nLossFinder-A Graphical User Interface Program for the Nontargeted Detection of DNA Adducts.

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.

Structured intervention plan including component-resolved diagnostics helps reducing the burden of food allergy among school-aged children.

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 172 700€ per year at start and 13 200€ per year at the end of the study; total savings were 128 400€ 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.

Elucidation of chromatographic peak shifts in complex samples using a chemometrical approach.

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.

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

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. Graphical abstract Using time trends to filter out emerging contaminants from large peak lists.

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics.

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.

TracMass 2--a modular suite of tools for processing chromatography-full scan mass spectrometry data.

In untargeted proteomics and metabolomics, raw data obtained with an LC/MS instrument are processed into a format that can be used for statistical analysis. Full scan MS data from chromatographic separation of biological samples are complex and analyte concentrations need to be extracted and aligned so that they can be compared across the samples. Several computer programs and methods have been developed for this purpose. There is still a need to improve the ease of use and feedback to the user because of the advanced multiparametric algorithms used. Here, we present and make publicly available, TracMass 2, a suite of computer programs that gives immediate graphical feedback to the data analyst on parameter settings and processing results, as well as producing state-of-the-art results. The main advantage of TracMass 2 is that the feedback and transparency of the processing steps generate confidence in the end result, which is a table of peak intensities. The data analyst can easily validate every step of the processing pipeline. Because the user receives feedback on how all parameter values affect the result before starting a lengthy computation, the user's learning curve is enhanced and the total time used for data processing can be reduced. TracMass 2 has been released as open source and is included in the Supporting Information . We anticipate that TracMass 2 will set a new standard for how chemometrical algorithms are implemented in computer programs.

A concept study on non-targeted screening for chemical contaminants in food using liquid chromatography-mass spectrometry in combination with a metabolomics approach.

A generic method to screen for new or unexpected contaminants at ppm levels in food has been developed. The method comprises an acidic acetonitrile extraction, detection with ultra-high-pressure liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry and statistical evaluation using a metabolomics approach comparing suspected contaminated food with uncontaminated foods. The method was tested for 26 model contaminants from 100 µg/g down to 0.4 µg/g in three brands of fresh orange juice. Blinded statistical evaluation revealed signals from all added contaminants detectable by liquid chromatography-electrospray ionisation using positive ionisation mode, while only two false-positive signals were reported. The method is primarily intended to be used for investigation of food samples suspected to be contaminated with unknown substances. Additionally it could be used to continuously monitor for appearance of new food contaminants as a complement to the specific targeted analysis that is today's foundation of food safety analysis.

Strategy for quantifying trace levels of BMAA in cyanobacteria by LC/MS/MS.

The cyanobacterial neurotoxin ß-N-methylamino-L-alanine (BMAA) is an amino acid that is putatively associated with the pathology of amyotrophic lateral sclerosis/Parkinsonism-dementia complex (ALS-PDC) disease. It raises serious health risk concerns since cyanobacteria are ubiquitous thus making human exposure almost inevitable. The identification and quantification of BMAA in cyanobacteria is challenging because it is present only in trace amounts and occurs alongside structurally similar compounds such as BMAA isomers. This work describes an enhanced liquid chromatography/tandem mass spectrometry platform that can distinguish BMAA from its isomers ß-amino-N-methyl-alanine, N-(2-aminoethyl) glycine (AEG), and 2,4-diaminobutyric acid, thus ensuring confident identification of BMAA. The method's sensitivity was improved fourfold by a post-column addition of acetonitrile. The instrument and method limits of detection were shown to be 4.2 fmol/injection (or 0.5 pg/one column) and 0.1 µg/g dry weight of cyanobacteria, respectively. The quantification method uses synthesized deuterated BMAA as an internal standard and exhibits good linearity, accuracy, and precision. Matrix effects were also investigated, revealing an ion enhancement of around 18 %. A lab-cultured cyanobacterial sample (Leptolyngbya PCC73110) was analyzed and shown to contain about 0.73 µg/g dry weight BMAA. The isomer AEG, whose chromatographic properties closely resemble those of BMAA, was also detected. These results highlight the importance of distinguishing BMAA from its isomers for reliable identification as well as providing a sensitive and accurate quantification method for measuring trace levels of BMAA in cyanobacterial samples.

Automated annotation and quantification of metabolites in 1H NMR data of biological origin.

In (1)H NMR metabolomic datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference database built from that single spectrum can be used for any number of (1)H NMR datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several (1)H NMR datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and data mining for new potential biomarkers among the unknowns.

Time-resolved biomarker discovery in 1H-NMR data using generalized fuzzy Hough transform alignment and parallel factor analysis.

This work addresses the subject of time-series analysis of comprehensive (1)H-NMR data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of (1)H-NMR data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological (1)H-NMR data as it provides almost full automation of the whole data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a (1)H-NMR rat urine dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.

