Untargeted metabolomics in urine to investigate smoking exposure

Background: Although thousands of different chemicals have been identified in cigarette smoke, the characterization of urinary metabolites derived from those compounds is still not completely achieved. The aim of this work was to perform an untargeted metabolomic experiment on a pilot cross-sectional study conducted on subjects with different smoking habits. Methods: Urine samples were collected from 67 adults; including 38 non-smokers, 7 electronic cigarette smokers, and 22 traditional tobacco smokers. Samples were analyzed by liquid chromatography/time-of flight mass spectrometer operating in data dependent mode. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The ANOVA test was used to detect significant features among groups. The software BEAMS (University of Birmingham) was implemented for grouping adducts and isotopes, and to perform a first annotation. Annotation was completed by comparing fragmentation patterns with on-line databases as Metlin, and using the software MS-FINDER. Results: One hundred and seventeen features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites, of which we were able to putatively annotate about half. The identification of the mercapturic acids of acrolein, 1,3-butadiene, and crotonaldeide, chemicals known to be present in tobacco smoke, supports the validity of the proposed approach. With a lower level of confidence, we annotated the glucuronide conjugated of 3-hydroxycotinine and the sulfate conjugate of methoxyphenol; finally, with the lowest degree of confidence, several other sulfate conjugates of small molecules were annotated. Short discussion/conclusions: The proposed approach seems to be useful for the investigation of exposure to toxicants in humans

A workflow for data integration, analysis, and metabolite annotation for untargeted metabolomics

Metabolomics is the youngest of the \u201comics\u201d disciplines and it is regarded as a promising approach to understand the metabolic changes that can occur in particular conditions and to identify new biomarkers. We present here a workflow for data integration, analysis, and metabolite annotation to be applied to untargeted metabolomic experiments. Data acquired with LC-MS/MS, operating in data dependent mode, are processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The data-table obtained is elaborated and submitted to statistical analysis using the on-line software MetaboAnalyst. Multivariate analysis, in particular principal component and partial least squares discriminant analysis are performed for data visualization. Univariate analysis, in particular T-test for pairwise and ANOVA for multi-groups comparison, are performed to detect significant features among groups. The software BEAMS, developed by the University of Birmingham, is then implemented for grouping adducts and isotopes, and to perform a first annotation. Metabolite annotation is finally completed by comparing the fragmentation pattern obtained from each parent ion corresponding to a significant feature with data stored in on-line databases as Metlin, and with the help of the software MS-FINDER, which performs in-silico fragmentation. We applied this workflow to an untargeted metabolomic experiment performed on 67 urine samples obtained from adult subjects with different smoking habits: non-smokers, electronic cigarette smokers, and traditional tobacco smokers. 117 features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites. We were able to putatively annotate compound classes of most of the significant metabolites (level 3 according to the \u201cProposed minimum reporting standards\u201d; Sumner et al., 2007) and to putatively annotate some of them (level 2). Among them, the glucuronide conjugated of 3-hydroxycotinine supports the validity of the proposed approach

Investigation of urine metabolites related to tobacco smoke chemicals using an untargeted metabolomic approach

Although thousands of different chemicals have been identified in cigarette smoke, the characterization of urinary metabolites derived from those compounds is still not completely achieved. The aim of this work was to perform an untargeted metabolomic experiment on a pilot cross-sectional study conducted on subjects with different smoking habits. Urine samples were collected from 67 adults; including 38 non-smokers, 7 electronic cigarette smokers, and 22 traditional tobacco smokers. Samples were analyzed by liquid chromatography/time-of flight mass spectrometer operating in data dependent mode. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The ANOVA test was used to detect significant features among groups. The software BEAMS (University of Birmingham) was implemented for grouping adducts and isotopes, and to perform a first annotation. Annotation was completed by comparing fragmentation patterns with on-line databases as Metlin, and using the software MS-FINDER. One hundred and seventeen features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites, for which we were able to putatively annotate about half. Among these, the identification of the glucuronide conjugated of 3-hydroxycotinine supports the validity of the proposed approach. Furthermore, several metabolites, mostly as sulfate conjugates, derived from chemicals known to be present in tobacco smoke, were annotated, among which the metabolite of methoxyphenol, acrolein, 1,3-butadiene, and crotonaldeide

