Project description:This Agilent QTOF data consists of urine and liver of mice fed AFFF, as well as PFAS chemical standards treated with mouse enzymes for biotransformation. Data acquired by Sheng Liu and David A. Dukes. It goes alongside the manuscript: "Expanding PFAS Identification with Transformation Product Libraries: Non-Targeted Analysis Reveals Biotransformation Products in Mice" published in Environmental Science & Technology An interactive dataset after FluoroMatch annotation for the urine data can be found here: https://innovativeomics.com/datasets/non-targeted-pfas-dataset-from-urine-of-afff-fed-mice/ Software with the biotransformation libraries can also be found at: https://innovativeomics.com Per- and polyfluoroalkyl substances (PFAS) are widely used persistent synthetic chemicals that have been linked to adverse health effects. While the behavior of PFAS has been evaluated in the environment, our understanding of reaction products in mammalian systems is limited. This study identified biological PFAS transformation products and generated mass spectral libraries to facilitate automated search and identification. The biological transformation products of 27 PFAS, spanning 5 chemical subclasses (alcohols, sulfonamides, carboxylic acids, ethers, and esters), were evaluated following enzymatic reaction with mouse liver S9 fractions. Four major pathways were identified by liquid chromatography-high resolution mass spectrometry: glucuronidation; sulfation; dealkylation and oxidation. Class-based fragmentation rules and associated PFAS transformation product libraries were generated and integrated into an automated non-targeted PFAS data analysis software (FluoroMatch). Fragmentation was additionally predicted for the potential transformation products of more than 2,500 PFAS in the EPA CompTox Chemicals Dashboard PFASSTRUCTv4. Generated mass spectral libraries were validated by applying FluoroMatch to a dataset of urine from aqueous film-forming foam (AFFF)-dosed mice. Toxicity predictions showed identified PFAS transformation products as potential developmental and mutagenic toxicants. This research enables more comprehensive PFAS characterization in biological systems which will improve assessment of exposures and evaluation of the associated health impacts.

Project description:This is the GC Orbitrap data accompanying the FluoroMatch GC Library manuscript. References and software are available here: https://innovativeomics.com/software/fluoromatch-gc-modular-pfas-annotation/ Despite thousands of documented PFAS, approaches to characterize volatile and semi-volatile PFAS are limited. To address this gap, we developed a non-targeted gas chromatography high-resolution mass spectrometry (GC-HRMS) workflow to extend coverage of volatile and semi-volatile PFAS. This workflow includes new FlouroMatch GC mass spectral libraries (>1900 EI and PCI predicted and experimental spectra) and characterization of thousands of PFAS specific EI fragments for fragment screening. Liquid chromatography (LC) and GC-HRMS approaches were highly complementary, with GC-HRMS revealing diverse PFAS profiles in 8 different environmental and biological matrices distinct from PFAS detected in LC approaches. For example, we detected a previously unreported PFAS, 2-(perfluorohexyl)ethanethiol in multiple firefighting foam (AFFF) formulations and a novel feature tentatively annotated as N-methyl-N-(2-hydroxyethyl) perfluorooctanesulfonamide (MeFOSE alcohol) that was consistently detected in various matrices related to point and non-point PFAS exposures including dried blood spots, leachate, industrial effluent, and settled dust collected from homes, which were not detected in corresponding LC approaches. Personal exposure to volatile and semi-volatile PFAS was found to vary significantly across individuals. In a cohort of 48 children in Connecticut wearing wristband passive samplers, GC-HRMS fragment screening revealed that 46% of 40 unique airborne PFAS detected only occurred in a single child, and 58% of 47 unique PFAS detected in settled dust collected from 11 households were only detected in a single home. Findings highlight the importance of personal monitoring using a combination of non-targeted GC and LC-HRMS approaches to comprehensively characterize PFAS exposures. The novel methods that we have established will allow the health burden PFAS to be rigorously evaluated, ultimately informing regulatory policies and public health interventions.

