Development of multi-compartmental model for the diagnosis of fatty acid β-oxidation disorders using U13C-Palmitic Acid in Vitro and UPLC-ESI -MS/MS. Antonina Gucciardi1, Dietrich Matern2, Elisa Sartori3, Gianna Maria Toffolo3, Iole Maria Di Gangi1, Irene Costa1, Paola Pirillo1, Piero Rinaldo2, Giuseppe Giordano1.

1 Women’s and Children’s Health Department, University of Padova, Padova, Italy 2 Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

3 Department of Information Engineering, University of Padova, Padova, Italy

UNIVERSITÀ

DEGLI STUDI

DI PADOVA

Introduction The mitochondrial fatty acids β-oxidation (FAO) disorders are a group of autosomal recessive pathologies with an incidence of 1:8200 newborns. They are associated to enzyme deficiencies (mild or severe) which emerge when the metabolic pathway is stimulated under catabolic stress. The same enzyme deficiency presents with heterogeneity and a variable severity of clinical symptoms, depending on the type of mutation in mature protein, on the transcriptional rate of its gene and also on post-translational modifications. All these influence enzyme activity and consequently the concentration of the acylcarnitines, the β-oxidation intermediates [1]. Their identification is based on the presence of high levels of diagnostically significant acylcarnitines in biological fluids. As private mutations are frequent in other FAOD, mutation analysis of genes of the FAO pathway has not been used widely as the first-line laboratory investigation for these diseases. Even mutational analysis may be inconclusive because there is still no well-defined genotype-phenotype correlation [2]. Complementary to these blood tests, in-vitro studies of FAO flux and acylcarnitine profiling in cultured cells have been used in many specialized centers for definitive diagnosis of FAOD [3-7]. The evaluation of only concentration of acylcarnitines to describe severity of a defect or to find a correlation genotype/phenotype is not enough. Since the compartmental modeling is a powerful mathematical modeling approach that is widely used to obtain quantitative or predictive information about the dynamics of a system, kinetic models development for the study of FAO disorders should also be applied. A multi-compartment model is a type of mathematical model used for describing the way materials or energies are transmitted among the compartments of a system.

To develop a multi compartment models to investigate the fatty acid β-oxidation kinetics parameters, using uniformly labeled 13C-palmitate as substrate in vitro FAO enzymes measuring

Aims Model development

Methods Patients Cultured skin fibroblasts from healthy subjects and those from patients with documented enzyme deficiencies were obtained from Division of Laboratory Genetics at Mayo Clinic (Rochester, Minnesota). Normal control cell lines (n = 11) Mitochondrial FAOD cell lines (n = 20): very long-chain acyl-CoA dehydrogenase deficiencies (VLCAD; n=10), medium-chain acyl-CoA dehydrogenase (MCAD; n=5), short-chain acyl-CoA dehydrogenase (SCAD; n=5). Cell culture and incubation with palmitic acid and L-carnitine Skin fibroblasts were cultured in RPMI 1640 culture medium supplemented with 10% (v/v) foetal calf serum, 2 mmol/l glutamine and 1% (v/v) penicillin/streptomycin. After washing with 0.9% (w/v) of NaCl solution and trypsinization, the content of the flasks was centrifuged at 1000 rpm and the pellet obtained was resuspended to proceed with incubation or frozen until analysis. The pellet was resuspended in 25 ml of the cell culture medium described above, then subcultured in a 24-well culture plate and settled for 48-72 hours to form a cell monolayer. The following day the cells were washed twice with 0.9% w/v of NaCl solution and the medium was replaced with 0.5 mL of reaction mixture containing [U-13C]-palmitic acid 0.2 mmol/L complexed to bovine serum albumin 2 mg/mL, L-carnitine 0.4 mmol/L, glucose 1000mg/mL and fetal calf serum 10% in Ham F10 nutrient mixture, prepared just before use. Incubation of the cells was performed at 37°C in humidified 5% CO2/95% air. At the start of each experiment, medium was collected from each well and labelled as time zero (t0). After 24, 48, 72 and 96 hours supernatant was collected from each cell line (t24, t48, t72, t96). Each cell line was seeded in duplicates for time point. Supernatant collected from each well was transferred to a tube for acylcarnitine analysis, and the cell monolayer retained for protein quantization. Protein determination The cell monolayer was washed twice with 0.9% w/v of NaCl solution, hydrolyzed with 0.25mL of 2.0 mol/L sodium hydroxide and left overnight. It was then neutralized with 1.0 mol/L hydrochloric acid. The protein concentrations were measured by the bicinchoninic acid (BCA) method against the bovine serum albumin (BSA) standard curve. The standard curve was constructed with eight concentration points including zero, 25, 50, 100, 125, 250, 500 and 750 µg/mL, prepared by serial dilution of the stock BSA standard (2.0 mg/mL) in 0.9% aqueous sodium chloride solution (w/v) just before each batch of assays. Quantification was determined by absorbance at 562 nm on an Evolution 500 UV-Vis Spectrophotometer (Thermo Electron Corporation, Madison WI, USA). Acylcarnitines quantification by UPLC-ESI-MS/MS Reaction media (500 μL) were added of 250 μL acylcarnitine internal standard solution (d3-C2, d3-C3, d7-C4, d9-C5, d3-C6, d3-C8, d3-C10-, d3-C12, d3-C14, d3-C16, d3-C18) and 750 μL of methanol, frozen at –20°C for 10 minutes, and then centrifuged at 12000 rpm for 10 minutes to remove the precipitate. The supernatant was collected, dried under nitrogen flow at 60°C, and acylcarnitines, after derivatization to their butyl esters, dissolved in 50 μL of acetonitrile:water solution (80:20, v/v) and analyzed by a previously published method [8]. Instrument: Quattro Ultima (Waters); ESI+ source; MRM acquisition (m/z 85 fragment) UPLC (Acquity, Waters) BEH C18 (1.7µm, 2.1 x 150mm) column. Solvent A: H2O MilliQ + 0.1% TFA ; Solvent B: ACN + 0.1% TFA; flow rate 0.4ml/min; run time: 15 min. Injected sample 7 µL (splitted 1:10).

Fig.2 Compartmental model developed to study FAO in vitro experiment.

q8C*2

k1 k2 k3 k4 k5 k6

k8

q3C*12

FFq1

C*16

q2C*14

q4C*10

q5C*8

q6C*6

q7C*4

k7

Control SCAD MCAD VLCAD

[1] Rinaldo P., Matern D., Bennett M.J., (2002). Fatty acid oxidation disorders. Annu Rev. Physiol. 64: 477-502.

[2] Waddell L., Wiley V., Carpenter K., Bennetts B., Angel L., Andresen B.S., Wilcken B., (2005). Medium-chain acyl-CoA dehydrogenase deficiency: Genotype-biochemical phenotype correlations. Mol Genet Metab. 2006 Jan;87(1):32-9.

[3] Saudubray J.M., Coude F.X., Demaugre F., Johnson C., Gibson K.M., Nyhan W.L., (1982). Oxidation of fatty acids in cultured fibroblasts: a model for the detection and study of defects in oxidation. Pediatr Res. 16: 877-881.

[4] Roe C.R., Roe D.S., 1999, Recent developmen ts in the investigation of inherited metabolic disorders using cultured human cells. Molecular Genetics and Metabolism 68: 243-257.

[5] Ventura F.V., Costa C.G., Struys E.A., Ruiter J., Allers P., Ijlst L., Tavares de Almeida I., Duran M., Jakobs C., Wanders R.J.A., (1999). Quantitative acylcarnitine profiling in fibroblasts using [U-13C] palmitic acid: an improved tool for the diagnosis of fatty acid oxidation defects. Clinica Chimica Acta 281: 1-17.

[6] Tyni T., Pourfarzam M., Turnbull D.M., (2002). Analysis of mitochondrial fatty acid oxidation intermediates by tandem mass spectrometry from intact mitochondria prepared from homogenates of cultured fibroblasts, skeletal muscle cells, and fresh muscle. Pediatric Research 52: 64-70.

[7] Eaton S., (2002). Control of mitochondrial ß-oxidation flux. Prog. Lipid. Res. 41: 197-239.

[8] Hugh P., Barrett R., Bell M., Cobelli C., Golde H., Schumitzky A., Vicini P., Foster DM. (1998). SAAMII: simulation, analysis, and Modeling Software for tracer and Pharmacokinetic Studies. Metabolism 47(4): 484-92.

[9] Gucciardi A., P. Pirillo, Di Gangi IM, Naturale M, Giordano G. (2012). A rapid UPLC-MS/MS method for simultaneous separation of 48 acylcarnitines in dried blood spots and plasma useful as a second-tier test for expanded newborn screening. Anal Bioanal Chem 404(3): 741-51.

Kinetic results

Conclusions and Perspectives

References

Quantitative results

Fig 3 Acylcarnitines concentration vs. time curves and associated model fit curves

for fibroblasts experiment from a control, SCAD, MCAD and VLCAD subjects.

