Exposome Journal Club

In the Spring 2021 semester, the Columbia University Department of Environmental Health Sciences Journal Club will focus on the exposome. We will be posting information about the course as the semester progresses. 

January 11, 2021       1. Introduction and the Wild papers

*Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 2005 Aug;14(8):1847-50. doi: 10.1158/1055-9965.EPI-05-0456. PMID: 16103423.

Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012 Feb;41(1):24-32. doi: 10.1093/ije/dyr236. Epub 2012 Jan 31. PMID: 22296988.

In our first session, Dr. Miller gave an overview of the course structure and discussed these two key papers from Dr. Chris Wild. He spent most of the time on the 2005 paper which introduced the term and laid the foundation for the establishment of the field. It is hard to believe that it was published over 15 years ago. 

January 25, 2021  2. The Berkeley Papers. 

Rappaport SM, Smith MT. Epidemiology. Environment and disease risks. Science. 2010 Oct 22;330(6003):460-1. doi: 10.1126/science.1192603. PMID: 20966241; PMCID: PMC4841276.*

*Rappaport SM. Implications of the exposome for exposure science. J Expo Sci Environ Epidemiol. 2011 Jan-Feb;21(1):5-9. doi: 10.1038/jes.2010.50. Epub 2010 Nov 17. PMID: 21081972.

Rappaport SM. Genetic Factors Are Not the Major Causes of Chronic Diseases. PLoS One. 2016 Apr 22;11(4):e0154387. doi: 10.1371/journal.pone.0154387. PMID: 27105432; PMCID: PMC4841510.

In our second meeting, Dr. Miller discussed the 5 year hiatus from the original Wild paper and highlighted the work of Steve Rappaport and Martyn Smith at UC Berkeley who helped advance the exposome concept in the U.S. We discussed the 2010 NAS meeting on the exposome and how 2010 represented a new beginning in the field.

February 1, 2021       3. Nature vs. Nurture,  Exposure vs. Biology

Miller GW, Jones DP. The nature of nurture: refining the definition of the exposome. Toxicol Sci. 2014 Jan;137(1):1-2. doi: 10.1093/toxsci/kft251. Epub 2013 Nov 9. PMID: 24213143; PMCID: PMC3871934.

Dennis KK, Auerbach SS, Balshaw DM, Cui Y, Fallin MD, Smith MT, Spira A, Sumner S, Miller GW. The Importance of the Biological Impact of Exposure to the Concept of the Exposome. Environ Health Perspect. 2016 Oct;124(10):1504-1510. doi: 10.1289/EHP140. Epub 2016 Jun 3. PMID: 27258438; PMCID: PMC5047763.

The group discussed why was it important to expand the definition of the exposome. For example, the exposome had the potential to be more than an issue of exposure science and epidemiology-it could be used as a broader biological construct. The primary differences from the Wild definition, biological response, behavior, and endogenous processes, were outlined. The group went into depth about the meaning of behavior. Specifically, this was intended to include actions initiated by the individual as well as external forces exerted upon the individual, i.e. built environment, social constructs, access to care, etc. 

February 8, 2021. Capturing the external forces

Escher BI et al. From the exposome to mechanistic understanding of chemical-induced adverse effects. Environ Int. 2017 Feb;99:97-106

This article describes the potential of integrating adverse outcome pathway (AOP) and aggregate exposure pathway (AEP) concepts from (exo)toxicology with exposome research strategies. The authors suggest that AOPs can enhance the exposome approach by providing a mechanistic understanding of chemical exposures and adverse outcomes. Exposomal studies may similarly expand AOP concepts by accounting for mixture effects in space and time, non-linear relationships, as well as multiple chemical and non-chemical stressors. To best characterize exposure sources and their mechanisms of action, the authors advocate for synergic methods to be used in the study of stressor-induced adverse health outcomes.

Mueller W, Steinle S, Pärkkä J, Parmes E, Liedes H, Kuijpers E, Pronk A, Sarigiannis D, Karakitsios S, Chapizanis D, Maggos T, Stamatelopoulou A, Wilkinson P, Milner J, Vardoulakis S, Loh M. Urban greenspace and the indoor environment: Pathways to health via indoor particulate matter, noise, and road noise annoyance. Environ Res. 2020 Jan;180:108850.

