Host macrocyclic acylcarnitines mediate symbiotic interactions between frogs and their skin microbiome.

The host-microbiome associations occurring on the skin of vertebrates significantly influence hosts' health. However, the factors mediating their interactions remain largely unknown. Herein, we used integrated technical and ecological frameworks to investigate the skin metabolites sustaining a beneficial symbiosis between tree frogs and bacteria. We characterize macrocyclic acylcarnitines as the major metabolites secreted by the frogs' skin and trace their origin to an enzymatic unbalance of carnitine palmitoyltransferases. We found that these compounds colocalize with bacteria on the skin surface and are mostly represented by members of the Pseudomonas community. We showed that Pseudomonas sp. MPFS isolated from frogs' skin can exploit acylcarnitines as its sole carbon and nitrogen source, and this metabolic capability is widespread in Pseudomonas. We summarize frogs' multiple mechanisms to filter environmental bacteria and highlight that acylcarnitines likely evolved for another function but were co-opted to provide nutritional benefits to the symbionts.

Hydrogen-Deuterium Addition and Exchange in N-Ethylmaleimide Reaction with Glutathione Detected by NMR Spectroscopy.

Glutathione (GSH) is an important and ubiquitous thiol compound abundantly present in virtually every living cell. It is a powerful antioxidant critically required to protect cells from oxidative damage and free radical injury. Its quantification in ex vivo analysis remains a major challenge because it spontaneously oxidizes to form glutathione disulfide. N-Ethylmaleimide (NEM) is a well-known Michael acceptor, which reacts rapidly and irreversibly with thiol and prevents disulfide bond formation. Based on thiol conjugation to NEM, recently, the concentration of GSH was determined in human blood using NMR spectroscopy [Anal. Chem, 2021, 93(44): 14844-14850]. It was found that hydrogen-deuterium addition and exchange occur during the thiol-maleimide reaction as well as NMR analysis, generating a series of poorly explored diastereomers/isotopomers. Here, we establish a general NMR approach to identify the thiosuccinimide diastereomers/isotopomers derived from the thiol-maleimide reaction. The thiol-Michael addition reaction was conducted for GSH and another thiol compound, cysteine, separately, using D2O and H2O. The conjugates were characterized by 1H/13C 1D/2D NMR under different solvent, buffer, and pH conditions. The Michael addition combined with the H/D exchange formed twelve unique diastereomers/isotopomers. NMR measurements allowed the distinct assignment of all structures in solutions and quantification of H/D addition and exchange. Interestingly, the deuterium exchange rate was dependent on structure, pH, and buffer. The elucidation of the thiol-maleimide reaction and H/D exchange mechanism can potentially impact areas including metabolomics, small molecule synthesis, and bioconjugation chemistry.

Expanding Urinary Metabolite Annotation through Integrated Mass Spectral Similarity Networking.

The urine metabolome constitutes a rich source of functional information reflecting physiological states that are influenced by distinct conditions and biological stresses, such as responses to drug treatments or disease manifestations. Although global liquid chromatography-mass spectrometry (MS) profiling provides the most comprehensive measurement of metabolites in complex biological samples, annotation remains a challenge, and computational approaches are necessary to translate the molecular composition into biological knowledge. Here, we investigated the use of tandem MS-based enhanced molecular networks (MolNetEnhancer) to improve the metabolite annotation of urine extracts. The samples (n = 10) were analyzed by hydrophilic interaction chromatography-quadrupole time-of-flight mass spectrometry in both electrospray ionization (ESI) modes. Consistent with other common data preprocessing software, the use of Progenesis QI led to the annotation of up to 20 metabolites based on MS2 library searches, showing a high fragmentation score (cosine similarity ≥ 0.7), that is, ∼2% of mass features containing MS2 spectra. Molecular networking based on library matching resulted in the annotation of up to 62 urinary compounds. Using a combination of unsupervised substructure discovery (MS2LDA), the in silico tool network annotation propagation (NAP), and ClassyFire chemical ontology, embedded in a multilayered molecular network by MolNetEnhancer, we were able to expand the chemical characterization to ∼50% of the data set. The integrative approach led to the annotation of 275 compounds at the metabolomics standards initiative (MSI) confidence level 2, as well as 459 and 578 urinary metabolites (MSI level 3) in both negative and positive ESI modes, respectively. The exhaustive MS2-based annotation outperformed similar studies applied to larger cohorts while offering the discovery of metabolites not identified by the MS2 library search. This is the first work that effectively integrates orthogonal annotation methods and MS2-based fragmentation studies to improve metabolite annotation in urine samples.

Glucocerebrosidase reduces the spread of protein aggregation in a Drosophila melanogaster model of neurodegeneration by regulating proteins trafficked by extracellular vesicles.