A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support.

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.

The correspondence problem for metabonomics datasets.

In metabonomics it is difficult to tell which peak is which in datasets with many samples. This is known as the correspondence problem. Data from different samples are not synchronised, i.e., the peak from one metabolite does not appear in exactly the same place in all samples. For datasets with many samples, this problem is nontrivial, because each sample contains hundreds to thousands of peaks that shift and are identified ambiguously. Statistical analysis of the data assumes that peaks from one metabolite are found in one column of a data table. For every error in the data table, the statistical analysis loses power and the risk of missing a biomarker increases. It is therefore important to solve the correspondence problem by synchronising samples and there is no method that solves it once and for all. In this review, we analyse the correspondence problem, discuss current state-of-the-art methods for synchronising samples, and predict the properties of future methods.

Feature detection and alignment of hyphenated chromatographic-mass spectrometric data. Extraction of pure ion chromatograms using Kalman tracking.

In this paper we present a new method, called TracMass, for analyzing data obtained using hyphenated chromatography-mass spectrometry (XC/MS). The method uses a Kalman filter to extract pure, noise-free ion chromatograms by exploiting the latent second order structure in the XC/MS data. TracMass differs from current state-of-the-art methodologies, which extract chromatograms by binning along the m/z axis and further processes the data in various ways, e.g. by baseline correction, component detection algorithm, peak detection, and curve resolution to extract molecular features. The proposed method was validated by analyzing two plasma datasets: one derived from 99 quality control samples where TracMass extracted 8880 Pure Ion Chromatograms (PICs) present in > or =90 of the samples. The second dataset was spiked with two different internal standard mixtures to test differential expression analysis. Here TracMass found 20000 PICs present in 10 samples, all differentially expressed analytes, and also a previously unreported discriminating metabolite. Finding as many PICs as possible is in this context essential to ensure that even small differentiating features are found (if they exist). The resulting data representation from TracMass (PICs) can be used directly for statistical analysis, and the method is fast (approximately 5min/sample), with few adjustable parameters.

Proof of principle of a generalized fuzzy Hough transform approach to peak alignment of one-dimensional 1H NMR data.

In metabolic profiling, multivariate data analysis techniques are used to interpret one-dimensional (1D) 1H NMR data. Multivariate data analysis techniques require that peaks are characterised by the same variables in every spectrum. This location constraint is essential for correct comparison of the intensities of several NMR spectra. However, variations in physicochemical factors can cause the locations of the peaks to shift. The location prerequisite may thus not be met, and so, to solve this problem, alignment methods have been developed. However, current state-of-the-art algorithms for data alignment cannot resolve the inherent problems encountered when analysing NMR data of biological origin, because they are unable to align peaks when the spatial order of the peaks changes-a commonly occurring phenomenon. In this paper a new algorithm is proposed, based on the Hough transform operating on an image representation of the NMR dataset that is capable of correctly aligning peaks when existing methods fail. The proposed algorithm was compared with current state-of-the-art algorithms operating on a selected plasma dataset to demonstrate its potential. A urine dataset was also processed using the algorithm as a further demonstration. The method is capable of successfully aligning the plasma data but further development is needed to address more challenging applications, for example urine data.

A comparison of methods for alignment of NMR peaks in the context of cluster analysis.

This paper compares the performance of two recently developed algorithms and methods for peak alignment of first-order NMR data of complex biological samples. The NMR spectra of such samples exhibit variations in peak position and peak shape due to variations in the sample matrix and to instrumental instabilities. The first method comprises an alignment of spectral segments with linear interpolation and shift correction to accommodate correspondence between a target and a test spectrum by a beam search or genetic algorithm. The second method is based on peak picking and needle vector representation of the NMR data with subsequent breadth-first search to establish shift corrections between the target and the test spectrum. The two proposed peak alignment methods and their respective merits are discussed for a real metabonomics application. Both alignment methods have been shown to enhance the interpretability of the resulting multivariate models, thereby increasing the prospect of detecting and following the onset of subtle biological changes reflected in the NMR data.

Determination of solvation free energies by adaptive expanded ensemble molecular dynamics.

A new method of calculating absolute free energies is presented. It was developed as an extension to the expanded ensemble molecular dynamics scheme and uses probability density estimation to continuously optimize the expanded ensemble parameters. The new method is much faster as it removes the time-consuming and expertise-requiring step of determining balancing factors. Its efficiency and accuracy are demonstrated for the dissolution of three qualitatively very different chemical species in water: methane, ionic salts, and benzylamine. A recently suggested optimization scheme by Wang and Landau [Phys. Rev. Lett. 86, 2050 (2001)] was also implemented and found to be computationally less efficient than the proposed adaptive expanded ensemble method.