This study determined the effect of greenspace, defined through Normalized DIfference Vegetation Index (NDVI), tree-cover density, and green land-use, on indoor air pollution (PM2.5), indoor noise levels (dB) and a self-reported noise annoyance measure. Study recruitment and exposure monitoring assessments were performed on 131 households across 4 European cities, under the Health and Environment-wide Association based on Large population Surveys (HEALS) study. Overall, the relationship between greenspace measures pollution/noise measures varied, with only summer-NDVI statistically significantly and inversely associated with PM2.5 levels, and summer-NDVI and winter-NDVI statistically significantly and inversely associated with self-reported noise annoyance.

February 15, 2021. Exposome databases-part 1.

Wishart D, Arndt D, Pon A, Sajed T, Guo AC, Djoumbou Y, Knox C, Wilson M, Liang Y, Grant J, Liu Y, Goldansaz SA, Rappaport SM. T3DB: the toxic exposome database. Nucleic Acids Res. 2015 Jan;43(Database issue):D928-34. doi: 10.1093/nar/gku1004. Epub 2014 Nov 5. PMID: 25378312; PMCID: PMC4383875.

This article describes updates to the Toxin-Toxin-Target Database (T3DB), a resource for looking up information about the toxic exposome. The T3DB was released in 2010 containing data on nearly 2900 common toxic substances along with detailed information on their chemical properties, descriptions, targets, toxic effects, toxicity thresholds, sequences (for both targets and toxins), and mechanisms. This 2015 update improves on the original T3DB in two ways: 1) by adding data on new compounds, and 2) by adding new types of data for each compound. First, the list of indexed compounds has been expanded to include relatively benign, naturally occurring or chronically toxic compounds such as glucose, fructose and cholesterol. This change reflects changes in the fields of molecular epidemiology, metabolomics and exposome science, which are increasingly shifting research interest toward chronic, minimally toxic exposures rather than just acute toxins. These additions also reflect an increasingly expanded definition of the exposome which includes endogenous compounds. Second, new information on each compound has been added. Of note, T3DB now also included gene expression data, normal and abnormal concentration ranges in different biofluids, and an updated taxonomy of chemical compounds. 

February 22, 2021. Exposome databases-part 2.

Neveu V, Nicolas G, Salek RM, Wishart DS, Scalbert A. Exposome-Explorer 2.0: an update incorporating candidate dietary biomarkers and dietary associations with cancer risk. Nucleic Acids Res. 2020 Jan 8;48(D1):D908-D912. doi: 10.1093/nar/gkz1009. PMID: 31724701; PMCID: PMC7145555.

The Exposome-Explorer database was started in 2012 as a place to compile exposure biomarkers from the scientific literature. This database was published online in 2017. In this article, the authors describe recent updates and improvements to version 2.0 of the database. There were two main updates, increasing the total number of biomarkers in the system from 692 to 908 and focusing on the inclusion of dietary biomarkers. Version 2.0 includes the addition of 185 dietary biomarkers and 1,356 associations between diet and cancer from epidemiological studies. Additionally, in version 2.0 there were database upgrades including interface enhancements, improvements to the hierarchy of classification terms, and new forms and fields to organize information.

March 1, 2021.       7. Exposome databases part 3 

Barupal DK, Fiehn O.Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach. Environ Health Perspect. 2019 Sep;127(9):97008. doi: 10.1289/EHP4713. Epub 2019 Sep 26.PMID: 31557052 

Technological advancements in analytical methods have led to an increase of studies investigating endogenous and exogenous compounds in blood specimens. The authors developed a blood exposome database by utilizing text mining approaches and integrating information across existing databases. A code-ˇbased approach was used to identify relevant publications, and chemicals mentioned were matched with identifiers and related publications from three NCBI databases. Chemicals and corresponding resources were then mapped to 50 additional databases, forming a comprehensive repository of 65,957 unique compounds. This database will be instrumental in expanding exposome research, particularly for developing targeted assays and identifying chemicals in untargeted metabolomics.