Un approccio metabolomico non mirato per indagare l'esposizione a sostanze tossiche nel fumo di sigaretta

Introduzione: Nel fumo di sigaretta siano state identificate migliaia di diverse sostanze chimiche pericolose; ci\uf2 nonostante la caratterizzazione dei metaboliti urinari di queste sostanze a seguito di esposizione nell'uomo \ue8 stata effettuata sono parzialmente. Obiettivo: Lo studio si propone di applicare un approccio metabolomico non mirato all'analisi di campioni di urina di soggetti con diversa abitudine al fumo, allo scopo di identificare i metaboliti derivanti da sostanze tossiche associati. Metodi: Sono stati raccolti campioni estemporanei di urina da 67 soggetti suddivisi in tre gruppi sulla base della loro abitudine al fumo: 38 soggetti erano non fumatori, 7 erano fumatori di sigaretta elettronica e 22 erano fumatori di tabacco. I campioni sono stati analizzati utilizzando la cromatografia liquida accoppiata ad uno spettrometro di massa con tempo di volo, raccogliendo i segnali degli ioni negativi. I dati sono stati processati utilizzando i pacchetti R IPA e MXCMS per correggere i tempi di ritenzione ed effettuare l'allineamento tra i cromatogrammi. Il test ANOVA \ue8 stato utilizzato per identificare gli elementi caratteristici che distinguono tra loro i gruppi. Il software BEAMS, sviluppato dall'universit\ue0 di Birmingham, \ue8 stato applicato per raggruppare gli addotti e gli isotopi riferiti ad una stessa sostanza ed effettuare una prima annotazione dei picchi. L'annotazione \ue8 stata completata confrontando gli spettri di frammentazione ottenuti da standard puri e con il database Metlin, usando il software MS-FINDER Risultati: Nei cromatogrammi ottenuti sono stati identificati complessivamente 3613 segnali, di cui 117 sono risultati diversi nei gruppi studiati. Questi segnali sono stati attribuiti a circa 80 diversi metaboliti, dei quali siamo riusciti ad annotarne putativamente circa la met\ue0. L\u2019identificazione, con un grado di confidenza pari a 1, degli acidi mercapturici dell\u2019acroleina, del 1,3-butadiene, e della crotonaldeide, sostanze risaputamene presenti nel fumo di tabacco, supportano la validit\ue0 dell\u2019approccio adottato (il grado di confidenza 1 si attribuisce alle molecole identificate con certezza per confronto con lo standard puro). Con un grado di confidenza minore (pari a 2) sono state identificati: il coniugato glucuronide della 3-idrossicotinina e il coniugato solfato del metossifenolo. Infine, con un grado di confidenza 3, sono state identificate numerose altre piccole molecole, escrete come coniugati solfati. Conclusione: L\u2019approccio proposto sembra utile per indagare l\u2019esposizione a miscele di sostanze tossiche nell\u2019uomo. Dato che l\u2019esposizione a miscele di sostanze chimiche, piuttosto che a singoli composti, \ue8 una caratteristica peculiare di molti ambienti di lavoro, si reputa che questo approccio apra interessanti prospettive per la medicina del lavoro

State of nature 2019

State of Nature 2019 presents an overview of how the country’s wildlife is faring, looking back over nearly 50 years of monitoring to see how nature has changed in the UK, its Crown Dependencies and Overseas Territories. As well as this long-term view, we focus on what has happened in the last decade, and so whether things are getting better or worse for nature. In addition, we have assessed the pressures that are acting on nature, and the responses being made, collectively, to counter these pressures