Dataset Information

The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques.

ABSTRACT: Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), making the collaborative trial a success. However, a relatively small share (37%) of all compounds were reported by more than one participant, which shows that there is plenty of room for improvement in the field of suspect and nontarget screening. An even a smaller share (5%) of the total number of compounds were detected using both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Thus, the two MS techniques are highly complementary. Most of the compounds were detected using LC with electrospray ionization (ESI) MS and comprehensive 2D GC (GC×GC) with atmospheric pressure chemical ionization (APCI) and electron ionization (EI), respectively. Collectively, the three techniques accounted for more than 75% of the reported compounds. Glycols, pharmaceuticals, pesticides, and various biogenic compounds dominated among the compounds reported by LC-MS participants, while hydrocarbons, hydrocarbon derivatives, and chlorinated paraffins and chlorinated biphenyls were primarily reported by GC-MS participants. Plastics additives, flavor and fragrances, and personal care products were reported by both LC-MS and GC-MS participants. It was concluded that the use of multiple analytical techniques was required for a comprehensive characterization of house dust contaminants. Further, several recommendations are given for improved suspect and nontarget screening of house dust and other indoor environment samples, including the use of open-source data processing tools. One of the tools allowed provisional identification of almost 500 compounds that had not been reported by participants.

SUBMITTER: Rostkowski P

PROVIDER: S-EPMC6458998 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques.

Rostkowski Pawel P Haglund Peter P Aalizadeh Reza R Alygizakis Nikiforos N Thomaidis Nikolaos N Arandes Joaquin Beltran JB Nizzetto Pernilla Bohlin PB Booij Petra P Budzinski Hélène H Brunswick Pamela P Covaci Adrian A Gallampois Christine C Grosse Sylvia S Hindle Ralph R Ipolyi Ildiko I Jobst Karl K Kaserzon Sarit L SL Leonards Pim P Lestremau Francois F Letzel Thomas T Magnér Jörgen J Matsukami Hidenori H Moschet Christoph C Oswald Peter P Plassmann Merle M Slobodnik Jaroslav J Yang Chun C

Analytical and bioanalytical chemistry 20190304 10

Untargeted analysis of a composite house dust sample has been performed as part of a collaborative effort to evaluate the progress in the field of suspect and nontarget screening and build an extensive database of organic indoor environment contaminants. Twenty-one participants reported results that were curated by the organizers of the collaborative trial. In total, nearly 2350 compounds were identified (18%) or tentatively identified (25% at confidence level 2 and 58% at confidence level 3), m ...[more]

PMID: 30830245

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Project description:Skeletal muscle is a key tissue site of insulin resistance in type 2 diabetes. Human myotubes are primary skeletal muscle cells displaying both morphological and biochemical characteristics of mature skeletal muscle and the diabetic phenotype is conserved in myotubes derived from subjects with type 2 diabetes. Several abnormalities have been identified in skeletal muscle from type 2 diabetic subjects, however, the exact molecular mechanisms leading to the diabetic phenotype has still not been found. Here we present a large-scale study in which we combine a quantitative proteomic discovery strategy using isobaric peptide tags for relative and absolute quantification (iTRAQ) and a label-free study with a targeted quantitative proteomic approach using selected reaction monitoring to identify, quantify, and validate changes in protein abundance among human myotubes obtained from nondiabetic lean, nondiabetic obese, and type 2 diabetic subjects, respectively. Using an optimized protein precipitation protocol, a total of 2832 unique proteins were identified and quantified using the iTRAQ strategy. Despite a clear diabetic phenotype in diabetic myotubes, the majority of the proteins identified in this study did not exhibit significant abundance changes across the patient groups. Proteins from all major pathways known to be important in type 2 diabetic subjects were well-characterized in this study. This included pathways like the trichloroacetic acid (TCA) cycle, lipid oxidation, oxidative phosphorylation, the glycolytic pathway, and glycogen metabolism from which all but two enzymes were found in the present study. None of these enzymes were found to be regulated at the level of protein expression or degradation supporting the hypothesis that these pathways are regulated at the level of post-translational modification. Twelve proteins were, however, differentially expressed among the three different groups. Thirty-six proteins were chosen for further analysis and validation using selected reaction monitoring based on the regulation identified in the iTRAQ discovery study. The abundance of adenosine deaminase was considerably down-regulated in diabetic myotubes and as the protein binds propyl dipeptidase (DPP-IV), we speculate whether the reduced binding of adenosine deaminase to DPP-IV may contribute to the diabetic phenotype in vivo by leading to a higher level of free DPP-IV to bind and inactivate the anti-diabetic hormones, glucagon-like peptide-1 and glucose-dependent insulintropic polypeptide.

Dataset Information

The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques.

Publications

The strength in numbers: comprehensive characterization of house dust using complementary mass spectrometric techniques.

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