Multi-omics signatures of the human early life exposome

Maitre, Léa; Hernández-Ferrer, Carles; Thiel, Denise; Lau, Chung-Ho E; Siskos, Alexandros P.; Vives-Usano, Marta; Ruiz‐Arenas, Carlos; Pelegrí-Sisó, Dolors; Robinson, Oliver; Mason, Dan; Wright, John; Cadiou, Solène; Slama, Rémy; Heude, Barbara; Casas, Maribel; Sunyer, Jordi; Papadopoulou, Eleni; Gützkow, Kristine B.; Andrušaitytė, Sandra; Gražulevičienė, Regina; Vafeiadi, Marina; Chatzi, Leda; Sakhi, Amrit Kaur; Thomsen, Cathrine; Tamayo, Ibón; Nieuwenhuijsen, Mark; Urquiza, José; Borràs, Eva; Sabidó, Eduard; Quintela, Inés; Carracedo, Ángel; Estivill, Xavier; Coen, Muireann; González, Juan R.; Keun, Hector C.; Vrijheid, Martine

doi:10.1038/s41467-022-34422-2

Cited by 75 publications

(40 citation statements)

References 116 publications

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“…As such, the proteome and metabolome are the closest omics to phenotype (and thus, perhaps most useful as a biomarker of disease risk or prognosis) but also exhibit the most interand intra-individual variability (Gallego-Paüls et al, 2021), thereby making replication and validation more challenging. However, result consistency across layers, if it occurs, reinforces causality, as we demonstrated in our recent multi-omics study of the early life exposome (Maitre, Bustamante, et al, 2022).…”

Section: Quality Of Evidence and Risk Of Biassupporting

confidence: 80%

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review

Maitre

Jedynak

Gallego³

et al. 2022

Preprint

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Health effects of endocrine disrupting chemicals (EDCs) are challenging to detect in the general population. Omics technologies become increasingly common to identify early biological changes before the apparition of clinical symptoms, to explore toxic mechanisms and to increase biological plausibility of epidemiological associations. This scoping review systematically summarises the application of omics in epidemiological studies assessing EDCs-associated biological effects to identify potential gaps and priorities for future research. Ninety-eight human studies (2004–2021) were identified through database searches (PubMed, Scopus) and citation chaining and focused on phthalates (34 studies), phenols (19) and PFASs (17), while PAHs (12) and recently-used pesticides (3) were less studied. The sample sizes ranged from 10 to 12,476 (median = 159), involving non-pregnant adults (38), pregnant women (11), children/adolescents (15) or both populations studied together (23). Several studies included occupational workers (10) and/or highly exposed groups (11) focusing on PAHs, PFASs and pesticides, while studies on phenols and phthalates were performed in the general population only. Analysed omics layers included metabolic profiles (30, including 14 targeted analyses), miRNA (13), gene expression (11), DNA methylation (8), microbiome (5) and proteins (3). Twenty-one studies implemented targeted multi-assays focusing on clinical routine blood lipid traits, oxidative stress or hormones. Overall, DNA methylation and gene expression associations with EDCs did not overlap across studies, while some EDC-associated metabolite groups, such as carnitines, nucleotides and amino acids in untargeted metabolomic studies, and oxidative stress markers through targeted studies were consistent across studies. Studies had common limitations such as small sample sizes, cross-sectional designs and single sampling for exposure biomonitoring. In conclusion, there is a growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a need for larger longitudinal studies, wider coverage of exposures and biomarkers, replication studies and standardisation of research methods and reporting.

