DNA adductomics is a relatively new omics approach aiming to measure known and unknown DNA modifications, called DNA adducts. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) has become the most common method for analyzing DNA adducts. Recent advances in the field of mass spectrometry have allowed the possibility to perform a comprehensive analysis of adducts, for instance, by using a nontargeted data-independent acquisition method, with multiple precursor m/z windows as an inclusion list. However, the generated data are large and complex, and there is a need to develop algorithms to simplify and automate the time-consuming manual analysis that has hitherto been used. Here, a graphical user interface (GUI) program was developed, with the purpose of tracking a characteristic neutral loss reaction from tandem mass spectrometry of the nucleoside adducts. This program, called nLossFinder, was developed in the MATLAB platform, available as open-source code. Calf thymus DNA was used as a model for method optimization, and the overall adductomics approach was applied to DNA from amphipods (Monoporeia affinis) collected within the Swedish National Marine Monitoring Program. In the amphipod DNA, over 150 putative adducts were found in comparison to 18 using a manual approach in a previous study. The developed program can improve the processing time for large MS data, as it processes each sample in a few seconds, and hence can be applicable for high-throughput screening of adducts.

Background: Food allergies can substantially burden patients and families by negatively affecting finances, social relationships, and personal perceptions of health. This study was performed under the Finnish Allergy Programme aimed at reducing avoidance diets to foods in schoolchildren by 50%. The main goal of this study was to investigate how many children could be freed from diet restrictions in a Finnish school district through a diagnostic algorithm including component-resolved diagnostics and food challenge. The secondary aim was to provide a crude estimate of the burden of the elimination food diets in the region, and the savings associated with the proposed intervention.

Methods: A total of 205 children on a food avoidance diet according to the school register because of food allergy were invited into the study. One hundred and fifty-seven children were interviewed, tested for IgE to extracts and allergen components and food challenged in respective order.

Results: After two years, 12 children still had an avoidance diet and three of them were treated successfully with sOTI; the rest suspended their avoidance diet (n = 134) or dropped out of the study (n = 11). The cost of the elimination diets was estimated in 172 700euro per year at start and 13 200euro per year at the end of the study; total savings were 128 400euro yearly.

Conclusions: The results demonstrate a 65% reduction of avoidance diets to foods in school-aged children, exceeding the 50% aim of the Finnish Allergy Programme. Therefore, it is possible to actively reduce the number of food allergy diagnoses that remain unmonitored in the society through a tailored diagnostic work-up.

Random noise has been addressed as a cause of overfitting in partial least squares regression. A previous study pinpointed that one of the sources of overfitting resides in the calculation of scores due to the accumulation of noise in the diagonal of the variance-covariance matrix, and a modified partial least squares regression was proposed with the removal of this diagonal prior to the score calculation. Here, a further modification of the NIPALS algorithm is proposed, with the same ability to overcome overfitting due to noise, but algebraically more similar to the original NIPALS. The results indicate that it is possible to get more reliable auto-prediction R-2 with a cross-validation performance close to that of the original NIPALS algorithm.

Chromatographic retention time peak shifts between consecutive analyses is a well-known fact yet not fully understood. Algorithms have been developed to align peaks between runs, but with no specific studies considering the causes of peak shifts. Here, designed experiments reveal chromatographic shift patterns for a complex peptide mixture that are attributable to the temperature and pH of the mobile phase. These results demonstrate that peak shifts are highly structured and are to a high degree explained by underlying differences in physico-chemical parameters of the chromatographic system and also provide experimental support for the alignment algorithm called the generalized fuzzy Hough transform which exploits this fact. It can be expected that the development of alignment algorithms enters a new phase resulting in increasingly accurate alignment by considering the latent structure of the peak shifts.

The optimization of bioprocesses for biopharmaceutical manufacturing by Chinese hamster ovary (CHO) cells can be a challenging endeavor and, today, heavily relies on empirical methods treating the bioreactor process and the cells as black boxes. Multi-omics approaches have the potential to reveal otherwise unknown characteristics of these systems and identify culture parameters to more rationally optimize the cultivation process. Here, the authors have applied both metabolomic and proteomic profiling to a perfusion process, using CHO cells for antibody production, to explore how cell biology and reactor environment change as the cell density reaches 200x10(6)cellsmL(-1). The extracellular metabolic composition obtained in perfusion mode shows a markedly more stable profile in comparison to fed-batch, despite a far larger range of viable cell densities in perfusion. This stable profile is confirmed in the extracellular proteosome. Furthermore, the proteomics data shows an increase of structural proteins as cell density increases, which could be due to a higher shear stress and explain the decrease in cell diameter at very high cell densities. Both proteomic and metabolic results shows signs of oxidative stress and changes in glutathione metabolism at very high cell densities. The authors suggest the methodology presented herein to be a powerful tool for optimizing processes of recombinant protein production.

A non-target analysis method for unexpected contaminants in food is described. Many current methods referred to as non-target are capable of detecting hundreds or even thousands of contaminants. However, they will typically still miss all other possible contaminants. Instead, a metabolomics approach might be used to obtain true non-target analysis. In the present work, such a method was optimized for improved detection capability at low concentrations. The method was evaluated using 19 chemically diverse model compounds spiked into milk samples to mimic unknown contamination. Other milk samples were used as reference samples. All samples were analyzed with UHPLC-TOF-MS (ultra-high-performance liquid chromatography time-of-flight mass spectrometry), using reversed-phase chromatography and electrospray ionization in positive mode. Data evaluation was performed by the software TracMass 2. No target lists of specific compounds were used to search for the contaminants. Instead, the software was used to sort out all features only occurring in the spiked sample data, i.e., the workflow resembled a metabolomics approach. Procedures for chemical identification of peaks were outside the scope of the study. Method, study design, and settings in the software were optimized to minimize manual evaluation and faulty or irrelevant hits and to maximize hit rate of the spiked compounds. A practical detection limit was established at 25 mu g/kg. At this concentration, most compounds (17 out of 19) were detected as intact precursor ions, as fragments or as adducts. Only 2 irrelevant hits, probably natural compounds, were obtained. Limitations and possible practical use of the approach are discussed.

Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may be associated with emerging bioaccumulative contaminants. As a result, compounds with increasing concentrations are flagged while compounds displaying random, decreasing, or steady-state time trends are removed. As an initial proof of concept, we created artificial time trends by fortifying human whole blood samples with isotopically labelled standards. Different scenarios were investigated: eight model compounds had a continuously increasing trend in the last two to nine time points, and four model compounds had a trend that reached steady state after an initial increase. Each time series was investigated at three fortification levels and one unfortified series. Following extraction, analysis by ultra performance liquid chromatography high-resolution mass spectrometry, and data processing, a total of 21,700 aligned peaks were obtained. Peaks displaying an increasing trend were filtered from randomly fluctuating peaks using time trend ratios and Spearman's rank correlation coefficients. The first approach was successful in flagging model compounds spiked at only two to three time points, while the latter approach resulted in all model compounds ranking in the top 11 % of the peak lists. Compared to initial peak lists, a combination of both approaches reduced the size of datasets by 80-85 %. Overall, non-target time trend screening represents a promising data reduction strategy for identifying emerging bioaccumulative contaminants in biological samples.

Wood combustion has been shown to contribute significantly to emissions of polycyclic aromatic hydrocarbons and hydroxylated polycyclic aromatic hydrocarbons, compounds with toxic and carcinogenic properties. However, only a small number of hydroxylated polycyclic aromatic hydrocarbons have been determined in particles from wood combustion, usually compounds with available reference standards. In this present study, suspect and non-target screening strategies were applied to characterize the wood smoke particles from four different wood types and two combustion conditions with respect to hydroxylated polycyclic aromatic hydrocarbons and other organic compounds. In the suspect screening, 32 peaks corresponding to 12 monohydroxylated masses were tentatively identified by elemental composition assignments and matching of isotopic pattern and fragments. More than one structure was suggested for most of the measured masses. Statistical analysis was performed on the non-target screening data in order to single out significant peaks having intensities that depend on the wood type and/or combustion condition. Significant peaks were found in both negative and positive ionization modes, with unique peaks for each wood type and combustion condition, as well as a combination of both factors. Furthermore, structural elucidation of some peaks was done by comparing the spectra in the samples with spectra found in the spectral databases. Six compounds were tentatively identified in positive ionization mode, and 19 in negative ionization mode. The results in this present study demonstrate that there are significant overall differences in the chemistry of wood smoke particles that depends on both the wood type and the combustion condition used.

The objective of this study was to develop a novel analytical chemistry method, comprised of a coupled high-performance liquid chromatography-gas chromatography/mass spectrometry system (LC-GC/MS) with low detection limits and high selectivity, for the identification and determination of oxygenated polycyclic aromatic hydrocarbons (OPAHs) and polycyclic aromatic hydrocarbons (PAHs) in urban air and diesel particulate matter. The linear range of the four OPAHs, which include 9,10-anthraquinone, 4H-cyclopenta[def]phenanthrene-4-one, benzanthrone, and 7,12-benz[a]anthraquinone, was 0.7 pg-43.3 ng with limits of detection (LODs) and limits of quantification (LOQs) on the order of 0.2-0.8 and 0.7-1.3 pg, respectively. The LODs in this study are generally lower than values reported in the literature, which can be explained by using large-volume injection. The recoveries of the OPAHs spiked onto glass fiber filters using two different pressurized liquid extraction (PLE) methods were in the ranges of 84-107 and 67-110 %, respectively. The analytical protocols were validated using the following National Institute of Standards and Technology standard reference materials: SRM 1649a (Urban Dust), SRM 1650b (Diesel Particulate Matter), and SRM 2975 (Diesel Particulate Matter, Industrial Forklift). The measured mass fractions of the OPAHs in the standard reference materials (SRMs) in this present study are higher than the values from the literature, except for benzanthrone in SRM 1649a (Urban Dust). In addition to the OPAHs, 44 PAHs could be detected and quantified from the same particulate extract used in this protocol. Using data from the literature and applying a two-sided t test at the 5 % level using Bonferroni correction, significant differences were found between the tested PLE methods for individual PAHs. However, the measured mass fractions of the PAHs were comparable, similar to, or higher than those previously reported in the literature.

Anaphylaxis is a potentially life-threatening condition triggered mainly by the release of inflammatory mediators, notably histamine. In pharmaceutical research, drug discovery, and clinical evaluation, it may be necessary to accurately assess the potential of a compound, event, or disorder to promote the release of histamine. In contrast to the measurement of plasma histamine, determination of the stable metabolite 1-methyl-4-imidazoleacetic acid (tele-MIAA) in urine provides a noninvasive and more reliable methodology to monitor histamine release. This study presents a repeatable high-performance liquid chromatography coupled to electrospray mass spectrometry (LC-ESI-MS) method where tele-MIAA is baseline separated from its structural isomer 1-methyl-5-imidazoleacetic acid (pi-MIAA) and an unknown in human urine. The ion-pairing chromatography method, in reversed-phase mode, based on 0.5 mM tridecafluoroheptanoic acid demonstrated high repeatability and was applied in a clinical development program that comprised a large number of clinical samples from different cohorts. The inter- and intra-run precision of the method for tele-MIAA were 8.4 and 4.3 %, respectively, at the mean urinary concentration level, while method accuracy was between -16.2 and 8.0 % across the linear concentration range of 22-1,111 ng mL(-1). Overall, method precision was greater than that reported in previously published methods and enabled the identification of gender differences that were independent of age or demography. The median concentration measured in female subjects was 3.0 mu mol mmol(-1) of creatinine, and for male subjects, it was 2.1 mu mol mmol(-1) of creatinine. The results demonstrate that the method provides unprecedented accuracy, precision, and practicality for the measurement of tele-MIAA in large clinical settings.