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

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

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

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Integrating sequence and array data to create an improved 1000 Genomes Project haplotype reference panel

A major use of the 1000 Genomes Project (1000GP) data is genotype imputation in genome-wide association studies (GWAS). Here we develop a method to estimate haplotypes from low-coverage sequencing data that can take advantage of single-nucleotide polymorphism (SNP) microarray genotypes on the same samples. First the SNP array data are phased to build a backbone (or 'scaffold') of haplotypes across each chromosome. We then phase the sequence data 'onto' this haplotype scaffold. This approach can take advantage of relatedness between sequenced and non-sequenced samples to improve accuracy. We use this method to create a new 1000GP haplotype reference set for use by the human genetic community. Using a set of validation genotypes at SNP and bi-allelic indels we show that these haplotypes have lower genotype discordance and improved imputation performance into downstream GWAS samples, especially at low-frequency variants. © 2014 Macmillan Publishers Limited. All rights reserved

Identification of common genetic risk variants for autism spectrum disorder

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.Peer reviewe

Phase Behavior of Aqueous Na-K-Mg-Ca-CI-NO3 Mixtures: Isopiestic Measurements and Thermodynamic Modeling

A comprehensive model has been established for calculating thermodynamic properties of multicomponent aqueous systems containing the Na{sup +}, K{sup +}, Mg{sup 2+}, Ca{sup 2+}, Cl{sup -}, and NO{sub 3}{sup -} ions. The thermodynamic framework is based on a previously developed model for mixed-solvent electrolyte solutions. The framework has been designed to reproduce the properties of salt solutions at temperatures ranging from the freezing point to 300 C and concentrations ranging from infinite dilution to the fused salt limit. The model has been parameterized using a combination of an extensive literature database and new isopiestic measurements for thirteen salt mixtures at 140 C. The measurements have been performed using Oak Ridge National Laboratory's (ORNL) previously designed gravimetric isopiestic apparatus, which makes it possible to detect solid phase precipitation. Water activities are reported for mixtures with a fixed ratio of salts as a function of the total apparent salt mole fraction. The isopiestic measurements reported here simultaneously reflect two fundamental properties of the system, i.e., the activity of water as a function of solution concentration and the occurrence of solid-liquid transitions. The thermodynamic model accurately reproduces the new isopiestic data as well as literature data for binary, ternary and higher-order subsystems. Because of its high accuracy in calculating vapor-liquid and solid-liquid equilibria, the model is suitable for studying deliquescence behavior of multicomponent salt systems

Clinical characteristics of women captured by extending the definition of severe postpartum haemorrhage with 'refractoriness to treatment': a cohort study

Background: The absence of a uniform and clinically relevant definition of severe postpartum haemorrhage hampers comparative studies and optimization of clinical management. The concept of persistent postpartum haemorrhage, based on refractoriness to initial first-line treatment, was proposed as an alternative to common definitions that are either based on estimations of blood loss or transfused units of packed red blood cells (RBC). We compared characteristics and outcomes of women with severe postpartum haemorrhage captured by these three types of definitions. Methods: In this large retrospective cohort study in 61 hospitals in the Netherlands we included 1391 consecutive women with postpartum haemorrhage who received either ≥4 units of RBC or a multicomponent transfusion. Clinical characteristics and outcomes of women with severe postpartum haemorrhage defined as persistent postpartum haemorrhage were compared to definitions based on estimated blood loss or transfused units of RBC within 24 h following birth. Adverse maternal outcome was a composite of maternal mortality, hysterectomy, arterial embolisation and intensive care unit admission. Results: One thousand two hundred sixty out of 1391 women (90.6%) with postpartum haemorrhage fulfilled the definition of persistent postpartum haemorrhage. The majority, 820/1260 (65.1%), fulfilled this definition within 1 h following birth, compared to 819/1391 (58.7%) applying the definition of ≥1 L blood loss and 37/845 (4.4%) applying the definition of ≥4 units of RBC. The definition persistent postpartum haemorrhage captured 430/471 adverse maternal outcomes (91.3%), compared to 471/471 (100%) for ≥1 L blood loss and 383/471 (81.3%) for ≥4 units of RBC. Persistent postpartum haemorrhage did not capture all adverse outcomes because of missing data on timing of initial, first-line treatment. Conclusion: The definition persistent postpartum haemo

Genome-wide by Environment Interaction Studies of Depressive Symptoms and Psychosocial Stress in UK Biobank and Generation Scotland

Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population-based cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77 x 10(-6)). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53-kb downstream PIWIL4; p = 4.95 x 10(-9); total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46 x 10(-8); dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00 x 10(-8); dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77 x 10(-6)). Polygenic risk scores (PRSs) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91 x 10(-3)). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions