Mass Spectral Database Research Articles

The Identity Algorithm: How the Most Popular Electron Ionization Mass Spectral Library Search Engine Actually Works.

The Identity algorithm implemented in the MS Search (NIST) software is widely used for library searches of gas chromatography/mass spectrometry data against electron ionization mass spectral databases. It has been available to researchers since 1993, with the release of MS Search 1.5a. Despite its extensive use, the operational details of the algorithm have remained ambiguous. Attempts to replicate the algorithm have been unsuccessful because, as found in this research, the description in the manual is neither fully complete nor accurate. The main novelty of this work is the development of a unique approach for deconstructing the Identity algorithm. It is purely based on analyzing library search results obtained from several groups of synthetic mass spectra, each tailored to isolate and examine specific components of the algorithm. This approach facilitated a comprehensive understanding of the Identity algorithm and led to the development of a custom implementation that fully replicates the results obtained from the original MS Search software. The custom implementation of the Identity algorithm is now available in the mssearchr R package, enhancing accessibility for researchers.

Determination of persistent and mobile organic compounds in the river–groundwater interface of the Besòs river delta, Spain, using a wide extraction approach

Climate change impacts the Mediterranean region, transforming it from region with a semi-arid climate to a region with an arid climate. Under this situation, while groundwater is an essential hydric resource, its existence is in danger due to anthropogenic pressures. Persistent mobile organic compounds (PMOCs) have recently been recognised as an emerging problem; however, PMOCs in groundwater need to be better characterised. Here, we present a new analytical method to characterise the profile of PMOCs in groundwater based on two parallel solid-phase extraction (SPE), using weak anion exchange and weak cation exchange. Extracts were analysed by ultraperformance liquid chromatography (UPLC) using mix-mode chromatography for those compounds analysed under negative ionisation conditions and hydrophilic interaction liquid chromatography (HILIC) under positive conditions coupled to high-resolution mass-spectrometry (HRMS) using a Q-Exactive Orbitrap™ analyser. For the suspect screening of PMOCs in groundwater, the acquisition mode was in full scan (FS) by “independent scan of all ion fragmentation”. For the tentative identification, different online databases such as Environmental and Food Safety (EFS) HRAM Compound database, PFAS NIST database, ChemSpider for chemical structural information, MzCloud as a mass spectral database, and an in-house list with 1280 PMOC structures have been used. The performance of the method was assessed with 29 representative PMOCs which were selected based on the previous literature. The recovery rates have been between 63 and 110 % for 90 % of the target compounds and method limits of quantification (MLQ) between 0.3 and 10.5 ng/L.The optimised approach was applied to assess PMOCs in the Besòs River aquifer, NE Spain, showing 148 tentatively identified structures at confidence levels 1–3. Among them, 66 suspects were tentatively identified at level 3, 54 at level 2, and 28 confirmed at level 1. Most of these compounds were polar and highly polar compounds which are difficult to retain with other extraction approaches. Major detected compounds were pharmaceuticals and personal care products (46), followed by perfluoroalkyl and polyfluoroalkyl substances (PFAS) (32), industrial additives (27), and pesticides (23), among other groups. Some compounds, such as ultrashort chain PFAS and fluorinated betaines, were detected for the first time in groundwaters in Spain.

Diagnostic screening of private well water using gas chromatography with high resolution mass spectrometry to support well water management

In the US, private well users are responsible for their own water quality testing, but local health officials are often uncertain what tests to recommend, particularly for regulated organic chemical contaminants. This study evaluated the utility of suspect and non-target screening (NTS) high resolution mass spectrometry (HRMS) as a tool to identify a wide range of organic chemicals of emerging concern in private well water and to inform well water management decisions. Qualitative NTS, which detects chemicals without providing concentrations, was used to analyze 25 private well water samples from Wake County, North Carolina. Using the NIST 20 mass spectral database (M1), NTS tentatively identified 106 unique organic chemicals across the 25 samples and an average of 11 organic chemicals per sample. At least one USEPA ToxCast chemical was identified in each private well water sample. Private well water users were interviewed prior to and after their sample's NTS results were reported back; four county groundwater managers were interviewed after aggregated results for all 25 water samples were reported back. All but one well user participant chose to participate in the reporting-back post-interview. The 24 private well users found NTS results useful and valued the contextualization of their results using NTS results for other well users and a local municipal water sample. Most private well users (67%) were surprised by their well water results, especially regarding the number of tentatively identified organic chemicals detected. All the groundwater managers believed the NTS results were useful and could help improve their testing recommendations to private well users. Even with qualitative limitations, NTS results can be an effective and valuable tool to inform the public and governance stakeholders in decisions around groundwater quality management.

Interlaboratory and cross-platform accessibility of time-of-flight wood identification database

The mass spectral database of tree species built by US Fish and Wildlife Service has thousands of entries and has been a valuable resource to combat illegal logging and international trade. The database was and continues to be constructed using a particular ambient ionization time-of-flight mass spectrometry (TOF-MS) platform in the agency branch in Ashland, OR, with which queries of unknown wood samples are investigated exclusively. Laboratories that operate different MS instruments also have an interest in using the database if they can produce valid matches to known samples compatible with the database. Four species were selected for inter-laboratory comparison using Orbitrap MS instruments and the equivalent TOF-MS platform with direct analysis in real time ionization of institution-sourced wood samples. Identities of the known samples were confirmed by examination of their microscopic wood anatomy. Orbitrap analysis was able to identify each species as confidently as the TOF instruments, often with less variation in spectra but not necessarily greater mass accuracy or better-matched signal abundance to the control database. The Orbitrap program also had to be doubled to two scanned mass ranges appended for greater peak intensity, before spectra could be correctly matched to the database, but the program was successful.

Sapwood and nikhra heartwood volatiles of two Combretaceae species traditionally used in Sudanese cosmetology and therapy

The smoke from Combretum hartmannianum and Terminalia laxiflora sapwood and fermented heartwood (nikhra) are used in body smoke baths and homemade skincare products in Sudan, which are believed to have various health benefits. In this study, the volatiles of sapwood and nikhra were collected using two extraction techniques: a Mono-TrapTM sampling technique (at low temperature) and a smoke trapping system (at high temperature). After GC-MS analysis, the chromatogram of each sample revealed numerous peaks. The detected peaks were identified using the DIST08 mass spectral library database, resulting in known and unknown compounds. The results showed that 1,2,4,5-tetrachloro-3,6-dimethoxybenzene was the major compound in the Mono-Trap and smoke extracts from nikhra samples of C. hartmannianum and T. laxiflora. In contrast, the dominant compounds in the Mono-Trap extracts of C. hartmannianum and T. laxiflora sapwoods were acetonyldimethylcarbinol and cadina-1(10),4-diene, respectively. The largest peaks in the chromatograms of the sapwood smoke extracts of both species corresponded to unknown compounds. Some of the detected compounds are known for their aromatic and pharmaceutical properties. Identifying these aromatic compounds in nikhra and not in sapwood might prove the traditional Sudanese beliefs, and it could potentially improve the manufacturing of health products.

Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

A Portable and Reusable Database Infrastructure for Mass Spectrometry, and Its Associated Toolkit (The DIMSpec Project).

Nontargeted analysis (NTA) is a rapidly growing field of techniques that includes the identification of unknown chemical analytes in complex mixtures such as environmental, biological, and food matrices. The use of reference mass spectral databases is a key component of most NTA workflows, providing a high level of confidence for chemical identification when analytical standards are not available, yet effective interlaboratory sharing of research grade spectra remains challenging. The Database Infrastructure for Mass Spectrometry (DIMSpec) project focused on the creation of an open-source toolkit supporting storage and sharing of high-resolution mass spectra with attached sample and methodological metadata. As a demonstration of its utility, the DIMSpec toolkit was used to create a database of curated mass spectra for per- and polyfluoroalkyl substances (PFAS) generated from various sources. While the underlying toolkit is agnostic to analytical targets, this initial release (along with the database schema, mass spectral data, and database tools) should enable PFAS researchers to use these data for their own studies, including the identification of novel PFAS in the environment.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Identification of environmental Actinobacteria in buildings by means of chemotaxonomy, 16S rRNA sequencing, and MALDI-TOF MS.

