The increasing amount of biological data makes possible their interpretation more accurate and richer than never before. Various way of representations and interpretations of the links between those data have been applied or developed consequently to these new elements which can be taken into account in diagnostics and soon in personalized medicine. The aim of this student project was to cross data coming from various databases to be able to link Perfluorooctaoic Acid (PFOA) to one or more human phenotypes and metabolic diseases. Our approach makes possible an easy and confident interpretation on the data kept and also allow us to rank diseases linked according to their risk of correlation to a specific set of proteins.

1. 1
Identification of PFOA linked Metabolic Diseases by Crossing Databases.
Quentin Letourneur, Yoann Pageaud.
Abstract:
The increasing amount of biological data makes possible their interpretation more accurate and richer than
never before. Various way of representations and interpretations of the links between those data have been
applied or developed consequently to these new elements which can be taken into account in diagnostics and
soon in personalized medicine. The aim of this student project was to cross data coming from various databases
to be able to link Perfluorooctaoic Acid (PFOA) to one or more human phenotypes and metabolic diseases.
Our approach makes possible an easy and confident interpretation on the data kept and also allow us to rank
diseases linked according to their risk of correlation to a specific set of proteins.
Introduction:
We base the representation of our results on the use of the software Cytoscape1
which gave reliable graph
representations easy to manipulate. We used various online tools to get information about interactions between
our data. We get information about the molecule-protein interactions on STITCH2
website. Others data were
collected from various database by using the online tool ToppFun on the TOPPGENE3
website. The amount
of data is so important that filtering is necessary. One tool we used for this purpose on the Gene Ontology4
Data (GO Data) is REVIGO5
. Following this protocol, metabolic diseases have been identified and linked at
different level of confidence to a set of proteins shown to interact with PFOA.
Materials and Methods:
For this purpose, Molecule-Protein interaction data were retrieved from requests on STITCH 4.0 website
(http://stitch.embl.de/) for PFOA. 10 Proteins were identified (IL10, CASP1, SLC22A12, HNF4A, GSTP1,
PPARA, PPARD, SLC22A11, PPARG and HSD17B3). Then, we used those proteins Uniprot IDs (Entry
Type = Uniprot) to retrieved different type of data by using the tool ToppFun available on the TOPPGENE
website (https://toppgene.cchmc.org/). Pathway Data were retrieved from the databases BIOCYC, KEGG,
PID, REACTOME, WikiPathways, GenMAPP, MSigDB C2 BIOCARTA v5.1, PantherDB, Pathway
Ontology and SMPDB. Disease Data were retrieved from the database UMLS. GO Data were retrieved from
the GO Database. Human Phenotype Data were retrieved from the Human Phenotype Ontology Database.
Gene Family Data were retrieved from the HGNC Database. From this request, we collected around 13,000
results. Firstly, results were filtered : we only kept the 50 first results from Pathway, Disease, Human
Phenotype, and Gene Family data returned, and beside we summarized GO Data into a reduce amount of
GOslim using the online tool REVIGO (http://revigo.irb.hr/). Then, Disease and Human Phenotype Data were
manually filtered considering only Metabolic Diseases and Metabolic Imbalance Human Phenotypes.
Pathways were grouped into 5 simplified terms (Nuclear Receptors, Transport, Fat Metabolism,
1
Paul Shannon et al., “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks,”
Genome Research 13, no. 11 (2003): 2498–2504.
2
Michael Kuhn et al., “STITCH 4: Integration of Protein–chemical Interactions with User Data,” Nucleic Acids Research 42, no. D1
(January 2014): D401–7, doi:10.1093/nar/gkt1207.
3
J. Chen et al., “ToppGene Suite for Gene List Enrichment Analysis and Candidate Gene Prioritization,” Nucleic Acids Research
37, no. Web Server (July 1, 2009): W305–11, doi:10.1093/nar/gkp427.
4
Michael Ashburner et al., “Gene Ontology: Tool for the Unification of Biology,” Nature Genetics 25, no. 1 (May 2000): 25–29,
doi:10.1038/75556.
5
Fran Supek et al., “REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms,” ed. Cynthia Gibas, PLoS ONE 6, no. 7
(July 18, 2011): e21800, doi:10.1371/journal.pone.0021800.

