Investigation of changes in rat’s blood metabolomic profile, caused by lead exposure

Introduction. The prevalence of lead in the environment, due to human production and economic activities, and the xenobiotic nature of the element substantiate the relevance of studying the changes caused by the action of this metal. Materials and methods. A non-target metabolomic screening of the blood of rats exposed to intraperitoneal administration of lead acetate by HPLC-mass spectrometry was carried out. The expression of the selected masses was compared with those for the control group of animals. The masses that significantly changed the intensity compared to the control were subjected to fragmentation to obtain characteristic fragments. The annotation of metabolites was performed by searching in MS/MS databases and by comparison with in silico fragmentation spectra. The involvement of annotated metabolites in metabolic processes was established by literature analyzing. Results. Non-target metabolomic screening revealed 37 m/z values for the exposed group, significantly changing the intensity compared to the control. Annotation using fragmentation spectra and in silico fragmentation allows establishing the structure of eight metabolites, including an epoxy derivative of linolic acid, 15-hydroxyeicosatetraenoic acid, four oxo- and hydroxyacylcarnitine derivatives of long-chain fatty acids, one acylcarnitine derivatives of medium-chain fatty acids and one lysophosphoserine. Conclusion. Analyzing the literature, the known functions of the identified metabolites were established and attributed to the known metabolic processes. So, oxo- and hydroxyacylcarnitines are derivatives for intermediate products of β-oxidation fatty acids - it is increased concentration compared to the control indicates a violation of this process under the influence of oxidative stress caused by lead. Epoxy and 15-hydroxy derivatives of fatty acids (increased content relative to the control group) act as regulatory metabolites (vasodynamic activity), on the one hand, and markers of lead-induced hypoxia on the other hand. The increase of the concentration for the lysophosphatidylserine derivative indicates the intensification of apoptotic processes in the organism of the exposed group in contrast to the control.

Radical Fragment Ions in Collision-Induced Dissociation Mass Spectrometry

Collision-induced dissociation (CID) is a common fragmentation strategy in mass spectrometry (MS) analysis. A conventional understanding is that fragment ions generated in low-energy CID should follow the even-electron rule. As such, (de)protonated precursor ions should predominately generate (de)protonated fragment ions with very few radical fragment ions (RFIs). However, the extent to which RFIs present in MS2 spectra has not been comprehensively investigated. This work uses the latest NIST 20 tandem mass spectral library to investigate of the occurrence of RFIs in CID MS2 experiments. In particular, RFIs were recognized using their integer double bond equivalent (DBE) values calculated from their annotated molecular formulas. Our study shows unexpected results as 65.4% and 68.8% of MS2 spectra contain at least 10% RFIs by ion-count (total number of ions) in positive and negative electrospray ionization (ESI) modes, respectively. Furthermore, we classified chemicals based on their compound classes and chemical substructures, and calculated the percentages of RFIs in each class. Results show that “Organic 1,3-dipolar compounds” and “Lignans, neolignans and related compounds” are the top 2 compound superclasses which tend to produce RFIs in their CID MS2 spectra. Moreover, aromatic, arylbromide, heteroaromatic, alkylarylether, phenol, and conjugated double bond-containing chemicals are more likely to produce RFIs. We also found four possible patterns of change in RFI percentages as a function of CID collision energy. Finally, we demonstrate that the inadequate consideration of RFIs in most conventional bioinformatic tools might cause problems during in silico fragmentation and de novo annotation of MS2 spectra. This work provides a further understanding of CID MS2 mechanism, and the unexpectedly large percentage of RFIs suggests a need for consideration in the development of bioinformatic software for MS2 interpretation.

Investigation and Resolution of Interference in the LC-QTOF-MS Detection of 4-MePPP

An interference resulting in the false-positive detection of the synthetic cathinone 4-MePPP in urine was suspected following the recent addition of 4-MePPP spectral data to an LC-QTOF-MS drug library. Although positive detection criteria were achieved, it was noted that all urine samples suspected of containing 4-MePPP also concurrently contained high levels of tramadol and its associated metabolites. Using QTOF-MS software elucidation tools, candidate compounds for the suspected interference were proposed. To provide further confidence in the identity of the interference, in silico fragmentation tools were used to match product ions generated in the analysis with product ions predicted from the theoretical fragmentation of candidate compounds. The ability of the suspected interference to subsequently produce the required product ions for spectral library identification of 4-MePPP was also tested. This information was used to provide a high preliminary confidence in the compound identity prior to purchase and subsequent confirmation with certified reference material. A co-eluting isobaric interference was identified and confirmed as an in-source fragment of the tramadol metabolite, N,N-bisdesmethyltramadol. Proposed resolutions for this interference are also described and subsequently validated by retrospective interrogation of previous cases of suspected interference.

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches

Motivation: Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.