PMID- 31960936
OWN - NLM
STAT- MEDLINE
DCOM- 20210112
LR  - 20210112
IS  - 1868-8551 (Electronic)
IS  - 1868-596X (Linking)
VI  - 37
IP  - 2
DP  - 2020
TI  - Computational systems biology as an animal-free approach to characterize 
      toxicological effects of persistent organic pollutants.
PG  - 287-299
LID - 10.14573/altex.1910161 [doi]
AB  - Exposure to persistent organic pollutants (POPs), as defined by the Stockholm 
      Convention, may alter biological systems and cause toxic effects. Computational 
      studies appear to be a relevant approach to increase our understanding of the 
      molecular mechanisms triggered by POPs. We investigated the use of a systems 
      toxicology approach to explore the effects of POPs on human health. A 
      protein-protein association network (PPAN) was developed based on known 
      POP-protein interactions. This model was used to predict protein complexes for 
      several candidate POPs, including dicofol, methoxychlor, and perfluorooctanoic 
      acid (PFOA), that are listed or proposed to be listed as POPs by the Stockholm 
      Convention. Integration of multiple data sources (pathways, disease annotations, 
      adverse outcome pathways) involving the identified protein complexes was 
      performed independently in order to reveal putative risk factors for human 
      health. This approach revealed that several systems may be disturbed by these 
      candidate POPs, mainly the reproductive, metabolic and nervous systems. This 
      study highlights that a computational systems toxicology approach may help to 
      decipher putative biological mecha-nisms of poorly studied chemicals and link 
      them to possible adverse effects with the aim to support regulatory assessment 
      and trigger new epidemiological and experimental studies. In order to develop 
      more accurate computational models as alternative methods to animal testing, the 
      next challenge will be to integrate more data according to the findable, 
      acces-sible, interoperable and reusable (FAIR) data principles.
FAU - Wu, Qier
AU  - Wu Q
AD  - Universite de Paris, Inserm UMR S-1124, Paris, France.
FAU - Achebouche, Rayanne
AU  - Achebouche R
AD  - Universite de Paris, Inserm UMR S-1124, Paris, France.
FAU - Audouze, Karine
AU  - Audouze K
AD  - Universite de Paris, Inserm UMR S-1124, Paris, France.
LA  - eng
PT  - Journal Article
DEP - 20200121
PL  - Germany
TA  - ALTEX
JT  - ALTEX
JID - 100953980
SB  - IM
MH  - Animal Testing Alternatives
MH  - Animals
MH  - *Computational Biology
MH  - Humans
MH  - Persistent Organic Pollutants/*toxicity
MH  - Risk Factors
MH  - *Systems Biology
MH  - Toxicity Tests/*methods
OTO - NOTNLM
OT  - adverse outcome pathways
OT  - computational biology
OT  - endocrine disruptors
OT  - systems toxicology
EDAT- 2020/01/22 06:00
MHDA- 2021/01/13 06:00
CRDT- 2020/01/22 06:00
PHST- 2019/10/16 00:00 [received]
PHST- 2020/01/14 00:00 [accepted]
PHST- 2020/01/22 06:00 [entrez]
PHST- 2020/01/22 06:00 [pubmed]
PHST- 2021/01/13 06:00 [medline]
AID - 10.14573/altex.1910161 [doi]
PST - ppublish
SO  - ALTEX. 2020;37(2):287-299. doi: 10.14573/altex.1910161. Epub 2020 Jan 21.