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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.


Number of diseases associated with each protein category. MF-CAN proteins involved in at least two diseases are associated with more diseases compared to MCNC (P = 2.6 × 10−3, Mann-Whitney U test, one-sided) and MONO proteins (P = 1.3 × 10−4, Mann-Whitney U test, one-sided). Mean values are depicted by red dots.
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Figure 1: Number of diseases associated with each protein category. MF-CAN proteins involved in at least two diseases are associated with more diseases compared to MCNC (P = 2.6 × 10−3, Mann-Whitney U test, one-sided) and MONO proteins (P = 1.3 × 10−4, Mann-Whitney U test, one-sided). Mean values are depicted by red dots.

Mentions: Using OMIM disease annotations, we found that the multifunctional proteins of the human interactome (i.e., proteins belonging to more than one network cluster, corresponding to MF-CAN and MCNC taken together) are enriched in proteins involved in diseases (1.33-fold, P < 2.2 × 10−16, Fisher's exact test, two-sided). We still observed this enrichment when considering MF-CAN and MCNC separately (Table 1). In addition, MF-CAN and MCNC are both significantly enriched in proteins involved in more than two diseases, with MF-CAN showing a higher proportion of such proteins compared to MCNC (2.04-fold and 1.25-fold, respectively). On the other hand, the MONO category is significantly depleted in disease proteins (Table 1). Given these results, we decided to focus our attention on those proteins of the three categories that are involved in more than two diseases. Interestingly, MF-CAN proteins are associated with a higher number of diseases compared to the other categories (3.6 compared to 3 and 2.9, on average, for MCNC and MONO respectively, P = 2.6 × 10−3 and P = 1.3 × 10−4, Mann-Whitney U test, one-sided) (Figure 1). This suggests that MF-CAN proteins are particularly associated to multiple diseases.


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

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

Number of diseases associated with each protein category. MF-CAN proteins involved in at least two diseases are associated with more diseases compared to MCNC (P = 2.6 × 10−3, Mann-Whitney U test, one-sided) and MONO proteins (P = 1.3 × 10−4, Mann-Whitney U test, one-sided). Mean values are depicted by red dots.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4477069&req=5

Figure 1: Number of diseases associated with each protein category. MF-CAN proteins involved in at least two diseases are associated with more diseases compared to MCNC (P = 2.6 × 10−3, Mann-Whitney U test, one-sided) and MONO proteins (P = 1.3 × 10−4, Mann-Whitney U test, one-sided). Mean values are depicted by red dots.
Mentions: Using OMIM disease annotations, we found that the multifunctional proteins of the human interactome (i.e., proteins belonging to more than one network cluster, corresponding to MF-CAN and MCNC taken together) are enriched in proteins involved in diseases (1.33-fold, P < 2.2 × 10−16, Fisher's exact test, two-sided). We still observed this enrichment when considering MF-CAN and MCNC separately (Table 1). In addition, MF-CAN and MCNC are both significantly enriched in proteins involved in more than two diseases, with MF-CAN showing a higher proportion of such proteins compared to MCNC (2.04-fold and 1.25-fold, respectively). On the other hand, the MONO category is significantly depleted in disease proteins (Table 1). Given these results, we decided to focus our attention on those proteins of the three categories that are involved in more than two diseases. Interestingly, MF-CAN proteins are associated with a higher number of diseases compared to the other categories (3.6 compared to 3 and 2.9, on average, for MCNC and MONO respectively, P = 2.6 × 10−3 and P = 1.3 × 10−4, Mann-Whitney U test, one-sided) (Figure 1). This suggests that MF-CAN proteins are particularly associated to multiple diseases.

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.