The manuscript addresses the challenges in identifying environmental bacteria using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) Biotyper-based protein profiling. The matter of the studies-actinobacterial strains-has been isolated mostly from building materials that originated from a confirmed moisture-damaged situation. Polyphasic taxonomy, 16S RNA gene sequencing, and MALDI-TOF mass spectrometry were applied for identification purposes. In this experimental paper, a few important facts are highlighted. First, Actinobacteria are abundant in the natural as well as built environment, and their identification on the species and genus levels is difficult and time-consuming. Second, MALDI-TOF MS is an effective tool for identifying bacterial environmental strains, and in parallel, continuous enrichment of the proteomics mass spectral databases is necessary for proper identification. Third, the chemical approach aids in the taxonomical inquiry of Actinobacteria environmental strains.

Mass Spectral Library for DNA Adductomics.

Endogenous electrophiles, ionizing and non-ionizing radiation, and hazardous chemicals present in the environment and diet can damage DNA by forming covalent adducts. DNA adducts can form in critical cancer driver genes and, if not repaired, may induce mutations during cell division, potentially leading to the onset of cancer. The detection and quantification of specific DNA adducts are some of the first steps in studying their role in carcinogenesis, the physiological conditions that lead to their production, and the risk assessment of exposure to specific genotoxic chemicals. Hundreds of different DNA adducts have been reported in the literature, and there is a critical need to establish a DNA adduct mass spectral database to facilitate the detection of previously observed DNA adducts and characterize newly discovered DNA adducts. We have collected synthetic DNA adduct standards from the research community, acquired MSn (n = 2, 3) fragmentation spectra using Orbitrap and Quadrupole-Time-of-Flight (Q-TOF) MS instrumentation, processed the spectral data and incorporated it into the MassBank of North America (MoNA) database, and created a DNA adduct portal Web site (https://sites.google.com/umn.edu/dnaadductportal) to serve as a central location for the DNA adduct mass spectra and metadata, including the spectral database downloadable in different formats. This spectral library should prove to be a valuable resource for the DNA adductomics community, accelerating research and improving our understanding of the role of DNA adducts in disease.

Analytical Study of Dashamoolakatu Treyam Kvatha Churna

Aim: For the purpose of analyzing the active principle in the obtained extracts of the herbs, GC-MS is considered as an important tool. This study includes two techniques where the components is separated by Gas chromatography and the mass spectroscopy does the analysis of the active component. As a part of such an analytical aspect, Dashamoolakayu treyam Kvatha Churna is subjected to GC-MS to study their bioactive compounds. Methodology: 1g of sample was extracted with 10mL of methanol and was filtered through a syringe filter (Nylon 13 mm 0.2um) and injected to GCMS. The components of the oil were identified by comparison of their mass spectra with those of the spectrometer/ mass spectral database using NIST library (NIST -08 SPECTRAL DATA). The identifications were confirmed by comparison of the fragmentation pattern and their retention indices with those reported in the literature. Result: Result shows three peaks with R.T mins as 7.689, 7.964, 8.841 and the compounds are identified are α-Copaene (Tricyclo [4.4.0.02,7] dec-3-ene, 1,3-dimethyl- 8- (1-methylethyl)-, stereoisomer), Caryophyllene, Caryophyllene oxide with peak heights as 17768, 104199, 39458 respectively. Discussion: Identified components has antioxidant, Analgesic and anti-inflammatory activity and are used in the therapy of inflammatory diseases of the upper airways and also in the management of acute and chronic infectious and inflammatory diseases of different localization. Conclusion: Thus, this GC-MS study indicates the scientific validation of Dashamoolakatu treyam Kvatha Churna as a potent medicine as claimed by ayurvedic literature and practice.

