure chemical ionization (APCI) source. The GC-APCI interface was tested against a commercially available atmospheric pressure photoionization (APPI) interface for three types of analytes that span the breadth of typical GC analyses: fatty acid methyl esters (FAMEs), polyaromatic hydrocarbons (PAHs), and saturated hydrocarbons. The GC-APCI-Orbitrap had similar or improved performance to the APPI and other reported methods in that it had a lower limit of quantitation, better signal to noise, and lower tendency to fragment analytes.

Leaving Group Effects in a Series of Electrosprayed CcHhN1 Anthracene Derivatives

We investigate the gas-phase structures and fragmentation pathways of model compounds of anthracene derivatives of the general formula C_cH_hN₁ utilizing tandem mass spectrometry and computational methods. We vary the substituent alkyl chain length, composition, and degree of branching. We find substantial experimental and theoretical differences between the linear and branched congeners in terms of fragmentation thresholds, available pathways, and distribution of products. Our calculations predict that the linear substituents initially isomerize to form lower energy branched isomers prior to loss of the alkyl substituents as alkenes. The rate-determining chemistry underlying these related processes is dominated by the ability to stabilize the alkene loss transition structures. This task is more effectively undertaken by branched substituents. Consequently, analyte lability systematically increased with degree of branching (linear < secondary < tertiary). The resulting anthracen-9-ylmethaniminium ion generated from these alkene loss reactions undergoes rate-limiting proton transfer to enable expulsion of either hydrogen cyanide or CNH. The combination of the differences in primary fragmentation thresholds and degree of radical-based fragmentation processes provide a potential means of distinguishing compounds that contain branched alkyl chain substituents from those with linear ones.

Compositional space: A guide for environmental chemists on the identification of persistent and bioaccumulative organics using mass spectrometry

Since 2001, twenty-eight halogenated groups of persistent organic pollutants (POPs) have been banned or restricted by the Stockholm Convention. Identifying new POPs among the hundreds of thousands of anthropogenic chemicals is a major challenge that is increasingly being met by state-of-the-art mass spectrometry (MS). The first step to identification of a contaminant molecule (M) is the determination of the type and number of its constituent elements, viz. its elemental composition, from mass-to-charge (m/z) measurements and ratios of isotopic peaks (M + 1, M + 2 etc.). Not every combination of elements is possible. Boundaries exist in compositional space that divides feasible and improbable compositions as well as different chemical classes. This study explores the compositional space boundaries of persistent and bioaccumulative organics. A set of ~305,134 compounds (PubChem) was used to visualize the compositional space occupied by F, Cl, and Br compounds, as defined by m/z and isotope ratios. Persistent bioaccumulative organics, identified by in silico screening of 22,049 commercial chemicals, reside in more constrained regions characterized by a higher degree of halogenation. In contrast, boundaries surrounding non-halogenated chemicals could not be defined. Finally, a script tool (R code) was developed to select potential POPs from high resolution MS data. When applied to household dust (SRM 2585), this approach resulted in the discovery of previously unknown chlorofluoro flame retardants.