Project description:This Dataset goes alongside the 2023 manuscript: Novel perfluoroalkyl substances (PFAS) discovered in whole blood using automated non-targeted analysis of dried blood spots by Jeremy P. Koelmel, Elizabeth Z. Lin, Emily Parry, Paul Stelben, Emma E.Rennie, Krystal J.Godri Pollitt A small subset of per- and polyfluoroalkyl substances (PFAS) are routinely screened in human blood. These compounds generally explain <50 percent of the total PFAS in human blood. The percentage of known PFAS in human blood has been decreasing as replacement PFAS and more complex PFAS chemistries are introduced to the market. Most of these novel PFAS have not been previous identified. Non-targeted methods are required to characterize this dark matter PFAS. Our objective was to apply non-targeted PFAS analysis to human blood to gain an understanding about the sources, concentrations, and toxicity of these compounds. A high-resolution tandem mass spectrometry (HRMS) and software workflow for PFAS characterization in dried blood spots is reported. Dried blood spots are a less invasive collection technique compared to venous blood draws, allowing collection from vulnerable populations. Biorepositories of archived dried blood spots are available internationally from newborns and present opportunities to study prenatal exposure to PFAS. In this study, dried blood spot cards were analyzed using iterative MS/MS by liquid chromatography HRMS. Data processing was conducted using FluoroMatch Suite including a visualizer tool that presents homologous series, retention time vs m/z plots, MS/MS spectra, feature tables, annotations, and fragments for fragment screening. The researcher performing data-processing and annotation was blinded to the fact that standards were spiked in, and was able to annotated 95 percent of standards spiked on dried blood spot samples, signifying a low false negative rate using FluoroMatch Suite. A total of 28 PFAS (20 standards and 4 exogenous compounds) were detected across five homologous series with Schymanski Level 2 confidence. Of these four, 3 were perfluoroalkyl ether carboxylic acids (PFECA), a chemical class of PFAS which is increasingly being detected in environmental and biological matrices but is not currently screened in most targeted analyses. A further 86 potential PFAS were detected using fragment screening. PFAS are extremely persistent and widespread yet remain largely unregulated. Our findings will contribute to an improved an understanding of exposures. Application of these methods in environmental epidemiology studies have the potential to inform policy with regards to PFAS monitoring, regulation, and individual-level mitigation strategies.

Project description:This Agilent QTOF data consists of urine and liver of mice fed AFFF, as well as PFAS chemical standards treated with mouse enzymes for biotransformation. Data acquired by Sheng Liu and David A. Dukes. It goes alongside the manuscript: "Expanding PFAS Identification with Transformation Product Libraries: Non-Targeted Analysis Reveals Biotransformation Products in Mice" published in Environmental Science & Technology An interactive dataset after FluoroMatch annotation for the urine data can be found here: https://innovativeomics.com/datasets/non-targeted-pfas-dataset-from-urine-of-afff-fed-mice/ Software with the biotransformation libraries can also be found at: https://innovativeomics.com Per- and polyfluoroalkyl substances (PFAS) are widely used persistent synthetic chemicals that have been linked to adverse health effects. While the behavior of PFAS has been evaluated in the environment, our understanding of reaction products in mammalian systems is limited. This study identified biological PFAS transformation products and generated mass spectral libraries to facilitate automated search and identification. The biological transformation products of 27 PFAS, spanning 5 chemical subclasses (alcohols, sulfonamides, carboxylic acids, ethers, and esters), were evaluated following enzymatic reaction with mouse liver S9 fractions. Four major pathways were identified by liquid chromatography-high resolution mass spectrometry: glucuronidation; sulfation; dealkylation and oxidation. Class-based fragmentation rules and associated PFAS transformation product libraries were generated and integrated into an automated non-targeted PFAS data analysis software (FluoroMatch). Fragmentation was additionally predicted for the potential transformation products of more than 2,500 PFAS in the EPA CompTox Chemicals Dashboard PFASSTRUCTv4. Generated mass spectral libraries were validated by applying FluoroMatch to a dataset of urine from aqueous film-forming foam (AFFF)-dosed mice. Toxicity predictions showed identified PFAS transformation products as potential developmental and mutagenic toxicants. This research enables more comprehensive PFAS characterization in biological systems which will improve assessment of exposures and evaluation of the associated health impacts.