Off-odour Identification from Volatile Organic Compounds (VOCs) of Spirulina

Spirulina platensis is a common cyanobacteria microalga with high nutrition and bioactive compound sources. The addition of spirulina in foods and beverages improves nutrition and bioactive compound content. However, certain species of cyanobacteria are known to produce various compounds causing off-odour. This study investigates the chemical profile and volatile organic compounds (VOCs) in spirulina biomass and determine off-odour potency. The spirulina extract was analysed phytochemical qualitatively and GC-MS (Gas Chromatography-Mass Spectrometry). The Spectra mass was compared to the mass spectral database and profile of chemical compound libraries. The result shows, phytochemical analysis positively contains of alkaloids, flavonoids, tannins, saponins, and terpenoids. A total of 155 volatile compounds consisting of classes acid, alcohol, aldehyde, alkene, benzene, ether, ester, ketone, sulphur-contain, and terpene were identified. The off-odour VOC content such as phytol; cyclopropanebutanoic acid, 2- [[2 - [[2 - [(2- pentylcyclopropyl) methyl) cyclopropyl) methyl) cyclopropyl) methyl]-, methyl ester; 3.7.11.15-tetramethyl-2-hexadecen-1-ol; Imidazole, 2-fluoro-5-(2-carboxyvinyl)-; β-ionone; and N,N-Dimethyl-O-(1-methyl-butyl)-hydroxylamine, were detected in spirulina. The odour descriptions of off-odour VOCs are floral, balsamic, powdery, waxy, rancid, sweaty, woody, alkali, and fish-like. The off-odour content of VOCs might influence food's sensory odour, with spirulina added in excessive quantities.

Toward Comprehensive Analysis of the 3D Chemistry of Pseudomonas aeruginosa Biofilms.

Bacterial biofilms are structured communities consisting of cells enmeshed in a self-generated extracellular matrix usually attached to a surface. They contain diverse classes of molecules including polysaccharides, lipids, proteins, nucleic acids, and diverse small organic molecules (primary and secondary metabolites) which are organized to optimize survival and facilitate dispersal to new colonization sites. In situ characterization of the chemical composition and structure of bacterial biofilms is necessary to fully understand their development on surfaces relevant to biofouling in health, industry, and the environment. Biofilm development has been extensively studied using confocal microscopy using targeted fluorescent labels providing important insights into the architecture of biofilms. Recently, cryopreparation has been used to undertake targeted in situ chemical characterization using Orbitrap secondary ion mass spectrometry (OrbiSIMS), providing a label-free method for imaging biofilms in their native state. Although the high mass resolution of OrbiSIMS enables more confident peak assignments, it is still very challenging to assign most of the peaks in the spectra due to complexity of SIMS spectra and lack of automatic peak assignment methods. Here, we analyze the same OrbiSIMS depth profile data generated from the frozen-hydrated biofilm, but employ a new untargeted chemical filtering process utilizing mass spectral databases to assign secondary ions to decipher the large number of fragments present in the SIMS spectra. To move towards comprehensive analysis of different chemistries in the sample, we apply a molecular formula prediction approach which putatively assigns 81% of peaks in the 3D OrbiSIMS depth profile analysis. This enables us to catalog over 1000 lipids and their fragments, 3500 protein fragments, 71 quorum sensing-related molecules (2-alkyl-4-quinolones and N-acylhomoserine lactones), 150 polysaccharide fragments, and glycolipids simultaneously from one data set and map these separated molecular classes spatially through a Pseudomonas aeruginosa biofilm. Assignment of different chemistries in this sample facilitates identification of differences between biofilms grown on biofilm-promoting and biofilm-resistant polymers.

Maintenance of cell integrity during hydroxyl radical rapid inactivation of Pseudanabaena sp. and simultaneous mineralization of odor compound 2-methylisoborneol

Pseudanabaena sp. and the odor compound it produces, 2-methylisoborneol (2-MIB), has been reportedly responsible for off-flavor pollution worldwide, leading to substandard drinking water sensory indicators and serious water supply crises. In this paper, the hydroxyl radical (•OH) produced by the synergistic effect of strong ionization discharge and hydrodynamic cavitation rapidly inactivated Pseudanabaena sp. and simultaneously mineralized 2-MIB to a concentration of 2.57 ng/L, which is below the odor threshold of 10 ng/L for a total reactive oxidants (TRO) concentration of 1.2 mg/L within 12 s. Crucially, the intracellular 2-MIB level was maintained in approximately 155.26– 162.29 ng/L range, indicating that 2-MIB was not released from the cells. Based on the scanning electron microscopy (SEM) results, the integrity of Pseudanabaena sp. cells was maintained with intact membranes and no intracellular organic matters (IOM) released during •OH inactivation. In contrast, ClO2 caused severe membrane rupture and massive IOM release. Based on the gas chromatograph/mass spectrometer (GC/MS) analyses and mass spectral database, the chromatogram fitted the baseline with a TRO concentration of 4 mg/L and no peaks corresponding to intermediates were detected. Moreover, •OH could mineralize 2-MIB by opening the ring structures of 1,2,3,3-tetramethyl-4-cyclopentenone, neomenthol, and 2-methylcyclohexene-1-aldehyde to produce small-molecule compounds, finally leading to CO2 and H2O formation via three reaction pathways. Therefore, the •OH not only maintained the cell integrity of Pseudanabaena sp. during inactivation but also mineralized 2-MIB simultaneously.

Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches.

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds. Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database. Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples. Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

Toward Machine Learning-Driven Mass Spectrometric Identification of Trichothecenes in the Absence of Standard Reference Materials.

While a significant body of work exists on the detection of commonly known trichothecene toxins, biological, environmental, and other transformational processes can generate many under-characterized and unknown modified trichothecenes. Lacking both analytical reference standards and associated mass spectral databases, identification of these modified compounds reflects both a challenge and a critical gap from forensic and public health perspectives. We report here the application of machine learning (ML) techniques toward identification of discriminative fragment ions from mass spectrometric data that can be exploited to detect evidence of type A and B trichothecenes. The goal of this work is to establish a new method for the identification of unknown, though structurally similar trichothecenes, by leveraging objective ML techniques. Discriminative fragments derived from a series of gradient-boosted machine learners are then used to develop ML-driven precursor ion scan (PIS) methods on a triple quadrupole mass spectrometer (QQQ) for screening of "unknown unknown" trichothecenes. Specifically, we apply the PIS method to a laboratory-synthesized trichothecene, a first step in demonstrating the power of alternative, machine learning-driven mass spectrometric methods.

Vulnerability of wells in unconfined and confined aquifers to modern contamination from flood events

Groundwater is a primary potable water supply for coastal North Carolina (NC), but the increased intensity of extreme rainfall events and floods may exacerbate surface and subsurface processes that contribute anthropogenic chemicals to wells in the major confined aquifers of this region. We evaluated groundwater for organic chemicals of emerging concern (CEC) and the presence of tritium using flooded and not-flooded wells in the NC Department of Environmental Quality well monitoring network across the NC Coastal Plain. Flooded wells experienced standing water around the well casing at least once during the study period. Tritium concentrations, which indicate modern water presence (water recharged after 1953), were significantly greater in groundwater from flooded wells than not-flooded wells. In confined aquifers, modern water was detected at greater depths in flooded wells (206 m) than not-flooded wells (100 m). Suspect-screening high resolution mass spectrometry (HRMS) analysis of 150 groundwater samples yielded a total of 382 unique organic chemicals. Each groundwater sample contained, on average, 19 tentatively identified chemicals from the NIST 20 mass spectral database (M1) and 9 USEPA ToxCast chemicals. The number of tentatively-identified chemicals per sample was not significantly different among aquifers demonstrating the pervasive presence of the detected CECs in unconfined and confined aquifers. The presence of modern water in groundwater from flooded wells coincided with higher detection frequencies of certain organic contaminant classes, particularly pharmaceuticals, food additives, and regulated aromatic hydrocarbons. These results indicate that wells in both unconfined and confined aquifers are susceptible to modern water contamination during flood events; this finding has critical public health implications for coastal communities.

