Combining the targeted and untargeted screening of environmental contaminants reveals associations between PFAS exposure and vitamin D metabolism in human plasma.

We have developed, validated, and applied a method for the targeted and untargeted screening of environmental contaminants in human plasma using liquid chromatography high-resolution mass spectrometry (LC-HRMS). The method was optimized for several classes of environmental contaminants, including PFASs, OH-PCBs, HBCDs, and bisphenols. One-hundred plasma samples from blood donors (19-75 years, men n = 50, women n = 50, from Uppsala, Sweden) were analyzed. Nineteen targeted compounds were detected across the samples, with 18 being PFASs and the 19th being OH-PCB (4-OH-PCB-187). Ten compounds were positively associated with age (in order of increasing p-values: PFNA, PFOS, PFDA, 4-OH-PCB-187, FOSA, PFUdA, L-PFHpS, PFTrDA, PFDoA, and PFHpA; p-values ranging from 2.5 × 10-5 to 4.67 × 10-2). Three compounds were associated with sex (in order of increasing p-values: L-PFHpS, PFOS, and PFNA; p-values ranging from 1.71 × 10-2 to 3.88 × 10-2), all with higher concentrations in male subjects compared with female subjects. Strong correlations (0.56-0.93) were observed between long-chain PFAS compounds (PFNA, PFOS, PFDA, PFUdA, PFDoA, and PFTrDA). In the non-targeted data analysis, fourteen unknown features correlating with known PFASs were found (correlation coefficients 0.48-0.99). Five endogenous compounds were identified from these features, all correlating strongly with PFHxS (correlation coefficients 0.59-0.71). Three of the identified compounds were vitamin D3 metabolites, and two were diglyceride lipids (DG 24:6;O). The results demonstrate the potential of combining targeted and untargeted approaches to increase the coverage of compounds detected with a single method. This methodology is well suited for exposomics to detect previously unknown associations between environmental contaminants and endogenous compounds that may be important for human health.

Oceanographic setting influences the prokaryotic community and metabolome in deep-sea sponges.

Marine sponges (phylum Porifera) are leading organisms for the discovery of bioactive compounds from nature. Their often rich and species-specific microbiota is hypothesised to be producing many of these compounds. Yet, environmental influences on the sponge-associated microbiota and bioactive compound production remain elusive. Here, we investigated the changes of microbiota and metabolomes in sponges along a depth range of 1232 m. Using 16S rRNA gene amplicon sequencing and untargeted metabolomics, we assessed prokaryotic and chemical diversities in three deep-sea sponge species: Geodia barretti, Stryphnus fortis, and Weberella bursa. Both prokaryotic communities and metabolome varied significantly with depth, which we hypothesized to be the effect of different water masses. Up to 35.5% of microbial ASVs (amplicon sequence variants) showed significant changes with depth while phylum-level composition of host microbiome remained unchanged. The metabolome varied with depth, with relative quantities of known bioactive compounds increasing or decreasing strongly. Other metabolites varying with depth were compatible solutes regulating osmolarity of the cells. Correlations between prokaryotic community and the bioactive compounds in G. barretti suggested members of Acidobacteria, Proteobacteria, Chloroflexi, or an unclassified prokaryote as potential producers.

The Effects of Sampling and Storage Conditions on the Metabolite Profile of the Marine Sponge Geodia barretti.