2. 2
Inflammasome Complex, Androgen Metabolism). Only 2 Gene Families were returned for matching 2
Proteins of our set (“Caspases” and “Interleukins and interleukin receptors”). All groupings and
simplifications were taken in account and are proportional to Graph Node Diameters (GND). However, GND
can only be compared if nodes represent same type of data. raphe. Graph Edge Lengths are not proportional
to any parameter; they depend on the «Organic » Layout default organization from Cytoscape.
Results:
PFOA can bind 10 proteins (Figure 1.). Depending on interactions between PFOA and proteins of interest,
effects on the metabolic processes can be more or less different from one individual to another.
The binding of PFOA to SLC22A11 or SLC22A12 seems to impact specifically urate metabolic processes
and seems to be linked to primary gout. The binding of PFOA to SLC22A11, SLC22A12 or CASP1 seems
associated to the inflammasome complex, which suggests that PFOA can affect the immune system and
inflammatory processes.
The most represented disease on the graph is the Diabetes milletus. Consequently, an individual has more
risks to develop Diabetes, when exposed to PFOA.
Other linked diseases are also strongly associated to the exposition to PFOA: Eczema, Kidney Failures, Acute
Coronary Syndromes.
PPARA, PPARD and PPARG seems to play a main role in the majority of the identified diseases. However,
they also seem to be specifically associated with data linked to fat metabolic processes.
PFOA seems also able to interact with IL10 and CASP1 which are respectively part of 2 gene families,
interleukins and caspases, themselves respectively linked to immune system and apoptotic processes.
Figure 1. Cytoscape Interaction Graph between Perfluorooctanoic Acid (PFOA), proteins able to bind PFOA,
and data retrieved from crossed requests on various databases using the website TOPPGENE with the online
tool ToppFun. Databases date access: October the 14th
2016.

3. 3
References:
1. Ashburner, Michael, Catherine A. Ball, Judith A. Blake, David Botstein, Heather Butler, J. Michael
Cherry, Allan P. Davis, et al. “Gene Ontology: Tool for the Unification of Biology.” Nature Genetics
25, no. 1 (May 2000): 25–29. doi:10.1038/75556.
2. Chen, J., E. E. Bardes, B. J. Aronow, and A. G. Jegga. “ToppGene Suite for Gene List Enrichment
Analysis and Candidate Gene Prioritization.” Nucleic Acids Research 37, no. Web Server (July 1,
2009): W305–11. doi:10.1093/nar/gkp427.
3. Kuhn, Michael, Damian Szklarczyk, Sune Pletscher-Frankild, Thomas H. Blicher, Christian von Mering,
Lars J. Jensen, and Peer Bork. “STITCH 4: Integration of Protein–chemical Interactions with User
Data.” Nucleic Acids Research 42, no. D1 (January 2014): D401–7. doi:10.1093/nar/gkt1207.
4. Shannon, Paul, Andrew Markiel, Owen Ozier, Nitin S. Baliga, Jonathan T. Wang, Daniel Ramage, Nada
Amin, Benno Schwikowski, and Trey Ideker. “Cytoscape: A Software Environment for Integrated
Models of Biomolecular Interaction Networks.” Genome Research 13, no. 11 (2003): 2498–2504.
5. Supek, Fran, Matko Bošnjak, Nives Škunca, and Tomislav Šmuc. “REVIGO Summarizes and
Visualizes Long Lists of Gene Ontology Terms.” Edited by Cynthia Gibas. PLoS ONE 6, no. 7 (July
18, 2011): e21800. doi:10.1371/journal.pone.0021800.