Urinary excretion of herbicide co-formulants after oral exposure to roundup MON 52276 in rats

The toxicity of surfactants, which are an integral component of glyphosate-formulated products is an underexplored and highly debated subject. Since biomonitoring human exposure to glyphosate co-formulants is considered as a public health priority, we developed and validated a high-resolution mass spectrometry method to measure the urinary excretion of surfactants present in Roundup MON 52276, the European Union (EU) representative formulation of glyphosate-based herbicides. Quantification was performed measuring the 5 most abundant compounds in the mixture. We validated the method and showed that it is highly accurate, precise and reproducible with a limit of detection of 0.0004 μg/mL. We used this method to estimate the oral absorption of MON 52276 surfactants in Sprague-Dawley rats exposed to three concentrations of MON 52276 via drinking water for 90 days. MON 52276 surfactants were readily detected in urine of rats administered with this commercial Roundup formulation starting from a low concentration corresponding to the EU glyphosate acceptable daily intake. Our results provide a first step towards the implementation of surfactant co-formulant biomonitoring in human populations

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

Use of Shotgun Metagenomics and Metabolomics to Evaluate the Impact of Glyphosate or Roundup MON 52276 on the Gut Microbiota and Serum Metabolome of Sprague-Dawley Rats

Background: There is intense debate on whether glyphosate can inhibit the shikimate pathway of gastrointestinal microorganisms, with potential health implications. Objectives: We tested whether glyphosate or its representative EU herbicide formulation Roundup MON 52276 affects the rat gut microbiome. Methods: We combined cecal microbiome shotgun metagenomics with serum and cecum metabolomics to assess the effects of glyphosate [0.5, 50, 175 mg/kg body weight (BW) per day] or MON 52276 at the same glyphosate-equivalent doses, in a 90-d toxicity test in rats. Results: Glyphosate and MON 52276 treatment resulted in ceca accumulation of shikimic acid and 3-dehydroshikimic acid, suggesting inhibition of 5-enolpyruvylshikimate-3-phosphate synthase of the shikimate pathway in the gut microbiome. Cysteinylglycine, γ-glutamylglutamine, and valylglycine levels were elevated in the cecal microbiome following glyphosate and MON 52276 treatments. Altered cecum metabolites were not differentially expressed in serum, suggesting that the glyphosate and MON 52276 impact on gut microbial metabolism had limited consequences on physiological biochemistry. Serum metabolites differentially expressed with glyphosate treatment were associated with nicotinamide, branched-chain amino acid, methionine, cysteine, and taurine metabolism, indicative of a response to oxidative stress. MON 52276 had similar, but more pronounced, effects than glyphosate on the serum metabolome. Shotgun metagenomics of the cecum showed that treatment with glyphosate and MON 52276 resulted in higher levels of Eggerthella spp., Shinella zoogleoides, Acinetobacter johnsonii, and Akkermansia muciniphila. Shinella zoogleoides was higher only with MON 52276 exposure. In vitro culture assays with Lacticaseibacillus rhamnosus strains showed that Roundup GT plus inhibited growth at concentrations at which MON 52276 and glyphosate had no effect. Discussion: Our study highlights the power of multi-omics approaches to investigate the toxic effects of pesticides. Multi-omics revealed that glyphosate and MON 52276 inhibited the shikimate pathway in the rat gut microbiome. Our findings could be used to develop biomarkers for epidemiological studies aimed at evaluating the effects of glyphosate herbicides on humans

The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets

<p>Abstract</p> <p>Background</p> <p>Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.</p> <p>Description</p> <p>Here, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.). Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline.</p> <p>Conclusions</p> <p>MetabolomeExpress <url>https://www.metabolome-express.org</url> provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.</p

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.

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

OpenChrom: a cross-platform open source software for the mass spectrometric analysis of chromatographic data

<p>Abstract</p> <p>Background</p> <p>Today, data evaluation has become a bottleneck in chromatographic science. Analytical instruments equipped with automated samplers yield large amounts of measurement data, which needs to be verified and analyzed. Since nearly every GC/MS instrument vendor offers its own data format and software tools, the consequences are problems with data exchange and a lack of comparability between the analytical results. To challenge this situation a number of either commercial or non-profit software applications have been developed. These applications provide functionalities to import and analyze several data formats but have shortcomings in terms of the transparency of the implemented analytical algorithms and/or are restricted to a specific computer platform.</p> <p>Results</p> <p>This work describes a native approach to handle chromatographic data files. The approach can be extended in its functionality such as facilities to detect baselines, to detect, integrate and identify peaks and to compare mass spectra, as well as the ability to internationalize the application. Additionally, filters can be applied on the chromatographic data to enhance its quality, for example to remove background and noise. Extended operations like do, undo and redo are supported.</p> <p>Conclusions</p> <p>OpenChrom is a software application to edit and analyze mass spectrometric chromatographic data. It is extensible in many different ways, depending on the demands of the users or the analytical procedures and algorithms. It offers a customizable graphical user interface. The software is independent of the operating system, due to the fact that the Rich Client Platform is written in Java. OpenChrom is released under the Eclipse Public License 1.0 (EPL). There are no license constraints regarding extensions. They can be published using open source as well as proprietary licenses. OpenChrom is available free of charge at <url>http://www.openchrom.net</url>.</p

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

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