CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Abstract

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Keywords: Biomonitoring; Database; Exposomics; Inter-chemical correlation; Metabolic pathways; Metabolomics; NHANES; Software.

Fig. 1.

Probable interpretations of correlation in targeted and untargeted GC/LC-HRMS datasets.

Fig. 2.

Prevalence of strong inter-chemical correlations across 35 studies in the CCDB. These are unique correlations. See the Table 1 for the description of each study and number of compounds. Table S3 shows the chemical detection rate across the indexed studies.

Fig. 3.

Correlations among chemicals within a class or having same source origin in the NHANES dataset. The correlation cutoff was 0.3 for PCB, PFC and Tobacco compounds, and 0.4 for PAHs. Online network can be accessed at the site - https://chemcor.idsl.site/originaldata/biomonitoring/#?studyid=NHANES. Edge thickness shows the correlation strength, by only the minimum and maximum correlation values are labelled on the edges for clarity. Thickness of edges are not comparable in two network figures. Abbreviations: Perfluorodecanoic acid (PFDeA), Perfluorohexane sulfonic acid (PFHxS), Perfluorononanoic acid (PFNA), Perfluoroundecanoic acid (PFUA), n-perfluorooctanoic acid (n-PFOA), n-perfluorooctane sulfonic acid (n-PFOS), Perfluoromethylheptane sulfonic acid isomers (SmPFOS), Polychlorinated Biphenyls (PCB); polyaromatic hydrocarbons (PAH), Perfluorinated compounds (PFC).

Fig. 4.

Compounds correlation with acylcarnitine 16:0 in the study ST002089. Edge thickness shows the correlation strength, by only the minimum and maximum correlation values are labelled on the edges for clarity. Thickness of edges are not comparable in two network figures. Abbreviations: acyl-carnitines (AC). Fatty acid (FA), glycerophosphoethanolamine (GPE), glycerophosphocholine (GPC).

Fig. 5.

Caffeine and phthalate metabolites in the NHANES survey data. Variable id URXMBP_PHTHTE_D (year 2005–2006) was used for mono-n-butyl phthalate (MnBP). Variable id URXMX7_CAFE_H (year 2013–2014) was used for caffeine. Label on the edges show the Pearson coefficient. Edge thickness shows the correlation strength, by only the minimum and maximum correlation values are labelled on the edges for clarity. Thickness of edges are not comparable in two network figures. Abbreviations: acetylamino-6-formylamino-3-methyluracil(AAMU).

Fig. 6.

Inter-chemical correlation among PFCs in the untargeted metabolomics datasets. Correlation threshold for ST001430 was 0.3 and for 0.6 for ST001231. White color node mean it was detected in by the reverse phase ESI (−) mode and a grey node means it was detected by a reverse phrase ESI (+) mode. Edge thickness shows the correlation strength, by only the minimum and maximum correlation values are labelled on the edges for clarity. Thickness of edges are not comparable in two network figures.

Fig. 7.

Chemical similarity enrichment analysis of PFOA and its correlation with other metabolites in the IDSLCCDB00001 study.