March 8, 2021        No class meeting             

March 15, 2021      8. Protein adducts of fingerprints of the exposome

 Dagnino S, Bodinier B, Grigoryan H, Rappaport SM, Karimi M, Guida F, Polidoro S, Edmands WB, Naccarati A, Fiorito G, Sacerdote C, Krogh V, Vermeulen R, Vineis P, Chadeau-Hyam M Agnostic Cys34-albumin adductomics and DNA methylation: Implication of N-acetylcysteine in lung carcinogenesis years before diagnosis. Int J Cancer. 2020 Jun 15;146(12):3294-3303. doi: 10.1002/ijc.32680. Epub 2019 Oct 31.PMID: 31513294

The group discussed the use of adductomics for the exposome. In this intriguing paper the authors identified an adduct to albumin by N-acetylcysteine that predated the diagnosis of lung cancer. The linkage of DNA methylation to the protein adducts was a strength of this paper.   The class discussed redox homeostasis and how N-acetylcysteine contributes to glutathione metabolism. The use of untargeted protein adductomics was recongized as an important tool for the exposome, but indicated that it was premature to suggest that N-acetylcysteine may have therapeutic actions given the numerous failed trials with antioxidants.

March 22, 2021      9. Computational approaches for the exposome

Santos S, Maitre L, Warembourg C, Agier L, Richiardi L, Basagaña X, Vrijheid M. Applying the exposome concept in birth cohort research: a review of statistical approaches .Eur J Epidemiol. 2020 Mar;35(3):193-204. doi: 10.1007/s10654-020-00625-4. Epub 2020 Mar 27.PMID: 32221742 

The students went above and beyond, creating a table that examines the various approaches. 

Category

Approach

Pros

Cons

Single Exposure Approach

ExWAS-multiple regression

-Computationally efficient

-Simultaneously examine many exposures and outcomes

-Can easily integrate multiple imputed datasets while incorporating uncertainty due to imputations

-Same confounders used for all exposures, no control for between exposure confounding

-Increases likelihood of false positives, with corrections for multiple comparisons that tend to be highly conservative

Variable Selection

DSA

-Supports Gaussian, binomial, and multinomial outcome distributions

-Allows incorporation of nonlinear terms for predictors

-Requires pre-selection of candidate exposures

-Not possible to restrict interactions to a specific factor

-Difficulty in combining multiple imputation

ENET

-Focus on sparsity so least informative predictors are screened out

-Accommodates different models (linear, logistic, Poisson, Cox, …)

-High sensitivity and moderate levels of false positives

-Can be used to evaluate multiple environmental exposures

-Difficulty in combining multiple imputation

GUESS

-High sensitivity and specificity

-Accounting for confounders can be tricky

-Prediction based, so correlations between exposures can lead to relevant exposures being excluded if they don’t improve prediction

Dimension Reduction

PCA and Clustering

-Can ID exposure patterns

-Classical pattern recognition technique, simpler to apply than more advanced techniques

-Ideal for exploratory analyses

-Interpretability of results (i.e. trying to summarize patterns in a reasonable number of PCs)

-Assume distinct groups exist with individuals sharing characteristics (discrete vs. continuum)

-May be difficult to combine data from multicenter studies

-Missing data/imputation complications

sPLS

-Focuses

on the variance that is relevant to the outcome of interest

-Avoids issue of multicollinearity and over-fitting

-Sparsity of predictors facilitates interpretability

-High sensitivity and a moderate false discovery rate

-Computationally efficient

-Does not capture as much of the variance as possible

-Difficult interpretability

-Limited ability to adjust for confounders

omics

untargeted analyses, e.g.: high-throughput, PLS:  genome-wide DNA methylation, transcriptomic, proteomics and metabolomics

-Network-based approaches may increase interpretability

-Useful for hypothesis generation and grouping of potential cause/effect pathways

-Do not require a priori hypotheses

-Highly sensitive to matrix selection, experimental conditions and thus measurement error

-Data are highly heterogeneous

-Hard to link to external exposures

 

 March 29, 2021   10. Socioexposome-social factors and health 

Senier L, Brown P, Shostak S, Hanna B. The Socio-Exposome: Advancing Exposure Science and Environmental Justice in a Post-Genomic Era. Environ Sociol. 2017;3(2):107-121. doi: 10.1080/23251042.2016.1220848. Epub 2016 Nov 7. PMID: 28944245; PMCID: PMC5604315.

The authors detail how the socio-exposome can fill in the gaps currently found in genomic and exposomic research. The socio-exposome approach expands the exposome definition by considering social settings that give rise to environmental exposures. Social considerations could include policies that affect the distribution of environmental exposures and hazards. Additionally, there is an explicit focus on community-based participatory research and environmental justice. The socio-exposome framework incorporates multilevel (e.g. individual, local/community, national) analyses to more effectively characterize the complex pathways of exposures and how such pathways affect downstream health outcomes.