Amanida: an R package for meta-analysis of metabolomics non-integral data.

SUMMARY: The combination, analysis and evaluation of different studies which try to answer or solve the same scientific question, also known as a meta-analysis, plays a crucial role in answering relevant clinical relevant questions. Unfortunately, metabolomics studies rarely disclose all the statistical information needed to perform a meta-analysis. Here, we present a meta-analysis approach using only the most reported statistical parameters in this field: P-value and fold-change. The P-values are combined via Fisher's method and fold-changes by averaging, both weighted by the study size (n). The amanida package includes several visualization options: a volcano plot for quantitative results, a vote plot for total regulation behaviours (up/down regulations) for each compound, and a explore plot of the vote-counting results with the number of times a compound is found upregulated or downregulated. In this way, it is very easy to detect discrepancies between studies at a first glance. AVAILABILITY AND IMPLEMENTATION: Amanida code and documentation are at CRAN and https://github.com/mariallr/amanida. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The untargeted urine volatilome for biomedical applications: methodology and volatilome database.

Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer.

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.

Mutations in TRAPPC11 are associated with a congenital disorder of glycosylation.

Congenital disorders of glycosylation (CDG) are a heterogeneous and rapidly growing group of diseases caused by abnormal glycosylation of proteins and/or lipids. Mutations in genes involved in the homeostasis of the endoplasmic reticulum (ER), the Golgi apparatus (GA), and the vesicular trafficking from the ER to the ER-Golgi intermediate compartment (ERGIC) have been found to be associated with CDG. Here, we report a patient with defects in both N- and O-glycosylation combined with a delayed vesicular transport in the GA due to mutations in TRAPPC11, a subunit of the TRAPPIII complex. TRAPPIII is implicated in the anterograde transport from the ER to the ERGIC as well as in the vesicle export from the GA. This report expands the spectrum of genetic alterations associated with CDG, providing new insights for the diagnosis and the understanding of the physiopathological mechanisms underlying glycosylation disorders.

SPME arrow-based extraction for enhanced targeted and untargeted urinary volatilomics.

BACKGROUND: Volatile organic compounds (VOCs) present in human urine are promising biomarkers for various health conditions and environmental exposures. However, their reliable detection is challenging due to the complexity of urinary matrices and the low concentrations of VOCs. Moreover, untargeted approaches present considerable challenges in terms of data interpretation, increasing the complexity of method development. Here we address these challenges by developing a new method that combines solid-phase microextraction (SPME) Arrow with gas chromatography-high resolution mass spectrometry (GC-HRMS), using a design of experiments (DOE) approach for targeted and untargeted compounds. This methodology, specifically tailored for SPME Arrow, represents a significant advancement in untargeted urinary analysis. RESULTS: The method was developed based on targeted and untargeted outcomes, were ranking results focus on the highest response area of 11 spiked target VOCs representative of urinary volatilomics, and on identifying the maximum untargeted number of VOCs. The method was developed focusing on the highest response area of 11 spiked target VOCs representative of urinary volatilomics and identifying the maximum number of VOCs. A univariate method determined the optimal coating type, urine volume, and salt addition. Subsequently, a central composite design (CCD) DOE was used to determine ideal temperature, extraction, and incubation times. The best method obtained has an extraction time of 60 min at a temperature of 53 °C, with an SPME Arrow CAR/PDMS using 2 mL of urine, with 0.25 % w/v of NaCl and a pH of 2. Compared to conventional SPME fibers, the SPME Arrow showed improved extraction efficiency, detecting more VOCs. Finally, the enhanced method was successfully applied to urine samples from children exposed and non-exposed to tobacco smoke, identifying specific VOCs, like p-cymene and p-isopropenyl toluene related to tobacco exposure. SIGNIFICANCE: By integrating both targeted and untargeted approaches, the developed method comprehensively captures the complexity of urinary metabolomics. This dual strategy ensures the precise identification of known compounds and the discovery of novel biomarkers, thereby providing a more complete metabolic profile. Such an approach is crucial for advancing in non-invasive diagnostics and environmental health studies, as it offers deeper insights into the intricate relationships between metabolic processes and various health conditions.

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison.

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

Influence of perinatal and childhood exposure to tobacco and mercury in children's gut microbiota.