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Section: Quality Of Evidence and Risk Of Biassupporting

confidence: 80%

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review

Maitre

Jedynak

Gallego³

et al. 2022

Preprint

Self Cite

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“…Furthermore, epigenetics might play a role in connecting the environments to PTB (70) . A multi-omics study found that maternal Mo exposure during pregnancy was related to the methylation levels of 72 CpGs representing sixty-three loci, and thirteen of them have previously been related to gestational age (71) . Another study suggested that maternal Cd exposure was associated with a decrease in gestational age through an alteration in DNA methylation at a specific CpG site, cg21010642 (72) .…”

Section: Discussionmentioning

confidence: 99%

Inverse association between maternal serum concentrations of trace elements and risk of spontaneous preterm birth: a nested case–control study in China

Li,

Yu,

Liu

et al. 2024

Br J Nutr

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Few studies have evaluated the joint effect of trace elements on spontaneous preterm birth (SPTB).This study aimed to examine the relationships between the individual or mixed maternal serum concentrations of iron (Fe), copper (Cu), zinc (Zn), selenium (Se), strontium (Sr) and molybdenum (Mo) during pregnancy, and risk of SPTB. Inductively coupled plasma mass spectrometry was employed to determine maternal serum concentrations of the six trace elements in 192 cases with SPTB and 282 controls with full-term delivery. Multivariate logistic regression, weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR) were used to evaluate the individual and joint effects of trace elements on SPTB. The median concentrations of Sr and Mo were significantly higher in controls than in SPTB group (P < 0.05). In multivariate logistic regression analysis, compared with the lowest quartile levels of individual trace elements, the third- and fourth-quartile Sr or Mo concentrations were significantly associated with reduced risk of SPTB with adjusted odds ratios (aORs) of 0.432 (95% confidence interval (CI): 0.247-0.756), 0.386 (95% CI: 0.213-0.701), 0.512 (95% CI:0.297-0.883), and 0.559 (95% CI: 0.321-0.972), respectively. WQSR revealed the inverse combined effect of the trace elements mixture on SPTB (aOR: 0.368, 95%CI: 0.228-0.593). BKMR analysis confirmed the overall mixture of the trace elements was inversely associated with the risk of SPTB, and the independent effect of Sr and Mo was significant. Our findings suggest that the risk of SPTB decreased with concentrations of the six trace elements, with Sr and Mo being the major contributors.

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“…This integration has not been achieved before IDSL.GOA since there was not a single knowledgebase developed that have created the GO-term specific focused reaction networks. IDSL.GOA can also support the statistical multi-omics analysis to extract meaningful biological information from correlated features across multiple omics datasets 31 . By integrating the results of IDSL.GOA with the results of statistical multi-omics analysis, researchers can obtain a more comprehensive understanding of the underlying biological mechanisms that are involved in the disease or condition of interest.…”

Section: Discussionmentioning

confidence: 99%

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

Mahajan

Fiehn

Barupal

2023

Preprint

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Biological interpretation of metabolomics datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, the map definitions and the metabolite coverage vary among different curated databases, leading to inaccurate and contradicting interpretations. For the lists of gene, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for the biological interpretation. GO terms are not limited to predefined pathways but can also include relevant metabolic processes that are not included in pathway databases. Despite the several advantages of GO terms over traditional pathway maps, GO analysis has not been achieved for metabolomics datasets. To overcome this, we present a new knowledgebase and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA knowledgebase covers 2,324 metabolic GO terms and associated 2,818 genes, 22,264 transcripts, 20,158 proteins, 1,482 EC annotations, 2,430 reactions and 2,212 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted over 250 GO terms being significantly overrepresented (FDR <0.05). The analysis suggested that in the older female brain cortex region, nucleotide salvage processes are severely affected. On contrast, for the same metabolite list, MetaboAnalyst and Reactome Pathway Analysis suggested less than 5 pathways at FDR <0.05, and none of them were related to nucleotide salvage pathways. We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not mentioned by a commonly used pathway analysis approach. Overall, we suggest that metabolomics researchers should not limit the interpretation of metabolite lists to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a powerful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/

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Multi-omics signatures of the human early life exposome

Cited by 75 publications

References 116 publications

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review

Inverse association between maternal serum concentrations of trace elements and risk of spontaneous preterm birth: a nested case–control study in China

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

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