Abnormal protein aggregation within neurons is a key pathologic feature of Parkinson's disease (PD). The spread of brain protein aggregates is associated with clinical disease progression, but how this occurs remains unclear. Mutations in glucosidase, beta acid 1 (GBA), which encodes glucocerebrosidase (GCase), are the most penetrant common genetic risk factor for PD and dementia with Lewy bodies and associate with faster disease progression. To explore how GBA mutations influence pathogenesis, we previously created a Drosophila model of GBA deficiency (Gba1b) that manifests neurodegeneration and accelerated protein aggregation. Proteomic analysis of Gba1b mutants revealed dysregulation of proteins involved in extracellular vesicle (EV) biology, and we found altered protein composition of EVs from Gba1b mutants. Accordingly, we hypothesized that GBA may influence pathogenic protein aggregate spread via EVs. We found that accumulation of ubiquitinated proteins and Ref(2)P, Drosophila homologue of mammalian p62, were reduced in muscle and brain tissue of Gba1b flies by ectopic expression of wildtype GCase in muscle. Neuronal GCase expression also rescued protein aggregation both cell-autonomously in brain and non-cell-autonomously in muscle. Muscle-specific GBA expression reduced the elevated levels of EV-intrinsic proteins and Ref(2)P found in EVs from Gba1b flies. Perturbing EV biogenesis through neutral sphingomyelinase (nSMase), an enzyme important for EV release and ceramide metabolism, enhanced protein aggregation when knocked down in muscle, but did not modify Gba1b mutant protein aggregation when knocked down in neurons. Lipidomic analysis of nSMase knockdown on ceramide and glucosylceramide levels suggested that Gba1b mutant protein aggregation may depend on relative depletion of specific ceramide species often enriched in EVs. Finally, we identified ectopically expressed GCase within isolated EVs. Together, our findings suggest that GCase deficiency promotes accelerated protein aggregate spread between cells and tissues via dysregulated EVs, and EV-mediated trafficking of GCase may partially account for the reduction in aggregate spread.

Persistent metabolomic alterations characterize chronic critical illness after severe trauma.

BACKGROUND: Following trauma, persistent inflammation, immunosuppression, and catabolism may characterize delayed recovery or failure to recover. Understanding the metabolic response associated with these adverse outcomes may facilitate earlier identification and intervention. We characterized the metabolic profiles of trauma victims who died or developed chronic critical illness (CCI) and hypothesized that differences would be evident within 1-week postinjury. METHODS: Venous blood samples from trauma victims with shock who survived at least 7 days were analyzed using mass spectrometry. Subjects who died or developed CCI (intensive care unit length of stay of ≥14 days with persistent organ dysfunction) were compared with subjects who recovered rapidly (intensive care unit length of stay, ≤7 days) and uninjured controls. We used partial least squares discriminant analysis, t tests, linear mixed effects regression, and pathway enrichment analyses to make broad comparisons and identify differences in metabolite concentrations and pathways. RESULTS: We identified 27 patients who died or developed CCI and 33 who recovered rapidly. Subjects were predominantly male (65%) with a median age of 53 years and Injury Severity Score of 36. Healthy controls (n = 48) had similar age and sex distributions. Overall, from the 163 metabolites detected in the samples, 56 metabolites and 21 pathways differed between injury outcome groups, and partial least squares discriminant analysis models distinguished injury outcome groups as early as 1-day postinjury. Differences were observed in tryptophan, phenylalanine, and tyrosine metabolism; metabolites associated with oxidative stress via methionine metabolism; inflammatory mediators including kynurenine, arachidonate, and glucuronic acid; and products of the gut microbiome including indole-3-propionate. CONCLUSIONS: The metabolic profiles in subjects who ultimately die or develop CCI differ from those who have recovered. In particular, we have identified differences in markers of inflammation, oxidative stress, amino acid metabolism, and alterations in the gut microbiome. Targeted metabolomics has the potential to identify important metabolic changes postinjury to improve early diagnosis and targeted intervention. LEVEL OF EVIDENCE: Prognostic/epidemiologic, level III.

Determination of phenolic compounds, in vitro antioxidant activity and characterization of secondary metabolites in different parts of Passiflora cincinnata by HPLC-DAD-MS/MS analysis.

Ethanol extracts of different parts of Passiflora cincinnata were obtained by maceration. The total phenolic and flavonoid contents were evaluated. The antioxidant activities were determined by ß-carotene-linoleic acid bleaching test, 2,2-diphenyl-1-picrylhydrazil (DPPH), and 2,2'-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical scavenging. The crude ethanol stem extract showed the highest amount of total polyphenols (45.53 mg gallic acid equivalent/g) while the highest total flavonoid contents (1.42 mg of quercetin equivalent/g) were observed in the leaf extract. The lowest IC50 (25.65 µg/ml) by the DPPH method was observed for the stem extract. The ABTS method showed a significant antioxidant activity for all investigated extracts. The secondary metabolite composition of ethanol extracts was assessed by HPLC-DAD-MS/MS analysis, leading to the identification of fourteen secondary metabolites in P. cincinnata extracts. These results showed the potentiality of this species as a source of phenolic compounds and antioxidants.

The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery.

Despite rapid evolution in the area of microbial natural products chemistry, there is currently no open access database containing all microbially produced natural product structures. Lack of availability of these data is preventing the implementation of new technologies in natural products science. Specifically, development of new computational strategies for compound characterization and identification are being hampered by the lack of a comprehensive database of known compounds against which to compare experimental data. The creation of an open access, community-maintained database of microbial natural product structures would enable the development of new technologies in natural products discovery and improve the interoperability of existing natural products data resources. However, these data are spread unevenly throughout the historical scientific literature, including both journal articles and international patents. These documents have no standard format, are often not digitized as machine readable text, and are not publicly available. Further, none of these documents have associated structure files (e.g., MOL, InChI, or SMILES), instead containing images of structures. This makes extraction and formatting of relevant natural products data a formidable challenge. Using a combination of manual curation and automated data mining approaches we have created a database of microbial natural products (The Natural Products Atlas, www.npatlas.org) that includes 24 594 compounds and contains referenced data for structure, compound names, source organisms, isolation references, total syntheses, and instances of structural reassignment. This database is accompanied by an interactive web portal that permits searching by structure, substructure, and physical properties. The Web site also provides mechanisms for visualizing natural products chemical space and dashboards for displaying author and discovery timeline data. These interactive tools offer a powerful knowledge base for natural products discovery with a central interface for structure and property-based searching and presents new viewpoints on structural diversity in natural products. The Natural Products Atlas has been developed under FAIR principles (Findable, Accessible, Interoperable, and Reusable) and is integrated with other emerging natural product databases, including the Minimum Information About a Biosynthetic Gene Cluster (MIBiG) repository, and the Global Natural Products Social Molecular Networking (GNPS) platform. It is designed as a community-supported resource to provide a central repository for known natural product structures from microorganisms and is the first comprehensive, open access resource of this type. It is expected that the Natural Products Atlas will enable the development of new natural products discovery modalities and accelerate the process of structural characterization for complex natural products libraries.

Quantitative Method to Investigate the Balance between Metabolism and Proteome Biomass: Starting from Glycine.

The balance between metabolism and biomass is very important in biological systems; however, to date there has been no quantitative method to characterize the balance. In this methodological study, we propose to use the distribution of amino acids in different domains to investigate this balance. It is well known that endogenous or exogenous amino acids in a biological system are either metabolized or incorporated into free amino acids (FAAs) or proteome amino acids (PAAs). Using glycine (Gly) as an example, we demonstrate a novel method to accurately determine the amounts of amino acids in various domains using serum, urine, and cell samples. As expected, serum and urine had very different distributions of FAA- and PAA-Gly. Using Tet21N human neuroblastoma cells, we also found that Myc(oncogene)-induced metabolic reprogramming included a higher rate of metabolizing Gly, which provides additional evidence that the metabolism of proliferating cells is adapted to facilitate producing new cells. It is therefore anticipated that our method will be very valuable for further studies of the metabolism and biomass balance that will lead to a better understanding of human cancers.

Re-investigation of the fragmentation of protonated carotenoids by electrospray ionization and nanospray tandem mass spectrometry.

RATIONALE: Carotenoids are polyene isoprenoids with an important role in photosynthesis and photoprotection. Their characterization in biological matrices is a crucial subject for biochemical research. In this work we report the full fragmentation of 16 polyenes (carotenes and xanthophylls) by electrospray ionization tandem mass spectrometry (ESI-CID-MS/MS) and nanospray tandem mass spectrometry (nanoESI-CID-MS/MS). METHODS: Analyses were carried out on a quadrupole time-of-flight (QTOF) mass spectrometer coupled with a nanoESI source and on a Fourier transform ion cyclotron resonance (FTICR) mass spectrometer with an ESI source. The formulae of the product ions were determined by accurate-mass measurements. RESULTS: It is demonstrated that the fragmentation routes observed for the protonated carotenoids derive essentially from charge-remote fragmentations and pericyclic rearrangements, such as electrocyclic and retro-ene eliminations (assisted or not by a sigmatropic hydrogen shift). All mechanisms are dependent on cis-trans isomerization through the formation of several conjugated polyene carbocation intermediates. Some specific ions for the carotenoid epoxides were justified through formation of cyclic oxonium ions. CONCLUSIONS: Complete fragmentation pathways of protonated carotenoids by ESI- and nanoESI-CID-MS/MS provided structural information about functional groups, polyene chain and double bonds, and contribute to identification of carotenoids based on MS/MS fragmentation patterns. Copyright © 2016 John Wiley & Sons, Ltd.

RAMSY: ratio analysis of mass spectrometry to improve compound identification.

The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY (ratio analysis of nuclear magnetic resonance spectroscopy; Anal. Chem. 2011, 83, 7616-7623), which extracts the (1)H signals related to the same metabolite based on peak intensity ratios. On the basis of this concept, we present the ratio analysis of mass spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography/MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.