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. 2015 Oct:83:137-45.
doi: 10.1016/j.envint.2015.05.010. Epub 2015 Jun 29.

Reconstructing pre-natal and early childhood exposure to multi-class organic chemicals using teeth: Towards a retrospective temporal exposome

Syam S Andra  1 , Christine Austin  2 , Robert O Wright  1 , Manish Arora  3
Affiliations

Reconstructing pre-natal and early childhood exposure to multi-class organic chemicals using teeth: Towards a retrospective temporal exposome

Syam S Andra et al. Environ Int. 2015 Oct.
No abstract available

Keywords: Biomarker; Children; Dentine; Exposome; Metabolomics; Non-targeted profiling; Organics; Prenatal; Teeth.

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Figures

Fig. 1
Fig. 1. Schematic of tooth development(Arora and others 2012)
(a) Earliest deposition of dentine (grey area) at DEJ at cusp tip (b) Continued extension of dentine (and enamel) towards the tooth cervix. (c) Neonatal line (NL), a histological feature, formed at the time of birth (d) Completion of enamel and primary dentine formation between 2 to 11 postnatal months depending on tooth type. Secondary dentine continues forming at pulpal margin (not shown). (e) Confocal laser scanning micrograph of NL in enamel. Reprinted with permission from Environ. Sci. Technol., 2012, 46 (9), pp 5118–5125. Copyright 2012 American Chemical Society."
Fig. 2
Fig. 2
Conceptual framework of how blood, urine, and deciduous teeth collected postnatally compare in estimating prenatal exposure to chemicals that have a short half-life. Grey shaded area indicates time periods for which exposure information is missing when using a biomarker collected in childhood.
Fig. 3
Fig. 3
Intensity and distribution of ‘known unknowns’ and ‘suspected unknown’ compounds in the pre- and postnatal tooth layers of the study children. QTOF-MS was operated in a dual electrospray ionization mode (positive and negative mode).
Fig. 4
Fig. 4
Scatter plots of retention time (min.) versus accurate mass (single isotopic m/z) of the top 100 ‘unknown unknowns’ obtained using the Batch Recursive Feature Extraction, and detected in both negative and positive electrospray ionization mode. Data from two children is depicted for graphical representation of chemical composition differences observed within (prenatal versus postnatal) and between (Child C versus E) children.
Fig. 5
Fig. 5
Venn representation of the common and dissimilar peaks among the pre- and post-natal dentine layers of two representative children from the study in both negative [A] and positive [B] electrospray ionization mode. The 100 most prevalent ‘unknown unknowns’ were obtained using the Batch Recursive Feature Extraction. We observed that only a small proportion of peaks were common between pre- and postnatal periods within the same child’s samples (for example, in negative mode, Child C had only 7% common peaks between the pre- and postnatal periods). Furthermore, when comparing the same time periods between children, we saw that only a small proportion of peaks were highly abundant for both children (for example, in negative mode, only 19% of peaks were common for both children during the prenatal period). These data rule out that the predominant compounds we have detected are generic housekeeping components of our matrix.
See this image and copyright information in PMC

References

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