Urine adulteration to circumvent positive drug testing represents a problem for toxicological laboratories. While creatinine is a suitable marker for dilution, detection of chemicals is often performed by dipstick tests associated with high rates of false positives. Several methods would be necessary to check for all possible adulterants. Untargeted mass spectrometry (MS) methods used in metabolomics should theoretically allow detecting concentration changes of any endogenous urinary metabolite or presence of new biomarkers produced by chemical adulteration. As a proof of concept study, urine samples from 10 volunteers were treated with KNO and analyzed by high-resolution MS. For statistical data evaluation, XCMS and MetaboAnalyst were used. Compound identification was performed by database searches using an in-house database, Chemspider, METLIN, HMDB, and NIST. Principle component analysis revealed clear separation between treated and untreated urine samples. In detail, 307 features showed significant concentration changes with fold changes greater than 2 (79 decreased; 228 increased). Mainly amino acids (e.g., histidine, methylhistidine, di- and trimethyllysine) and purines (uric acid) were detected in lower amounts. 5-HO-isourate was found to be formed as a new compound from uric acid and, e.g., imidazole lactate concentrations increased due to the breakdown of histidine. This metabolomics-based strategy allowed for a broad identification range of markers of urinary adulteration. More studies will be needed to investigate routine applicability of identified potential markers exploring urinary conditions of their formation and stability. Selected markers might then be integrated into routine MS screening procedures allowing for detection of adulteration within routine MS analysis. Graphical Abstract ᅟ.

Over the past few years, the interest in metabolomics has increased in various fields including forensic toxicology. Forensic analysis typically requires a high degree of accuracy, which is often a problem in metabolomics applications. We aimed for a systematic evaluation of different analytical considerations of a metabolomics workflow allowing a targeted approach within an untargeted setup. Samples with 69 metabolites from different chemical classes were qualitatively and quantitatively analyzed on a high resolution quadrupole time of flight mass spectrometer coupled to liquid chromatography (UHPLC-QTOF). Three issues were addressed: (a) Two different approaches on "blind matrix" a simulated body fluid (SBF) and plasma-filtrate, were tested for calibration samples; (b) comparison of two different HPLC columns, reverse-phase (RP) and hydrophilic interaction chromatography (HILIC); and (c) comparison of three different acquisition modes (TOF-MS, information dependent data acquisition (IDA), and sequential window acquisition of all theoretical fragment-ion spectra (SWATH). Samples were measured repeatedly for method comparison based on sensitivity, accuracy, precision, and detection robustness. The blind matricesshowed similar accuracy for most analytes, while SBF provided an easier preparation with satisfying results. To cover a wide part of the human metabolome, a combination of RP and HILIC showed the best results. The different scan modes performed equally regarding metabolite quantification while TOF-MS was more sensitive but lacked MS/MS spectra generation. IDA and SWATH files were aligned to various databases where IDA showed good MS/MS spectra matches. SWATH seemed to be beneficial in detection rate but was incompatible with many important software tools in metabolomics.

Knowledge about when a bloodstain was deposited at a crime scene can be of critical value in forensic investigation. A donor of a genetically identified bloodstain could be linked to a suspected time frame and the crime scene itself. Determination of the time since deposition (TsD) has been extensively studied before but has yet to reach maturity. We therefore conducted a proof-of-principle study to study time- and storage-dependent changes of the proteomes of dried blood stains. A bottom-up proteomics approach was employed, and high-resolution liquid-chromatography–mass-spectrometry (HR-LC–MS) and data-independent acquisition (DIA) were used to analyze samples aged over a 2 month period and two different storage conditions. In multivariate analysis, samples showed distinct clustering according to their TsD in both principal component analysis (PCA) and in partial least square discriminant analysis (PLS DA). The storage condition alters sample aging and yields different separation-driving peptides in hierarchical clustering and in TsD marker peptide selection. Certain peptides and amino acid modifications were identified and further assessed for their applicability in assessing passed TsD. A prediction model based on data resampling (Jackknife) was applied, and prediction values for selected peptide ratios were created. Depending on storage conditions and actual sample age, mean prediction performances ranges in between 70 and 130% for the majority of peptides and time points. This places this study as a first in investigating LC–MS based bottom-up proteomics approaches for TsD determination.

Being able to attest when a bloodstain was deposited at a crime scene can be invaluable to a prosecution process, and methods to provide that information have long been desired. Determining the Time since Deposition (TsD) of a trace would allow placing a subject both in space and time to the crime scene-or prove that a trace left by that person was unrelated to it because it was deposited before or after the time the crime had occurred. To this day, no method for TsD determination has made its way into routine forensic casework, mainly because of the numerous challenges that await when trying to understand and account for all the influencing and confounding factors that affect the aging process (such as, e.g., temperature, UV-light exposure, or humidity). Here, we present an untargeted metabolomics-based study using liquid chromatography high-resolution mass spectrometry (LC-HR-MS) and data-dependent acquisition to analyze blood samples aged under two distinctly different storage conditions over 48 weeks. Global differences in age-and storage-dependent changes in blood metabolomes were described, and TsD-classification strategies based on qualitative and quantitative assessment of molecular features (MFs) have been proposed.Based on the selected criteria to best predict the TsD, the dipeptide Phenylalanylalanine (PheAla) can be considered as a promising candidate for TsD prediction. In essence, changes in the blood metabolome dynamics showed a strong association with increasing TsD, but significant differences depending on storage conditioning were observed, facilitating the need to study further the influence of individual influencing factors on TsD determination.

Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are important regulators of metabolism, making their receptors (GLP-1R and GIPR) attractive targets in the treatment of type 2 diabetes mellitus (T2DM). GLP-1R agonists are used clinically to treat T2DM but the use of GIPR agonists remains controversial. Recent studies suggest that simultaneous activation of GLP-1R and GIPR with a single peptide provides superior glycemic control with fewer adverse effects than activation of GLP-1R alone. We investigated the signaling properties of a recently reported dual-incretin receptor agonist (P18). GLP-1R, GIPR, and the closely related glucagon receptor (GCGR) were expressed in HEK-293 cells. Activation of adenylate cyclase via Gαs was monitored using a luciferase-linked reporter gene (CRE-Luc) assay. Arrestin recruitment was monitored using a bioluminescence resonance energy transfer (BRET) assay. GLP-1, GIP, and glucagon displayed exquisite selectivity for their receptors in the CRE-Luc assay. P18 activated GLP-1R with similar potency to GLP-1 and GIPR with higher potency than GIP. Interestingly, P18 was less effective than GLP-1 at recruiting arrestin to GLP-1R and was inactive at GCGR. These data suggest that P18 can act as both a dual-incretin receptor agonist, and as a G protein-biased agonist at GLP-1R.