Background: Early life determinants of the development of gut microbiome composition in infants have been widely investigated; however, if early life pollutant exposures, such as tobacco or mercury, have a persistent influence on the gut microbial community, its stabilization at later childhood remains largely unknown. Objective: In this exposome-wide study, we aimed at identifying the contribution of exposure to tobacco and mercury from the prenatal period to childhood, to individual differences in the fecal microbiome composition of 7-year-old children, considering co-exposure to a width of established lifestyle and clinical determinants. Methods: Gut microbiome was studied by 16S rRNA amplicon sequencing in 151 children at the genus level. Exposure to tobacco was quantified during pregnancy through questionnaire (active tobacco consumption, second-hand smoking -SHS) and biomonitoring (urinary cotinine) at 4 years (urinary cotinine, SHS) and 7 years (SHS). Exposure to mercury was quantified during pregnancy (cord blood) and at 4 years (hair). Forty nine other potential environmental determinants (12 at pregnancy/birth/infancy, 15 at 4 years and 22 at 7 years, such as diet, demographics, quality of living/social environment, and clinical records) were registered. We used multiple models to determine microbiome associations with pollutants including multi-determinant multivariate analysis of variance and linear correlations (wUnifrac, Bray-Curtis and Aitchison ß-diversity distances), single-pollutant permutational multivariate analysis of variance adjusting for co-variates (Aitchison), and multivariable association model with single taxa (MaAsLin2; genus). Sensitivity analysis was performed including genetic data in a subset of 107 children. Results: Active smoking in pregnancy was systematically associated with microbiome composition and ß-diversity (R2 2-4%, p < 0.05, Aitchison), independently of other co-determinants. However, in the adjusted single pollutant models (PERMANOVA), we did not find any significant association. An increased relative abundance of Dorea and decreased relative abundance of Akkermansia were associated with smoking during pregnancy (q < 0.05). Discussion: Our findings suggest a long-term sustainable effect of prenatal tobacco exposure on the children's gut microbiota. This effect was not found for mercury exposure or tobacco exposure during childhood. Assessing the role of these exposures on the children's microbiota, considering multiple environmental factors, should be further investigated.

Analysis of DNA methylation at birth and in childhood reveals changes associated with season of birth and latitude.

BACKGROUND: Seasonal variations in environmental exposures at birth or during gestation are associated with numerous adult traits and health outcomes later in life. Whether DNA methylation (DNAm) plays a role in the molecular mechanisms underlying the associations between birth season and lifelong phenotypes remains unclear. METHODS: We carried out epigenome-wide meta-analyses within the Pregnancy And Childhood Epigenetic Consortium to identify associations of DNAm with birth season, both at differentially methylated probes (DMPs) and regions (DMRs). Associations were examined at two time points: at birth (21 cohorts, N = 9358) and in children aged 1-11 years (12 cohorts, N = 3610). We conducted meta-analyses to assess the impact of latitude on birth season-specific associations at both time points. RESULTS: We identified associations between birth season and DNAm (False Discovery Rate-adjusted p values < 0.05) at two CpGs at birth (winter-born) and four in the childhood (summer-born) analyses when compared to children born in autumn. Furthermore, we identified twenty-six differentially methylated regions (DMR) at birth (winter-born: 8, spring-born: 15, summer-born: 3) and thirty-two in childhood (winter-born: 12, spring and summer: 10 each) meta-analyses with few overlapping DMRs between the birth seasons or the two time points. The DMRs were associated with genes of known functions in tumorigenesis, psychiatric/neurological disorders, inflammation, or immunity, amongst others. Latitude-stratified meta-analyses [higher (≥ 50°N), lower (< 50°N, northern hemisphere only)] revealed differences in associations between birth season and DNAm by birth latitude. DMR analysis implicated genes with previously reported links to schizophrenia (LAX1), skin disorders (PSORS1C, LTB4R), and airway inflammation including asthma (LTB4R), present only at birth in the higher latitudes (≥ 50°N). CONCLUSIONS: In this large epigenome-wide meta-analysis study, we provide evidence for (i) associations between DNAm and season of birth that are unique for the seasons of the year (temporal effect) and (ii) latitude-dependent variations in the seasonal associations (spatial effect). DNAm could play a role in the molecular mechanisms underlying the effect of birth season on adult health outcomes.

Meta-analysis of epigenome-wide association studies in newborns and children show widespread sex differences in blood DNA methylation.

BACKGROUND: Among children, sex-specific differences in disease prevalence, age of onset, and susceptibility have been observed in health conditions including asthma, immune response, metabolic health, some pediatric and adult cancers, and psychiatric disorders. Epigenetic modifications such as DNA methylation may play a role in the sexual differences observed in diseases and other physiological traits. METHODS: We performed a meta-analysis of the association of sex and cord blood DNA methylation at over 450,000 CpG sites in 8438 newborns from 17 cohorts participating in the Pregnancy And Childhood Epigenetics (PACE) Consortium. We also examined associations of child sex with DNA methylation in older children ages 5.5-10 years from 8 cohorts (n = 4268). RESULTS: In newborn blood, sex was associated at Bonferroni level significance with differences in DNA methylation at 46,979 autosomal CpG sites (p < 1.3 × 10-7) after adjusting for white blood cell proportions and batch. Most of those sites had lower methylation levels in males than in females. Of the differentially methylated CpG sites identified in newborn blood, 68% (31,727) met look-up level significance (p < 1.1 × 10-6) in older children and had methylation differences in the same direction. CONCLUSIONS: This is a large-scale meta-analysis examining sex differences in DNA methylation in newborns and older children. Expanding upon previous studies, we replicated previous findings and identified additional autosomal sites with sex-specific differences in DNA methylation. Differentially methylated sites were enriched in genes involved in cancer, psychiatric disorders, and cardiovascular phenotypes.

Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis.

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles.