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Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective.

Zanzoni A, Chapple CE, Brun C - Front Physiol (2015)

Bottom Line: We found that disease-related genes are over-represented among those candidates.Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records.Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

View Article: PubMed Central - PubMed

Affiliation: INSERM, UMR_S1090 TAGC Marseille, France ; Aix-Marseille Université, UMR_S1090, TAGC Marseille, France.

ABSTRACT
Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

No MeSH data available.


Disease pairs phenotypic similarity based on Human Phenotype Ontology annotations. Distributions of phenotypic similarity, measured using the Sorensen-Dice coefficient, for MF-CAN, MCNC, and MONO proteins. Mean values are depicted by red dots.
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Figure 4: Disease pairs phenotypic similarity based on Human Phenotype Ontology annotations. Distributions of phenotypic similarity, measured using the Sorensen-Dice coefficient, for MF-CAN, MCNC, and MONO proteins. Mean values are depicted by red dots.

Mentions: To do so, we mapped OMIM diseases to the Human Phenotype Ontology (HPO) (Köhler et al., 2014). For each disease, HPO provides a list of annotation terms describing the phenotypes associated to the disease. We clustered them in 19 broad classes of organ abnormalities. We found that proteins belonging to MF-CAN, MCNC, and MONO are over-represented in almost all of the HPO broad classes (Figure 3). Particularly, MF-CAN proteins are over-represented in all HPO classes except “abnormality of prenatal development or birth” (Figure 3A), MCNC proteins are under-represented in the “abnormality of metabolism/homeostasis” class (Figure 3B) whereas MONO proteins are under-represented in three HPO classes: “abnormality of the breast,” “abnormality of the blood and blood-forming tissue,” and “abnormality of head and neck” (Figure 3C). More interestingly, we observed a significantly higher fraction of shared MF-CAN proteins, 32% on average, between HPO classes compared to MCNC (21%, P = 2.3 × 10−13, Mann-Whitney U test, one-sided) and MONO (19%, P < 2.2 × 10−16). This result shows that MF-CAN proteins are involved in disease affecting distinct body parts. Therefore, this led us to evaluate the phenotypic similarity between disease pairs by computing the Sorensen-Dice distance based on their HPO annotation terms (see Methods for details). For each pair of diseases in which a same gene is involved, we have compared their phenotypic descriptions to assess their similarity aiming at grasping possible differences. We indeed found that those pairs associated with MF-CAN are significantly different (Figure 4, Supplementary Figure 1) compared to the other categories (P = 4.7 × 10−5 and P = 1.3 × 10−11 for MCNC and MONO, respectively, Mann-Whitney U test, one-sided). We obtained similar results using semantic similarity measures (Supplementary Figure 2) based on DO annotations (Schriml et al., 2012). Indeed, MONO disease pairs are significantly more similar than MF-CAN and MCNC pairs (P = 2.7 × 10−3 and 1.5 × 10−5, respectively, Mann-Whitney U test, one-sided). These results could be due to the fact that multifunctional candidates (MF-CAN and MCNC) are acting in several biological processes, and are therefore involved in different diseases and phenotypes.


Relationships between predicted moonlighting proteins, human diseases, and comorbidities from a network perspective.

Zanzoni A, Chapple CE, Brun C - Front Physiol (2015)

Disease pairs phenotypic similarity based on Human Phenotype Ontology annotations. Distributions of phenotypic similarity, measured using the Sorensen-Dice coefficient, for MF-CAN, MCNC, and MONO proteins. Mean values are depicted by red dots.
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Related In: Results  -  Collection

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Figure 4: Disease pairs phenotypic similarity based on Human Phenotype Ontology annotations. Distributions of phenotypic similarity, measured using the Sorensen-Dice coefficient, for MF-CAN, MCNC, and MONO proteins. Mean values are depicted by red dots.
Mentions: To do so, we mapped OMIM diseases to the Human Phenotype Ontology (HPO) (Köhler et al., 2014). For each disease, HPO provides a list of annotation terms describing the phenotypes associated to the disease. We clustered them in 19 broad classes of organ abnormalities. We found that proteins belonging to MF-CAN, MCNC, and MONO are over-represented in almost all of the HPO broad classes (Figure 3). Particularly, MF-CAN proteins are over-represented in all HPO classes except “abnormality of prenatal development or birth” (Figure 3A), MCNC proteins are under-represented in the “abnormality of metabolism/homeostasis” class (Figure 3B) whereas MONO proteins are under-represented in three HPO classes: “abnormality of the breast,” “abnormality of the blood and blood-forming tissue,” and “abnormality of head and neck” (Figure 3C). More interestingly, we observed a significantly higher fraction of shared MF-CAN proteins, 32% on average, between HPO classes compared to MCNC (21%, P = 2.3 × 10−13, Mann-Whitney U test, one-sided) and MONO (19%, P < 2.2 × 10−16). This result shows that MF-CAN proteins are involved in disease affecting distinct body parts. Therefore, this led us to evaluate the phenotypic similarity between disease pairs by computing the Sorensen-Dice distance based on their HPO annotation terms (see Methods for details). For each pair of diseases in which a same gene is involved, we have compared their phenotypic descriptions to assess their similarity aiming at grasping possible differences. We indeed found that those pairs associated with MF-CAN are significantly different (Figure 4, Supplementary Figure 1) compared to the other categories (P = 4.7 × 10−5 and P = 1.3 × 10−11 for MCNC and MONO, respectively, Mann-Whitney U test, one-sided). We obtained similar results using semantic similarity measures (Supplementary Figure 2) based on DO annotations (Schriml et al., 2012). Indeed, MONO disease pairs are significantly more similar than MF-CAN and MCNC pairs (P = 2.7 × 10−3 and 1.5 × 10−5, respectively, Mann-Whitney U test, one-sided). These results could be due to the fact that multifunctional candidates (MF-CAN and MCNC) are acting in several biological processes, and are therefore involved in different diseases and phenotypes.

Bottom Line: We found that disease-related genes are over-represented among those candidates.Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records.Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

View Article: PubMed Central - PubMed

Affiliation: INSERM, UMR_S1090 TAGC Marseille, France ; Aix-Marseille Université, UMR_S1090, TAGC Marseille, France.

ABSTRACT
Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that (i) they are significantly involved in more than one disease, (ii) they contribute to complex rather than monogenic diseases, (iii) the diseases in which they are involved are phenotypically different according to their annotations, finally, (iv) they are enriched for diseases pairs showing statistically significant comorbidity patterns based on Medicare records. Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.

No MeSH data available.