Limits...
Mining protein interactomes to improve their reliability and support the advancement of network medicine.

Alanis-Lobato G - Front Genet (2015)

Bottom Line: The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives.Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology.In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Biology, Institute of Molecular Biology, Johannes Gutenberg University of Mainz Mainz, Germany ; Integrative Systems Biology Lab, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology Thuwal, Saudi Arabia.

ABSTRACT
High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.

No MeSH data available.


Related in: MedlinePlus

(A) A bipartite network of diseases and their associated genes or symptoms can be mapped to the disease or gene/symptom space by linking nodes of one type that are connected with the same nodes of the other. The weight of the edges in the resulting projection indicates the number of such common nodes. (B) The application of a community detection algorithm to the Autoimmune Disease Network, mapped to the gene space, reveals groups of genes associated with similar disorders and high levels of co-morbidity (adapted from Alanis-Lobato et al., 2014). (C) An example human protein interactome in which gene products associated with diseases A, B, and C have been labeled with different colors. According to Menche et al. (2015), the topologically closer two diseases are (like B and C), the higher the GO similarity and co-expression of their associated proteins and the higher their co-morbidity and symptom similarity.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4585290&req=5

Figure 2: (A) A bipartite network of diseases and their associated genes or symptoms can be mapped to the disease or gene/symptom space by linking nodes of one type that are connected with the same nodes of the other. The weight of the edges in the resulting projection indicates the number of such common nodes. (B) The application of a community detection algorithm to the Autoimmune Disease Network, mapped to the gene space, reveals groups of genes associated with similar disorders and high levels of co-morbidity (adapted from Alanis-Lobato et al., 2014). (C) An example human protein interactome in which gene products associated with diseases A, B, and C have been labeled with different colors. According to Menche et al. (2015), the topologically closer two diseases are (like B and C), the higher the GO similarity and co-expression of their associated proteins and the higher their co-morbidity and symptom similarity.

Mentions: It is possible that the first work that advocated for a systems-based approach to disease is the one by Goh et al. (2007). They take advantage of the Online Mendelian Inheritance in Man (OMIM) repository to build a bipartite network of disorders linked to their associated genes (see Figure 2A middle). Starting from this network, projections are carried out, one to the disease space (Figure 2A left) and the other to the gene space (Figure 2A right). In the disease projection, they observe a giant network component, suggesting shared genetic origins of its constituent diseases. The gene projection provides phenotypic relationship between gene pairs and presents a high overlap with a network of high-quality PIs (Goh et al., 2007). Moreover, essential human genes tend to encode hub proteins and are found to be expressed in most tissues. Whereas, disease genes are less connected and possess tissue specificity (Goh et al., 2007).


Mining protein interactomes to improve their reliability and support the advancement of network medicine.

Alanis-Lobato G - Front Genet (2015)

(A) A bipartite network of diseases and their associated genes or symptoms can be mapped to the disease or gene/symptom space by linking nodes of one type that are connected with the same nodes of the other. The weight of the edges in the resulting projection indicates the number of such common nodes. (B) The application of a community detection algorithm to the Autoimmune Disease Network, mapped to the gene space, reveals groups of genes associated with similar disorders and high levels of co-morbidity (adapted from Alanis-Lobato et al., 2014). (C) An example human protein interactome in which gene products associated with diseases A, B, and C have been labeled with different colors. According to Menche et al. (2015), the topologically closer two diseases are (like B and C), the higher the GO similarity and co-expression of their associated proteins and the higher their co-morbidity and symptom similarity.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585290&req=5

Figure 2: (A) A bipartite network of diseases and their associated genes or symptoms can be mapped to the disease or gene/symptom space by linking nodes of one type that are connected with the same nodes of the other. The weight of the edges in the resulting projection indicates the number of such common nodes. (B) The application of a community detection algorithm to the Autoimmune Disease Network, mapped to the gene space, reveals groups of genes associated with similar disorders and high levels of co-morbidity (adapted from Alanis-Lobato et al., 2014). (C) An example human protein interactome in which gene products associated with diseases A, B, and C have been labeled with different colors. According to Menche et al. (2015), the topologically closer two diseases are (like B and C), the higher the GO similarity and co-expression of their associated proteins and the higher their co-morbidity and symptom similarity.
Mentions: It is possible that the first work that advocated for a systems-based approach to disease is the one by Goh et al. (2007). They take advantage of the Online Mendelian Inheritance in Man (OMIM) repository to build a bipartite network of disorders linked to their associated genes (see Figure 2A middle). Starting from this network, projections are carried out, one to the disease space (Figure 2A left) and the other to the gene space (Figure 2A right). In the disease projection, they observe a giant network component, suggesting shared genetic origins of its constituent diseases. The gene projection provides phenotypic relationship between gene pairs and presents a high overlap with a network of high-quality PIs (Goh et al., 2007). Moreover, essential human genes tend to encode hub proteins and are found to be expressed in most tissues. Whereas, disease genes are less connected and possess tissue specificity (Goh et al., 2007).

Bottom Line: The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives.Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology.In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Biology, Institute of Molecular Biology, Johannes Gutenberg University of Mainz Mainz, Germany ; Integrative Systems Biology Lab, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology Thuwal, Saudi Arabia.

ABSTRACT
High-throughput detection of protein interactions has had a major impact in our understanding of the intricate molecular machinery underlying the living cell, and has permitted the construction of very large protein interactomes. The protein networks that are currently available are incomplete and a significant percentage of their interactions are false positives. Fortunately, the structural properties observed in good quality social or technological networks are also present in biological systems. This has encouraged the development of tools, to improve the reliability of protein networks and predict new interactions based merely on the topological characteristics of their components. Since diseases are rarely caused by the malfunction of a single protein, having a more complete and reliable interactome is crucial in order to identify groups of inter-related proteins involved in disease etiology. These system components can then be targeted with minimal collateral damage. In this article, an important number of network mining tools is reviewed, together with resources from which reliable protein interactomes can be constructed. In addition to the review, a few representative examples of how molecular and clinical data can be integrated to deepen our understanding of pathogenesis are discussed.

No MeSH data available.


Related in: MedlinePlus