(76g) "Invited Talk" Exhes Study Reveals the Impact of Prenatal Exposure to Metals, PFOS, PFOA, Organophosphates, and Organochlorines on Early Child Development.

Unravelling the exposome may be a cornerstone towards precision prevention in public health. The aim of this study was to established links between metabolic pathway perturbations and clinically observed phenotypes of neonates. The introduction of exposome studies to research started to make clear that the timing of exposure to environmental factors could be even more critical than the dose. Given the complexity of the individual lifetime exposome assessment, a broad array of technologies must be employed. The presented approach, developed in the framework of HEALS EU project, was applied to 50 mother-child pairs from Spain. The samples were collected in the framework of the HEALS pilot European Exposure and Health Examination Survey (EXHES).

Urine samples were thawed in controlled conditions. A quantity of 600 μL urine samples was centrifuged (10000 rpm for 10 min) to remove all precipitates. 500 μL of supernatant was placed on autosampler vials and diluted with 1000 μL of LC-MS water. Furthermore, a quantity of 50 μL was used for the preparation of the pooled quality control (QC) sample following the same procedure as the samples. The same procedure was followed for the preparation of the QC pooled samples taking a quantity of 50 μL of each sample. Serum samples were thawed in stable conditions at 4^οC. Immediately the samples were vortexed, and 1:3 methanol was added to perform protein precipitation. After centrifugation, at 15000rpm for 15 min, 300μl of the supernatant was transferred to dryness through a sample concentrator using N₂. The dry extracts were reconstituted with 100% water LC-MS grade with internal standards and vortex for 5 min. A final centrifuge of the samples at 15000rpm for 10 min was performed before transferring them to autosampler vials for analysis. A pooled QC sample was prepared following the procedure mentioned above.

An Agilent 1290 Infinity HPLC LC System coupled to an Agilent 6540 HRMS-QTOF/ LCMS system used for urinary and serum untargeted metabolomics analysis. Mobile Phase A was LC-MS grade water with 0.1% formic acid and mobile phase B was acetonitrile with 0.1% formic acid. The flow rate was set at 400 μL/min and 250 μL/min in case of urine and serum samples analysis, respectively. Chromatographic separations were achieved using an Acquity UPLC HSS T3 column (100 x 2.1 mm, 1.8 μm, Waters, Milford, MA, USA) maintained at a constant temperature of 40 ^oC. For the urine samples, the following gradient was used for both positive and negative mode: 1% B at 0 min, 1% B at 1 min, 15% B at 3 min, 50% B at 6 min, 95% B at 9 min, 95% B at 10 min, 1% B at 10.1 min and 1% B at 15 min. The following gradient was used for serum samples: 0% B at 0 min, 0% B at 5 min, 100% B at 20 min, 100% B at 30 min, 0% B at 33 min, 0% B at 35 min. The injection volume was 5 μL. The data are acquired between 50 and 1000 m/z at a scan rate of 1.5 spectra/s in centroid mode at a resolution of 40,000 FWHM. For urine samples analysis the following conditions were used: gas temperature 330 ^oC, drying gas 7 L/min, nebulizer 50 psig, fragmentor 200 V, skimmer 65 V, and capillary voltage 3500V or -3500V in positive or negative mode, respectively. The source conditions for serum samples analysis were as follows: gas temperature 300 ^oC, drying gas 7 L/min, nebulizer 50 psig, fragmentor 250 V, skimmer 65 V, and capillary voltage 3500V or -3500V in positive or negative mode, respectively.

