The surfactant co-formulant POEA in the glyphosate-based herbicide RangerPro but not glyphosate alone causes necrosis in Caco-2 and HepG2 human cell lines and ER stress in the ToxTracker assay

The toxicity of co-formulants present in glyphosate-based herbicides (GBHs) has been widely discussed leading to the European Union banning the polyoxyethylene tallow amine (POEA). We identified the most commonly used POEA, known as POE-15 tallow amine (POE-15), in the widely used US GBH RangerPro. Cytotoxicity assays using human intestinal epithelial Caco-2 and hepatocyte HepG2 cell lines showed that RangerPro and POE-15 are far more cytotoxic than glyphosate alone. RangerPro and POE-15 but not glyphosate caused cell necrosis in both cell lines, and that glyphosate and RangerPro but not POE-15 caused oxidative stress in HepG2 cells. We further tested these pesticide ingredients in the ToxTracker assay, a system used to evaluate a compound's carcinogenic potential, to assess their capability for inducing DNA damage, oxidative stress and an unfolded protein response (endoplasmic reticulum, ER stress). RangerPro and POE-15 but not glyphosate gave rise to ER stress. We conclude that the toxicity resulting from RangerPro exposure is thus multifactorial involving ER stress caused by POE-15 along with oxidative stress caused by glyphosate. Our observations reinforce the need to test both co-formulants and active ingredients of commercial pesticides to inform the enactment of more appropriate regulation and thus better public and environmental protection

Detection of applied and ambient forces with a matter-wave magnetic gradiometer

An atom interferometer using a Bose–Einstein condensate of 87Rb atoms is utilized for the measurement of magnetic-field gradients. Composite optical pulses are used to construct a spatially symmetric Mach–Zehnder geometry. By using a biased interferometer we demonstrate the ability to measure small residual forces in our system and discriminate between magnetic and inertial effects. These are a residual ambient magnetic-field gradient of 15±2 mG/cm and an inertial acceleration of 0.08±0.02 m/s2. Our method has important applications in the calibration of precision measurement devices and the reduction of systematic errors

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at http://gmd.mpimp-golm.mpg.de/. All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities

Metabolic profiling of HepG2 cells incubated with S(−) and R(+) enantiomers of anti-coagulating drug warfarin

Warfarin is a commonly prescribed oral anticoagulant with narrow therapeutic index. It achieves anti-coagulating effects by interfering with the vitamin K cycle. Warfarin has two enantiomers, S(−) and R(+) and undergoes stereoselective metabolism, with the S(−) enantiomer being more effective. We reported the intracellular metabolic profile in HepG2 cells incubated with S(−) and R(+) warfarin by GCMS. Chemometric method PCA was applied to analyze the individual samples. A total of 80 metabolites which belong to different categories were identified. Two batches of experiments (with and without the presence of vitamin K) were designed. In samples incubated with S(−) and R(+) warfarin, glucuronic acid showed significantly decreased in cells incubated with R(+) warfarin but not in those incubated with S(−) warfarin. It may partially explain the lower bio-activity of R(+) warfarin. And arachidonic acid showed increased in cells incubated with S(−) warfarin but not in those incubated with R(+) warfarin. In addition, a number of small molecules involved in γ-glutamyl cycle displayed ratio variations. Intracellular glutathione detection further validated the results. Taken together, our findings provided molecular evidence on a comprehensive metabolic profile on warfarin-cell interaction which may shed new lights on future improvement of warfarin therapy

A randomised controlled trial for the effectiveness of intra-articular Ropivacaine and Bupivacaine on pain after knee arthroscopy: the DUPRA (DUtch Pain Relief after Arthroscopy)-trial

In this double-blinded, randomised clinical trial, the aim was to compare the analgesic effects of low doses of intra-articular Bupivacaine and Ropivacaine against placebo after knee arthroscopy performed under general anaesthesia. A total of 282 patients were randomised to 10 cc NaCl 0.9%, 10 cc Bupivacaine 0.5% or 10 cc Ropivacaine 0.75%. Patients received the assigned therapy by intra-articular injection after closure of the portal. Pain and satisfaction were measured at one, 4 h and 5-7 days after arthroscopy with Numerical Rating Scale (NRS) -scores. NSAID consumption was also recorded. One-h NRS-scores at rest were higher in the NaCl group compared with the Bupivacaine group (P <0.01), 1 h NRS-scores in flexion were higher in the NaCl group compared with the Bupivacaine (P <0.01) and Ropivacaine (P <0.01) groups. NRS-satisfaction at 4 h was higher for the Bupivacaine group compared with the NaCl group (P = 0.01). Differences in NRS-scores were significant but low in magnitude. NSAID consumption was lower in the Bupivacaine group compared with the NaCl group (P <0.01). The results of this randomised clinical trial demonstrate improved analgesia after administration of low doses of intra-articular Bupivacaine and Ropivacaine after arthroscopy of the knee. Considering reports of Bupivacaine and Ropivacaine being chondrotoxic agents and the relatively small improvement on patient comfort found in this trial, it is advised to use systemic anaesthetic instead of intra-articular Bupivacaine or Ropivacaine for pain relief after knee arthroscopy.

Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

BACKGROUND: Structure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas. RESULTS: An algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries. CONCLUSION: The seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem

Genetic improvement of tomato by targeted control of fruit softening

Controlling the rate of softening to extend shelf life was a key target for researchers engineering genetically modified (GM) tomatoes in the 1990s, but only modest improvements were achieved. Hybrids grown nowadays contain 'non-ripening mutations' that slow ripening and improve shelf life, but adversely affect flavor and color. We report substantial, targeted control of tomato softening, without affecting other aspects of ripening, by silencing a gene encoding a pectate lyase

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages