Open Access Research article

Individualized therapy of HHT driven by network analysis of metabolomic profiles

Neema Jamshidi1, Franklin J Miller2, Jess Mandel3, Timothy Evans4 and Michael D Kuo1*

Author Affiliations

1 Department of Radiological Sciences, University of California, Los Angeles, USA

2 Department of Radiology, University of California, San Diego, USA

3 Department of Internal Medicine, University of California, San Diego, USA

4 Department of Medicine, University of California, San Francisco-Fresno, USA

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BMC Systems Biology 2011, 5:200  doi:10.1186/1752-0509-5-200

Published: 20 December 2011

Abstract

Background

Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant disease with a varying range of phenotypes involving abnormal vasculature primarily manifested as arteriovenous malformations in various organs, including the nose, brain, liver, and lungs. The varied presentation and involvement of different organ systems makes the choice of potential treatment medications difficult.

Results

A patient with a mixed-clinical presentation and presumed diagnosis of HHT, severe exertional dyspnea, and diffuse pulmonary shunting at the microscopic level presented for treatment. We sought to analyze her metabolomic plasma profile to assist with pharmacologic treatment selection. Fasting serum samples from 5 individuals (4 healthy and 1 with HHT) were metabolomically profiled.

A global metabolic network reconstruction, Recon 1, was used to help guide the choice of medication via analysis of the differential metabolism between the patient and healthy controls using metabolomic data. Flux Balance Analysis highlighted changes in metabolic pathway activity, notably in nitric oxide synthase (NOS), which suggested a potential link between changes in vascular endothelial function and metabolism. This finding supported the use of an already approved medication, bevacizumab (Avastin). Following 2 months of treatment, the patient's metabolic profile shifted, becoming more similar to the control subject profiles, suggesting that the treatment was addressing at least part of the pathophysiological state.

Conclusions

In this 'individualized case study' of personalized medicine, we carry out untargeted metabolomic profiling of a patient and healthy controls. Rather than filtering the data down to a single value, these data are analyzed in the context of a network model of metabolism, in order to simulate the biochemical phenotypic differences between healthy and disease states; the results then guide the therapy. This presents one approach to achieving the goals of individualized medicine through Systems Biology and causal models analysis.