A computational approach to chemical etiologies of diabetes
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A computational approach to chemical etiologies of diabetes. / Audouze, Karine Marie Laure; Brunak, Søren; Grandjean, Philippe.
In: Scientific Reports, Vol. 3, 2712, 19.09.2013, p. 2712.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A computational approach to chemical etiologies of diabetes
AU - Audouze, Karine Marie Laure
AU - Brunak, Søren
AU - Grandjean, Philippe
PY - 2013/9/19
Y1 - 2013/9/19
N2 - Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases.
AB - Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases.
U2 - 10.1038/srep02712
DO - 10.1038/srep02712
M3 - Journal article
C2 - 24048418
VL - 3
SP - 2712
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 2712
ER -
ID: 58017398