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Bayesian Network Inference Enables Unbiased Phenotypic Anchoring of Transcriptomic Responses to Cigarette Smoke in Humans
journal contribution
posted on 2015-10-19, 00:00 authored by Danyel G. J. Jennen, Danitsja M. van
Leeuwen, Diana M. Hendrickx, Ralph W. H. Gottschalk, Joost
H. M. van Delft, Jos C.
S. KleinjansMicroarray-based transcriptomic analysis
has been demonstrated
to hold the opportunity to study the effects of human exposure to,
e.g., chemical carcinogens at the whole genome level, thus yielding
broad-ranging molecular information on possible carcinogenic effects.
Since genes do not operate individually but rather through concerted
interactions, analyzing and visualizing networks of genes should provide
important mechanistic information, especially upon connecting them
to functional parameters, such as those derived from measurements
of biomarkers for exposure and carcinogenic risk. Conventional methods
such as hierarchical clustering and correlation analyses are frequently
used to address these complex interactions but are limited as they
do not provide directional causal dependence relationships. Therefore,
our aim was to apply Bayesian network inference with the purpose of
phenotypic anchoring of modified gene expressions. We investigated
a use case on transcriptomic responses to cigarette smoking in humans,
in association with plasma cotinine levels as biomarkers of exposure
and aromatic DNA-adducts in blood cells as biomarkers of carcinogenic
risk. Many of the genes that appear in the Bayesian networks surrounding
plasma cotinine, and to a lesser extent around aromatic DNA-adducts,
hold biologically relevant functions in inducing severe adverse effects
of smoking. In conclusion, this study shows that Bayesian network
inference enables unbiased phenotypic anchoring of transcriptomics
responses. Furthermore, in all inferred Bayesian networks several
dependencies are found which point to known but also to new relationships
between the expression of specific genes, cigarette smoke exposure,
DNA damaging-effects, and smoking-related diseases, in particular
associated with apoptosis, DNA repair, and tumor suppression, as well
as with autoimmunity.