Geodia barretti is a deep-sea marine sponge common in the north Atlantic and waters outside of Norway and Sweden. The sampling and subsequent treatment as well as storage of sponges for metabolomics analyses can be performed in different ways, the most commonly used being freezing (directly upon collection or later) or by storage in solvent, commonly ethanol, followed by freeze-drying. In this study we therefore investigated different sampling protocols and their effects on the detected metabolite profiles in liquid chromatography-mass spectrometry (LC-MS) using an untargeted metabolomics approach. Sponges (G. barretti) were collected outside the Swedish west coast and pieces from three sponge specimens were either flash frozen in liquid nitrogen, frozen later after the collection cruise, stored in ethanol or stored in methanol. The storage solvents as well as the actual sponge pieces were analyzed, all samples were analyzed with hydrophilic interaction liquid chromatography as well as reversed phase liquid chromatography with high resolution mass spectrometry using full-scan in positive and negative ionization mode. The data were evaluated using multivariate data analysis. The highest metabolite intensities were found in the frozen samples (flash frozen and frozen after sampling cruise) as well as in the storage solvents (methanol and ethanol). Metabolites extracted from the sponge pieces that had been stored in solvent were found in very low intensity, since the majority of metabolites were extracted to the solvents to a high degree. The exception being larger peptides and some lipids. The lowest variation between replicates were found in the flash frozen samples. In conclusion, the preferred method for sampling of sponges for metabolomics was found to be immediate freezing in liquid nitrogen. However, freezing the sponge samples after some time proved to be a reliable method as well, albeit with higher variation between the replicates. The study highlights the importance of saving ethanol extracts after preservation of specimens for biology studies; these valuable extracts could be further used in studies of natural products, chemosystematics or metabolomics.

Improved Sensitivity in Hydrophilic Interaction Liquid Chromatography-Electrospray-Mass Spectrometry after Removal of Sodium and Potassium Ions from Biological Samples.

Inorganic ions, such as sodium and potassium, are present in all biological matrices and are sometimes also added during sample preparation. However, these inorganic ions are known to hamper electrospray ionization -mass spectrometry (ESI-MS) applications, especially in hydrophilic interaction liquid chromatography (HILIC) where they are retained and can be detected as adducts and clusters with mobile phase components or analytes. The retention of inorganic ions leads to co-elution with analytes and as a result ion-suppression, extensive adduct formation and problems with reproducibility. In the presented work, a sample preparation method using cation exchange solid phase extraction (SPE) was developed to trap Na+ and K+ ions from human blood plasma and head and neck cancer cells for the analysis of small cationic, anionic as well as neutral organic analytes. The investigated analytes were small, hydrophilic compounds typically in focus in metabolomics studies. The samples were analyzed using full-scan HILIC-ESI-quadrupole time of flight (QTOF)-MS with an untargeted, screening approach. Method performance was evaluated using multivariate data analysis as well as relative quantifications, spiking of standards to evaluate linearity of response and post-column infusion to study ion-suppression. In blood plasma, the reduction of sodium and potassium ion concentration resulted in improved sensitivity increased signal intensity for 19 out of 28 investigated analytes, improved linearity of response, reduced ion-suppression and reduced cluster formation as well as adduct formation. Thus, the presented method has significant potential to improve data quality in metabolomics studies.

Exploring Radiation Response in Two Head and Neck Squamous Carcinoma Cell Lines Through Metabolic Profiling.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common form of cancer worldwide. Radiotherapy, with or without surgery, represents the major approach to curative treatment. However, not all tumors are equally sensitive to irradiation. It is therefore of interest to apply newer system biology approaches (e.g., metabolic profiling) in squamous cancer cells with different radiosensitivities in order to provide new insights on the mechanisms of radiation response. In this study, two cultured HNSCC cell lines from the same donor, UM-SCC-74A and UM-SCC-74B, were first genotyped using Short Tandem Repeat (STR), and assessed for radiation response by the means of clonogenic survival and growth inhibition assays. Thereafter, cells were cultured, irradiated and collected for subsequent metabolic profiling analyses using liquid chromatography-mass spectrometry (LC-MS). STR verified the similarity of UM-SCC-74A and UM-SCC-74B cells, and three independent assays proved UM-SCC-74B to be clearly more radioresistant than UM-SCC-74A. The LC-MS metabolic profiling demonstrated significant differences in the intracellular metabolome of the two cell lines before irradiation, as well as significant alterations after irradiation. The most important differences between the two cell lines before irradiation were connected to nicotinic acid and nicotinamide metabolism and purine metabolism. In the more radiosensitive UM-SCC-74A cells, the most significant alterations after irradiation were linked to tryptophan metabolism. In the more radioresistant UM-SCC-74B cells, the major alterations after irradiation were connected to nicotinic acid and nicotinamide metabolism, purine metabolism, the methionine cycle as well as the serine, and glycine metabolism. The data suggest that the more radioresistant cell line UM-SCC-74B altered the metabolism to control redox-status, manage DNA-repair, and change DNA methylation after irradiation. This provides new insights on the mechanisms of radiation response, which may aid future identification of biomarkers associated with radioresistance of cancer cells.

Adduct formation in electrospray ionisation-mass spectrometry with hydrophilic interaction liquid chromatography is strongly affected by the inorganic ion concentration of the samples.

Hydrophilic interaction liquid chromatography (HILIC)/ electrospray ionisation-mass spectrometry (ESI-MS) has gained interest for the analysis of polar analytes in bioanalytical applications in recent years. However, ESI-MS is prone to adduct formation of analytes. In contrast to reversed phase chromatography, small inorganic ions have retention in HILIC, i.e. analytes and inorganic ions may co-elute, which could influence the adduct formation. In the present paper, it was demonstrated that the co-elution of sodium ions or potassium ions and analytes in HILIC/ESI-MS affect the adduct formation and that different concentrations of sodium ions and potassium ions in biological samples could have an impact on the quantitative response of the respective adducts as well as the quantitative response of the protonated adduct. The co-elution also lead to cluster formation of analytes and sodium formate or potassium formate, causing extremely complicated spectra. In analytical applications using HILIC/ESI-MS where internal standards are rarely used or not properly matched, great care needs to be taken to ensure minimal variation of inorganic ion concentration between samples. Moreover, the use of alkali metal ion adducts as quantitative target ions in relative quantitative applications should be made with caution if proper internal standards are not used.

Method selectivity evaluation using the co-feature ratio in LC/MS metabolomics: Comparison of HILIC stationary phase performance for the analysis of plasma, urine and cell extracts.

Evaluation of the chromatographic separation in metabolomics studies has primarily been done using preselected sets of standards or by counting the number of detected features. An alternative approach is to calculate each feature's co-feature ratio, which is a combined selectivity measurement for the separation (i.e. extent of co-elution) and the MS-signal (i.e. adduct formation and in-source fragmentation). The aim of this study was to demonstrate how the selectivity of different HILIC stationary phases can be evaluated using the co-feature ratio approach. The study was based on three sample types; plasma, urine and cell extracts. Samples were analyzed on an UHPLC-ESI-Q-ToF system using an amide, a bare silica and a sulfobetaine stationary phase. For each feature, a co-feature ratio was calculated and used for multivariate analysis of the selectivity differences between the three stationary phases. Unsupervised PCA models indicated that the co-feature ratios were highly dependent on type of stationary phase. For several metabolites a 15-30 fold difference in the co-feature ratio were observed between the stationary phases. Observed selectivity differences related primarily to the retention patterns of unwanted matrix components such as inorganic salts (detected as salt clusters), glycerophospholipids, and polyethylene glycols. These matrix components affected the signal intensity of co-eluting metabolites by interfering with the ionization efficiency and/or their adduct formation. Furthermore, the retention pattern of these matrix components had huge influence on the number of detected features. The co-feature ratio approach has successfully been applied for evaluation of the selectivity performance of three HILIC stationary phases. The co-feature ratio could therefore be used in metabolomics for developing selective methods fit for their purpose, thereby avoiding generic analytical approaches, which are often biased, as type and amount of interfering matrix components are metabolome dependent.

TAp73 is a marker of glutamine addiction in medulloblastoma.

Medulloblastoma is the most common solid primary brain tumor in children. Remarkable advancements in the understanding of the genetic and epigenetic basis of these tumors have informed their recent molecular classification. However, the genotype/phenotype correlation of the subgroups remains largely uncharacterized. In particular, the metabolic phenotype is of great interest because of its druggability, which could lead to the development of novel and more tailored therapies for a subset of medulloblastoma. p73 plays a critical role in a range of cellular metabolic processes. We show overexpression of p73 in a proportion of non-WNT medulloblastoma. In these tumors, p73 sustains cell growth and proliferation via regulation of glutamine metabolism. We validated our results in a xenograft model in which we observed an increase in survival time in mice on a glutamine restriction diet. Notably, glutamine starvation has a synergistic effect with cisplatin, a component of the current medulloblastoma chemotherapy. These findings raise the possibility that glutamine depletion can be used as an adjuvant treatment for p73-expressing medulloblastoma.