Fasting plasma samples drawn from 5 healthy individuals (no chronic medical conditions, no current daily medications or herbal supplements; ages 21-37) and 1 patient with HHT (age 24) (UCLA Institutional Review Board retrospective case report exemption was obtained). The blood from the healthy volunteers was taken as part of a general protocol for internal standardization/normalization at UCSD. A set of samples from individuals in the study were used and are currently part of an ongoing study that allows acquisition and analysis of blood from healthy individuals as well as HHT patients (UCLA Institutional Review Board Protocol Number 11-000843-AM-00003). Blood draws from two different time points had been previously obtained from two of the healthy individuals and for the patient, resulting in a total of 9 samples. Additionally one individual provided a fasting as well as non-fasting blood sample, which served as a non-fasting positive control. Written informed consent was obtained from the patient for publication of this report. A copy of the written consent is available for review by the Editor-in-Chief. All of the experimental research with volunteers and the patient were performed with the approval of the ethics committees of the institutions where the studies were done.
Blood samples were obtained by a licensed health care practitioner and spun down for 10 minutes at approximately 3000 rpm at 20 C. The supernatant was stored in a freezer, and after all samples were obtained, were sent for processing and profiling (Chenomx Inc, Edmonton, Alberta). The samples were filtered using 3 kDa molecular weight cut-off filters (Nanosep 3K Omega microcentrifuge filter tubes). Internal standard solution was added to each sample solution, and the resulting mixture was vortexed for 30s. 600 μL of the mixed solution was transferred to an NMR tube for data acquisition. Spectra were acquired on a 600 MHz Varian INOVA spectrometer with 32 scans per sample at 298 K. Spectra were processed and CNX files were generated using the Processor module in Chenomx NMR Suite 6.0. Metabolites were identified and quantified using the Profiler and Library Manager modules in Chenomx NMR Suite 6.0, using a metabolite reference library of 297 metabolites.
Sample analysis
Unsupervised hierarchical clustering showed clear separation between the healthy individuals and the patient. There were not enough replicates to apply conventional statistical tests (seven measurements in one group and two in another), however two-way hierarchical clustering with unweighted average distance linkage of the metabolite profiles demonstrated a clear separation between the patient and healthy individuals, which also drove the separation in the clustergram (Figure 1). The distance measure in Figure 1 is the infinity norm, but similar separations were observed with other norms (e.g. 1- and 2-norms). Separation of the pre-treatment and post-treatment of the HHT patient as well as re-grouping of the post-treatment HHT patient with the non-HHT individuals was observed with Principal Component Analysis as well (see Additional File 1).
Differences in metabolism between non-HHT individuals and the HHT patient were assessed using the plasma metabolomic profiles. Specific metabolites that were quantitatively and qualitatively different between the non-HHT and HHT patient were determined by identifying those metabolites whose maximum concentration in one group was less than the minimum of the other group or vice versa. This resulted in two sets of metabolites that were different in the two conditions; a high set and a low set. The two general classes of metabolites were labeled the as 'ketone group' and the 'amino acid/carbon rich group' and used to define qualitative metabolic pseudo-reactions.
Network analysis
Recon 1, a global human metabolic network reconstruction [17], with elementally charge and mass balanced equations can be used for analysis of transcriptomic, proteomic, and metabolomic data [19] using constraint-based analysis methods [20, 21].
We predicated our approach on the assumptions that following 8 hour fasting overnight, the body is at or moving towards a homeostatic state, the full content of Recon 1 represents the set of metabolic interactions in the human body, and that changes in plasma profile reflect net changes in uptake and/or secretion of different metabolites with the set of all organs. We briefly describe the constraint-based analysis approach for Flux Balance Analysis, noting there is a rich literature on the subject that the interested reader can pursue in greater detail [9, 20, 21].
In which S is the mxn stoichiometric matrix with m metabolites, n reactions, with each column representing a metabolic (or transport) reaction and v is a vector of fluxes corresponding to each reaction in the network.
Constraints area applied to the network as upper and lower bounds on the fluxes,
In which the α vector specifies reaction flux lower bounds and the β vector specifies reaction flux upper bounds.
The next step requires specification of an objective function which will then be minimized or maximized. We consider linear objective functions only in this study, thus,
are the objectives, in which c is a signed, binary vector. Flux Variability Analysis (FVA) [22] is a method in which every reaction in a network is maximized and minimized under a specified set of constraints, thus providing a 'bounding box' on the current state. This approach can be useful when one is interested in providing a general characterization of the fluxes in one state and how the extrema change from one condition to another. This method has demonstrated interesting results in the analysis of human metabolism in the general as well as context specific conditions [23, 24].
The intravascular space is available for uptake and secretion of metabolites with all organs of the body, thus changes in metabolomic profiles may be interpreted in the context of a global human metabolic network. Direct flux data for particular reactions were not available, so the two qualitatively different profiles, derived from the quantitative differences in the metabolomic profiles of plasma were used to define two sets of different transport constraints. Changes in the simulation conditions provided information about the changes in the metabolite profiles, and were used to define constraints (see Additional File 2). The changes in metabolite profiles reflect differences in metabolic states between the HHT patient and the non-HHT individuals. While the underlying cellular objectives cannot be directly deciphered from these data, the changes in metabolite profiles can be implemented as a constraint to be satisfied, through formulation of a pseudo-reaction, grossly similar to the use of non-growth associated biomass objectives in bacteria [25].
The COBRA toolbox [26] was used to carry out calculations in Matlab (The Mathworks, Natick, MA) with GLPK (GNU software, http://www.gnu.org) for the linear optimization steps, using Recon 1 [17]. The cytochrome oxidase reactions were adjusted to be completely charge and mass balanced, as previously described [5, 27] (see Additional File 3 for the stoichiometric matrix). FVA was performed on Recon 1 with a 'rich media' set of uptake constraints (permission to uptake 20 amino acids, glucose, palmitate, oxygen and exchange of protons and water). The flux variability results were used to define upper and lower bounds on the reactions for an 'open set' of uptake constraints.
The set of metabolites that were elevated in the HHT patient (i.e. whose minimum measured concentrations in the HHT patient were greater than the maximum measured concentrations of all of the non-HHTs) largely consisted of ketones and were dubbed the 'ketone group'. Conversely, the set of metabolites that were decreased in the HHT patient (i.e. whose maximum measured concentrations in the HHT patient were less than the minimum measured concentrations of all of the non-HHTs) primarily were amino acids and were dubbed the 'amino acid group'. The production (i.e. exchange) maxima for the 'ketone group' and 'amino acid group' metabolites were used to specify coefficients for two different pseudo-objective reaction constraints, one representing the HHT patient and the other representing the non-HHT profiles. The 'ketone group' objective was scaled by one-half in order to be able to maintain a feasible solution in the null space (the general set of constraints on the model could not simultaneously satisfy the individual ketone group metabolite maxima). These objectives represent the different non-biomass demands for each condition (using the FVA calculations to avoid setting an infeasible coefficient, see Additional File 2, 'metabolites' tab). The amino acid group included tyrosine, taurine, serine, glycine, alanine, citrate, lactate, methanol, and creatinine. The ketone group included acetone, formate, and acetate. After each model (HHT and non-HHT) was optimized for their respective pseudo-objective and fixed at that value, FVA was again carried out. Subsequently the Flux Span (difference between maximum and minimum flux attainable for all reactions in a particular condition) and the Flux Span Ratio (the reaction-wise ratio of the Flux Span between two conditions) were calculated for comparison.