Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [ESI]+
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF-X
ORGANISM(S): Surface Water / Bangladesh
SUBMITTER: 
Jonathan W. Martin
PROVIDER: MSV000089705 | MassIVE | Wed Jun 22 04:59:00 BST 2022
REPOSITORIES: MassIVE
Similar Datasets
Project description:Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [ESI]-
Published here: https://doi.org/10.1021/acs.est.2c08200
2022-06-22 | MSV000089706 | MassIVE
Project description:Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [APCI]-
Published here: https://doi.org/10.1021/acs.est.2c08200
2022-06-22 | MSV000089704 | MassIVE
Project description:Nontarget analysis of Bangladeshi surface water samples by LC-HRMS - analyzed by DIA [APCI]+
Published here: https://doi.org/10.1021/acs.est.2c08200
2022-06-22 | MSV000089703 | MassIVE
Project description:A conventional evaluation methodology for drinking water pollution focuses on analysing hundreds of compounds, usually by liquid chromatography-tandem mass spectrometry. High-resolution mass spectrometry allows comprehensive evaluation of all detected signals (compounds) based on their elemental composition, intensity, and numbers. We combined target analysis of 192 emerging micropollutants with nontarget (NT) full-scan/MS/MS methods to describe the impact of treatment steps in detail and assess drinking water treatment efficiency without compound identification. The removal efficiency based on target analytes ranged from - 143 to 97%, depending on the treatment section, technologies, and season. The same effect calculated for all signals detected in raw water by the NT method ranged between 19 and 65%. Ozonation increased the removal of micropollutants from the raw water but simultaneously caused the formation of new compounds. Moreover, ozonation byproducts showed higher persistence than products formed during other types of treatment. We evaluated chlorinated and brominated organics detected by specific isotopic patterns within the developed workflow. These compounds indicated anthropogenic raw water pollution but also potential treatment byproducts. We could match some of these compounds with libraries available in the software. We can conclude that passive sampling combined with nontargeted analysis shows to be a promising approach for water treatment control, especially for long-term monitoring of changes in technology lines because passive sampling dramatically reduces the number of samples and provides time-weighted average information for 2 to 4 weeks.
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Project description:Prior studies of Bangladeshi migrants in the UK revealed that reproductive function is adaptive, responding to different environments during childhood by adjusting the timing of puberty, reproductive lifespan and overall reproductive function. Here we aimed to understand the basis of this plasticity. Our goals were to establish whether epigenetic mechanisms play a role in the plasticity of this adaptive reproductive phenotype. We hypothesized that women growing up in Bangladesh would have distinct DNA methylation signatures compared to those who moved to the UK at a young age or were born to Bangladeshi parents in the UK. Some of these environmentally induced epigenetic differences would be detected in buccal cell DNA and reflect the divergent gene expression responsible for the altered reproductive function. The women of the study who grew up in Bangladesh were relatively affluent, well-nourished and rarely performed manual work, but a significant confounding factor in their early life was the level of disease load presenting a chronic immune challenge
2019-06-27 | GSE133355 | GEO
Project description:Anas platyrhynchos breed:Bangladeshi duck Genome sequencing and assembly
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Project description:Non-targeted screening (NTS) with reversed phase liquid chromatography electrospray ionization high resolution mass spectrometry (LC/ESI/HRMS) is increasingly employed as an alternative to targeted analysis; however, it is not possible to quantify all compounds found in a sample with analytical standards. As an alternative, semi-quantification strategies are, or at least should be, used to estimate the concentrations of the unknown compounds before final decision making. All steps in the analytical chain, from sample preparation to ionization conditions and data processing can influence the signals obtained, and thus the estimated concentrations. Therefore, each step needs to be considered carefully. Generally, less is more when it comes to choosing sample preparation as well as chromatographic and ionization conditions in NTS. By combining the positive and negative ionization mode, the performance of NTS can be improved, since different compounds ionize better in one or the other mode. Furthermore, NTS gives opportunities for retrospective analysis. In this tutorial, strategies for semi-quantification are described, sources potentially decreasing the signals are identified and possibilities to improve NTS are discussed. Additionally, examples of retrospective analysis are presented. Finally, we present a checklist for carrying out semi-quantitative NTS.
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Project description:Bangladeshi women living in UK: Buccal cell DNA methylation analysis
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Project description:An UHPLC-PDA-ESI/HRMS(n) profiling method was used to identify the glucosinolates and flavonoids of Rorippa indica (Cruciferae), a wild vegetable and Chinese herb used to treat cough, diarrhea, and rheumatoid arthritis. Thirty-three glucosinolates, more than 40 flavonol glycosides, and 18 other phenolic and common organic compounds were identified. The glucosinolates and polyphenols were separated by UHPLC. High-resolution deprotonated molecules provided high accuracy mass values that were used to determine formulas and provide putative identification of the glucosinolates and flavonoids. The fragments from multistage mass spectrometry were used to elucidate the structures. The concentrations of the main components were based on UV peak areas and molar relative response factors with a single calibration standard. This study found this plant to be a rich source for glucosinolates, containing 24 new glucosinolates, including 14 glucosylated glucosinolates that were previously unidentified.
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Project description:Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.
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