Project description:Isolated murine kidney glomeruli were lysed in buffer containing 8M urea, and proteins were reduced and alkylated. Proteins were digested with trypsin. After reverse-phase chomatrography, peptides were fractionated using strong cation exchange chromatography. Phosphopeptides were enriched using IMAC (FeNTA) columns. Peptides were analyzed by LC-MS/MS on a Q Exactive mass spectrometer or an LTQ Orbitrap XL mass spectrometer. Metadata for Orbitrap samples (MaxQuant Output files include „Orbi“) 1. General features – 1.1 Global descriptors a. Responsible person or role: Markus Rinschen. markus.rinschen@uk-koeln.de; tobias.lamkemeyer@uni-koeln.de b. Instrument manufacturer, model: LTQ-Orbitrap XL, Thermo Scientific c. customization 2. Ion sources – ESI fed by nLC 2 (Proxeon) 3. Post source component 3.1. Analyser: MS1 survey scan in an Orbitrap and MS2 analysed in Linear Trap. 3.2. Activation/Dissociation: CID 4. Spectrum and peak list generation and annotation 4.1. Data acquisition: MaxQuant v. 1.3.05 Top one method with a cycle of one full MS1 scan in the Orbitrap, followed by the fragmentation with dynamic exclusion window of 60 seconds and followed by the acquisition of 5 product ion scans generated in the LTQ analyser, and detected in the LTQ. Unselected fragmentation. For further details, see .raw files. URL of file: Filename 4.2. Data analysis: MaxQuant v 1.3.05 Parameters used in the generation of peak lists or processed spectra: see annotation in “parameters” file. The mouse uniprot reference database “complete proteome” obtained on January 2nd, 2013 was used. Metadata for QExactive samples (Max Quant output files include „QE“) 1. General features – 1.1 Global descriptors a. Responsible person or role: Markus Rinschen. markus.rinschen@uk-koeln.de; Marcus Krüger marcus.krueger@mpi-bn.mpg.de b. Instrument manufacturer, model: Q Exactive Thermo Scientific c. customization 2. Ion sources – ESI fed by nLC 1000 (Proxeon) 3. Post source component 3.1. Analyser: MS1 survey scan in an Orbitrap and MS2 analysed in Orbitrap/HCD cell 3.2. Activation/Dissociation: HCD 4. Spectrum and peak list generation and annotation 4.1. Data acquisition: MaxQuant v. 1.3.05 Top one method with a cycle of one full MS1 scan in the Orbitrap, followed by the fragmentation with dynamic exclusion window of 20 seconds in the HCD cell and followed by the acquisition of 10 product ion scans generated in the LTQ analyser, and detected in the LTQ. Unselected fragmentation. For further details, see .raw files. URL of file: Filename 4.2. Data analysis: MaxQuant v 1.3.05 Parameters used in the generation of peak lists or processed spectra: see annotation in “parameters” file. The mouse uniprot reference database “complete proteome” obtained on January 2nd, 2013 was used.

Project description:Granzymes are a family of structurally related serine proteases involved in immunity. To date four out of five human granzymes have been assigned orthologues in mice; however for granzyme H, no clear murine orthologue has been suggested and its role in cytotoxicity remains controversial. In this study, we demonstrate that granzyme H is an inefficient cytotoxin. Besides analyzing the substrate specificity profile of granzyme H by means of substrate phage display, we assessed human granzyme H and mouse granzyme C cleavage susceptibility on a proteome-wide level. The extended specificity profiles of granzyme C and granzyme H (i.e. beyond P4-P4') match those previously observed for granzyme B. We demonstrate conservation of these extended specificity profiles among various granzymes since granzyme B cleavage susceptibility of an otherwise granzyme H/C specific cleavage site can be conferred simply by altering the P1-residue to aspartate, the preferred P1-residue of granzyme B. Our results thus indicate a conserved, but hitherto generally underappreciated specificity-determining role of extended protease-substrate contacts in steering cleavage susceptibility. Bio-IT: The spectra were searched with Mascot Daemon 2.2. The following search parameters were used. Peptide mass tolerance was set at 0.2 Da and peptide fragment mass tolerance at 0.1 Da; with the ESI-QUAD-TOF as selected instrument for peptide fragmentation rules for the Q-TOF Premier data. Endoproteinase semiArg-C/P (i.e., no restriction towards arginine-proline cleavage) was set as enzyme allowing one missed cleavage. Variable modifications were pyroglutamate formation of N-terminal glutamine, pyrocarbamidomethyl formation of N-terminal alkylated cysteine, deamidation of asparagine, acetylation or tri-deuteroacetylation of the alpha-N-terminus. Fixed modifications were methionine oxidation (sulfoxide), carbamidomethyl for cysteine, tri-deuteroacetylation of lysine and for identifying heavy labelled peptides, [13C6] or [13C615N4]-arginine were additionally set as fixed modifications. The spectra and identifications were stored in ms_lims

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FlouroMatch Modular and FlouroMatch Flow Software v2.4

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