Gas chromatography – Mass spectrometry (GC-MS) profiling reveals newly described bioactive compounds in Citrullus colocynthis (L.) seeds oil extracts

Citrullus colocynthis (L.) (C. colocynthis), commonly known as Handal in Yemen and other Arabic countries, is a plant with a wide range of pharmacological properties. These properties are attributed to secondary metabolites, known as phytochemicals, present in the plant. In this study, the seed of C. colocynthis were extracted using dichloromethane, and the resulting oil extract was screened to identify active phytoconstituents using gas chromatography – Mass spectrometry (GC-MS). The gas chromatography – Mass spectrometry is used to identification of the phytoconstituents and the spectrum of unknown compounds were compared with the compounds stored in the National Institute of Standards and Technology Mass Spectral database (NIST) and WILEY library of GC-MS. A total of fifty five compounds appeared in GC-MS chromatogram, twenty-four major bioactive compounds were identified in the present study. The major components of the oil extract were found to be carotenes, phenols, esters, and steroids. From the GC-MS chromatogram of dichloromethane seeds oil extract, some of the identified components possess pharmacological actions as per information available in the literature. One of the major component identified in the C. colocynthis seed oil extract was isooctylphthalate (58%), which exhibit strong antimicrobials effect. Therefore, C. colocynthis is considered to be a natural source of isooctylphthalate. From the results, this study is the first to report the presence of various bioactive components of phytopharmaceutical importance in C. colocynthis.

MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry

Mapping the chemical space of compounds to chemical structures remains a challenge in metabolomics. Despite the advancements in untargeted liquid chromatography-mass spectrometry (LC–MS) to achieve a high-throughput profile of metabolites from complex biological resources, only a small fraction of these metabolites can be annotated with confidence. Many novel computational methods and tools have been developed to enable chemical structure annotation to known and unknown compounds such as in silico generated spectra and molecular networking. Here, we present an automated and reproducible Metabolome Annotation Workflow (MAW) for untargeted metabolomics data to further facilitate and automate the complex annotation by combining tandem mass spectrometry (MS2) input data pre-processing, spectral and compound database matching with computational classification, and in silico annotation. MAW takes the LC-MS2 spectra as input and generates a list of putative candidates from spectral and compound databases. The databases are integrated via the R package Spectra and the metabolite annotation tool SIRIUS as part of the R segment of the workflow (MAW-R). The final candidate selection is performed using the cheminformatics tool RDKit in the Python segment (MAW-Py). Furthermore, each feature is assigned a chemical structure and can be imported to a chemical structure similarity network. MAW is following the FAIR (Findable, Accessible, Interoperable, Reusable) principles and has been made available as the docker images, maw-r and maw-py. The source code and documentation are available on GitHub (https://github.com/zmahnoor14/MAW). The performance of MAW is evaluated on two case studies. MAW can improve candidate ranking by integrating spectral databases with annotation tools like SIRIUS which contributes to an efficient candidate selection procedure. The results from MAW are also reproducible and traceable, compliant with the FAIR guidelines. Taken together, MAW could greatly facilitate automated metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery.

Construction of Chemical Libraries of Volatile Compounds by Combinatorial Synthesis of Homologous Mixtures: Alk-4-en-1-ols, Alk-4-enals and Methyl Alk-4-enoates.

A compound library of sixty six linear compounds, eleven representatives of six molecular families: (E)- and (Z)-isomers of alk-4-en-1-ols, alk-4-enals, and methyl alk-4-enoates, was prepared by combinatorial syntheses to allow the creation of a mass spectral database directly usable for their identification in GC/MS analyses. We demonstrate here that compound libraries can be prepared by combinatorial syntheses using long linear synthetic sequences, i. e., eight step in the case of 4-enals. The resulting mixtures of homologues are still perfectly exploitable to deliver the requested information such as clean mass spectra and good gas chromatographic retention indices.