Spectral processing was performed using the Bioconductor R - based packages XCMS and CAMERA. Briefly, following the import of the raw data in .mzML format using the ImportRawMSData function, an initial data inspection was performed using the base peak chromatograms of the samples, boxplots representing the distribution of total ion currents per file, and heatmaps. These plots were, also, USED to inform the setting of parameters downstream (e.g. the noise level, peak width, s/n). Chromatographic peak detection was performed based on the centWave algorithm. Internal standards were added to the QC samples to evaluate the expected mass error (ppm) and mzdiff, the most critical parameters. The Obiwarp method was used for alignment, and the retention time adjustment map was used as a diagnostic plot. The featureDefinitions function was used for the correspondence. We used the fillChromPeaks method to fill in intensity data for missing values from the original files due to false negatives. The PerformPeakAnnotation function used for isotope and adduct annotation using the CAMERA package. Also, An R package, called xMSannotator was used for Network-Based annotation. More specifically, the funtion multilevelannotation was used for metabolites annotation. The function uses a multi-level scoring algorithm to annotate features by comparing the MS/MS from the analyzed samples and the MS/MS from the online databases (HMDB, KEGG, and LipidMaps) and in-house build database. The querying was strict to metabolites that can be found in urine or blood To increase the confidence level of annotation. The adduct list used for database matching was set at c("all") for all possible positive or negative adducts. Mass tolerance in ppm for database matching was set based on the optimized calculated mass errors. Finally, in the case of HMDB only the "Detected" metabolites were considered. Then, a batch effect correction procedure was performed. The 80% rule was applied to the QC samples to obtain consistent variables. The instrument and overall process variability were then determined by calculating the median RSD for all endogenous metabolites for the cohorts under study. Enrichment and pathway analyses were performed using GeneSpring, which mapped significant biomarkers to known biochemical pathways based on the information contained in public databases (MetaCyc, Wikipathways, and KEGG). The Exposome-Wide Association Study (EWAS) approach was adopted to comprehensively and systematically explore and associate multiple exposure factors and modifiers discovering and replicating robust correlations with metabolites levels and dysregulated pathways. The ‘X-Wide Association Analyses (XWAS)’ package in R was used for this. Volcano plots and correlation globes were used for data visualization.

According to the results of the chemical analyses on the biosamples collected, the mean serum level of PFOS was higher than that of PFOA in analyzed samples. In urine samples of the 1^st, 2^d and 3^d trimester, 2-diethylamino-6-methylpyrimidin-4-ol was the most abundant organophosphate. Among organochlorines, 4,4'-DDE and PCB101 showed respectively the highest and lowest mean serum value in the 1^st-trimester samples. Also, in the 2^d and 3^d trimester samples, 4,4'-DDE was the most abundant organochlorine compound. The most abundant metals in urine samples of the 1^st, 2^d and 3^d trimester were Zn, As and Se. In these samples, V, Cr and Mn showed the lowest levels. In serum samples, Zn, Cu, and Se had the highest levels while V, Co and Cr had the lowest levels.

Metabolite identification revealed that the total number of unique annotated metabolites in urine and serum samples analysis using LC-HRMS was 751, and 7830, respectively. The biomarkers of interest detected in the Spain samples through serum untargeted metabolomics analysis were the citric acid, which is participating in the TCA cycle, the amino acids L-Citrulline and L-Arginine, which are involving in the Citrulline-Nitric Oxide cycle Pathway, the L-Glutamine found in S-methyl-5thio-Alpha-Dribose 1 -Phosphate Degradation Pathway. Finally, S-Adenosylhomocysteine, and 5-Hydroxyindoleacetic acid, are both metabolites of the Folate metabolism Pathway. The urinary biomarkers of interest detected in the samples from Spain were the Citric acid, Oxoglutaric acid, trans-Aconitate, and Isocitrate, which are involving in the TCA cycle, the amino acids L-Tyrosine and 5-Hydroxy-L-tryptophan, the hippuric acid participating in the Phenylalanine metabolism, the N6,N6,N6-trimethyl-L-lysine, which is one of the Serotonine’s degradation Pathway metabolites. Moreover, hexanoglycine was detected, as a metabolite of Fatty acid Pathway, the N-Acetylglutamic acid involving in the S-methyl-5thio_Alpha-Dribose 1 -Phosphate Degradation Pathway, and the 5-Hydroxyindoleacetic acid, which is involved in the Serotonine Degradation Pathway. These metabolites are linked either with health outcomes, such as neurodevelopmental disorders, overweight, obesity and diabetes, or with exposure to endocrine disruptors, according to previously published data.

The detected metabolites on serum samples were mapped on 246 pathways, while urinary metabolites on 163.

According to EWAS analysis, birth weight is positively affected by S-Adenosylhomocysteine levels during the first trimester of pregnancy and negatively associated with the levels of Citrulline, and DEAMPY, at delivery. In addition, higher exposure levels to Hexachlorocyclohexane (HCH), 2,2',4,5,5'-Pentachlorobiphenyl (PCB101) and 2.4’-DDT, can lead to height increasement. The same outcome is associated with citric acid levels. Head circumference is positively associated with exposure to 4.4’-DDT at the first trimester. Overall, functionally coupling advanced bioinformatics algorithms applied to omics data with exposome-derived information on exposures and health indicators can support the high-dimension-biology-based association of environmental exposures and adverse health outcomes in early life.

Overall, functionally coupling advanced bioinformatics algorithms applied to omics data with exposome-derived information on exposures and health indicators can support the high-dimension-biology-based association of environmental exposures and adverse health outcomes in early life.