Can network biology unravel the aetiology of congenital hyperinsulinism?
© Stevens et al; licensee BioMed Central Ltd. 2013
Received: 23 November 2012
Accepted: 3 February 2013
Published: 8 February 2013
Congenital Hyperinsulinism is a condition with a number of genetic causes, but for the majority of patients, the underlying aetiology is unknown. We present here a rational argument for the use of computational biology as a valuable resource for identifying new candidate genes which may cause disease and for understanding the complex mechanisms which define the pathophysiology of this rare disease.
Congenital Hyperinsulinism (CHI) is a rare disease, but is the most common cause of recurrent hypoglycaemia in infancy . The treatment of CHI can be difficult and involves drugs which may not be successful and often are poorly tolerated. As a potentially life-threatening condition, CHI is associated with lifelong sequelae - including critical brain damage (epilepsy, cerebral palsy and neurological impairment) in up to 40% of cases. To date, nine candidate genes associate with CHI, but for the majority of patients – estimated to be approximately 65%, both the aetiology of the CHI and the mechanisms of disease are unknown.
Our current approach to the classification and treatment of CHI is based largely upon observational correlations between the pathological analysis of candidate gene defects and clinical symptoms of hypoglycaemia [1–3]. In this respect, there are similarities between CHI and many other diseases in which numerous mutations in different genes give rise to clinical phenotypes that are essentially indistinguishable from one another. However, under normal physiological conditions, cells function correctly because there is a high degree of interdependency between individual biochemical components (DNA, RNA, proteins and metabolites) and their complex interactions (DNA-protein interactions, protein-protein interactions, metabolic and biochemical pathways, etc.), and tissues function in a co-ordinated manner because there is interplay between different cell types. Diseases rarely result from an abnormality in a single gene, but are in fact the manifestation of disturbances in the multiple networks that integrate cellular processes, and those that link cells with tissues, and tissues with organ systems. As a result, current approaches to molecular diagnosis, however valuable, have shortcomings. These include a lack of sensitivity in identifying preclinical disease, a poor ability to predict prognosis, and ambiguity in defining and resolving a condition where several clinical phenotypes can be observed. All of these inadequacies are evident in CHI, with our current understanding of the causes of disease failing to distinguish transient from persistent disease at the point of presentation and to determine accurately the severity of disease. Also, it is not possible to identify at diagnosis which patients require curative surgery from those who could be successfully managed by short or long-term medical therapy. For these reasons we believe an innovative approach to CHI is required – one which can identify new causes and new mechanisms of dysfunction. One such approach is the use of network biology, first to summate the various interactions and interdependencies between gene networks, and second to identify critical components and pathways which may contribute to the pathophysiology of CHI.
Pathway ontology associated with the CHI disease network
Tropomyosin Receptor Kinase Signalling
5.0 × 10-5
RAF/MAP Kinase Cascade
5.3 × 10-5
5.9 × 10-5
6.3 × 10-5
4.0 × 10-4
3.0 × 10-4
Regulation of SMAD2/3 Signalling
1.0 × 10-4
Oestrogen Receptor-α Signalling
1.1 × 10-4
Oestrogen Receptor-β Signalling
1.5 × 10-4
Retinoic Acid Receptor Signalling
1.4 × 10-4
Growth Factor Signalling
BARD1 Signalling Events
1.3 × 10-4
p53 Signalling pathway
2.0 × 10-4
HDAC Class III Signalling
1.0 × 10-3
2.6 × 10-4
6.6 × 10-3
9.0 × 10-3
6.6 × 10-3
Integration of Energy Metabolism
1.0 × 10-3
This is the first analysis of its kind for CHI or any other monogenic disorder of glucose-regulation in infancy or childhood (e.g. neonatal diabetes mellitus, MODY, etc.). From here, we are now in a position to explore the possibility that genes integral to the network or tightly connected to it, may be new candidates for the aetiology of CHI and other monogenic causes of glucose-regulation disorders. Furthermore, by analysis of the components of the associated pathway ontology – listed in Table 1, we have created a portal to identify novel mechanisms of disease which may provide insights for the management and treatment of CHI. We strongly believe that these in silico techniques involving access to readily available databases are highly applicable to many rare diseases, and that when used alone or in combination with other datasets – e.g. metabolomic, they will form the future basis of identifying of new candidate gene defects, and understanding the pathophysiology of rare diseases.
AS initiated and undertook the bioinformatic analysis; AS, KEC, RP, MSS, IB, and PEC participated in the interpretation of data and in drafting the manuscript. MJD conceived the study, and participated in its design and coordination and helped to draft the manuscript. All authors have read and approved the final manuscript.
- De León DD, Stanley CA: Mechanisms of disease: advances in diagnosis and treatment of hyperinsulinism in neonates. Nat Clin Pract Endocrinol Metab. 2007, 3 (1): 57-68. 10.1038/ncpendmet0368.PubMedView ArticleGoogle Scholar
- Senniappan S, Shanti B, James C, Hussain K: Hyperinsulinaemic hypoglycaemia: genetic mechanisms, diagnosis and management. J Inherit Metab Dis. 2012, 35 (4): 589-601. 10.1007/s10545-011-9441-2.PubMedView ArticleGoogle Scholar
- Arnoux JB, Verkarre V, Saint-Martin C, Montravers F, Brassier A, Valayannopoulos V, Brunelle F, Fournet JC, Robert JJ, Aigrain Y, Bellanné-Chantelot C, de Lonlay P: Congenital hyperinsulinism: current trends in diagnosis and therapy. Orphanet J Rare Dis. 2011, 6: 63-70. 10.1186/1750-1172-6-63.PubMed CentralPubMedView ArticleGoogle Scholar
- Barabási AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011, 12 (1): 56-68. 10.1038/nrg2918.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu YY, Slotine JJ, Barabási AL: Controllability of complex networks. Nature. 2011, 473 (7346): 167-73. 10.1038/nature10011.PubMedView ArticleGoogle Scholar
- Vidal M, Cusick ME, Barabási AL: Interactome networks and human disease. Cell. 2011, 144 (6): 986-98. 10.1016/j.cell.2011.02.016.PubMed CentralPubMedView ArticleGoogle Scholar
- Loscalzo J, Kohane I, Barabasi AL: Human disease classification in the post-genomic era: a complex systems approach to human pathobiology. Mol Syst Biol. 2007, 3: 124.PubMed CentralPubMedView ArticleGoogle Scholar
- Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabási AL: The human disease network. Proc Natl Acad Sci (USA). 2006, 104 (21): 8685-90.View ArticleGoogle Scholar
- Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A, Stark C, Nixon J, Ramage L, Kolas N, O'Donnell L, Reguly T, Breitkreutz A, Sellam A, Chen D, Chang C, Rust J, Livstone M, Oughtred R, Dolinski K, Tyers M: The BioGRID interaction database: 2013 update. Nucleic Acids Res. 2013, 41 (D1): D816-23. 10.1093/nar/gks1158.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhao Y, Mooney SD: Functional organization and its implication in evolution of the human protein-protein interaction network. BMC Genomics. 2012, 13 (150): p1-10.Google Scholar
- Newman ME: Modularity and community structure in networks. Proc Natl Acad Sci (USA). 2006, 103 (23): 8577-82. 10.1073/pnas.0601602103.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.