Butyl esters of labelled acylcarnitine derived from the precursor [U-13C] palmitate that had accumulated in the reaction media, after 24,48, 72 and 96 hours incubation (cell lines duplicates of each time point) were calculated and normalized to protein content (nmol/mg of protein). Mean±SD for each group are reported in Fig. 1.

Fibroblasts from VLCAD, SCAD and MCAD patients revealed comparable acylcarnitine profiles reflecting consistently substrate specificities. The VLCAD were characterized by dodecanoyl- (C12) myristoyl- (C14) and palmitoyl-(C16) carnitine, the MCAD by hexanoyl- (C6), octanoyl- (C8) and decanoyl- (C10), while SCAD by butyryl- (C4) carnitine.

An eight compartmental model (Fig.2) was developed using SAAM II ("Simulation Analysis And Modeling" version II, SAAM Institute Inc., University of Washington, Seattle, USA). Fluxes between compartments were considered to be unidirectional during the timeframe of the study, considering that reaction of beta-oxidation step were irreversible.

Where: Cn are the concentrations (in nmol/mg protein) of the labelled acylcarnitine intermediate, with suffix n= 16, 14, 12, 10, 8, 6, 4, 2 indicating the number of chain carbon units; Ċn represents their rate of change (in nmol/mg protein/h); ki (h-1) are the rate constants; τn,m quantify the delays, e.g. represents the delay between and time courses.

Group Sample k1 (C16 in) cv(%) k2 (C16:C14) cv(%) k3 (C14:C12) cv(%) k4 (C12:C10) cv(%) k5 (C10:C8) cv(%) k6 (C8:C6) cv(%) k7 (C6:C4) cv(%) k8 (C2) cv(%)

ctrl 1 0.99 32 12.97 33 3.68 33 0.97 32 1.43 33 7.19 33 4.17 34 2.60 33

ctrl 2 0.72 25 8.48 26 2.92 25 0.96 26 1.39 26 6.54 27 1.76 28 2.99 26

ctrl 3 - - 0.97 34 0.53 35 0.70 36 - - - -

ctrl 4 1.39 12 23.26 15 13.74 16 2.21 12 2.30 12 2.34 14 0.41 15 4.54 16

CONTROLS ctrl 5 0.65 22 8.26 22 4.10 22 1.10 22 1.33 23 1.41 23 0.46 24 1.68 23

ctrl 6 0.13 4 3.06 15 1.95 25 0.50 18 0.54 9 0.58 5 0.10 4 0.43 10

ctrl 7 0.08 12 0.82 13 0.51 13 0.16 13 0.23 14 0.35 26 0.02 70 0.41 17

ctrl 8 0.13 7 1.86 8 0.74 16 0.14 10 0.48 16 0.16 6 0.03 14 0.36 8

ctrl 9 0.14 19 2.78 21 1.50 20 0.40 21 0.45 21 0.43 22 0.10 25 0.76 21

ctrl 10 0.32 56 5.72 57 3.34 58 0.70 58 0.98 58 1.26 59 0.46 59 5.63 59

ctrl 11 0.42 36 5.10 37 2.99 38 0.77 38 0.99 38 1.17 39 0.54 39 4.34 39

VLCAD-M1 0.04 4 0.04 7 0.07 9 0.20 10 0.45 11 0.12 14 0.34 11 3.90 28

VLCAD-M2 0.05 4 0.04 3 0.08 3 0.18 8 0.35 8 0.08 31 0.40 11 1.44 10

VLCAD-M3 0.49 10 3.30 10 1.53 10 0.77 12 1.14 14 0.48 11 1.11 13 5.67 17

VLCAD-M4 0.04 5 0.11 6 0.11 7 0.21 8 0.41 8 0.47 9 0.62 8 2.10 16

VLCAD-M5 0.01 5 0.01 14 0.09 18 0.22 21 0.38 22 0.09 34 0.40 24 0.78 18

VLCAD-S1 0.05 4 0.15 6 0.09 6 0.08 8 0.11 8 0.00 Fixed 0.11 10 0.45 21

VLCAD-S2 0.16 7 1.48 10 0.73 9 0.30 9 0.34 9 0.09 20 0.34 9 1.53 12

VLCAD-S3 0.05 4 0.09 5 0.08 5 0.11 6 0.21 6 0.04 29 0.16 7 0.93 8

VLCAD-S4 0.51 13 3.40 14 1.75 14 1.00 16 1.72 18 0.43 14 1.96 19 8.45 18

VLCAD-S5 0.09 8 0.57 9 0.30 9 0.15 10 0.20 10 0.05 31 0.31 11 1.33 14

MCAD 1 0.08 16 0.44 16 0.27 19 0.12 28 0.07 47 1.65 45 0.65 Fixed 1.19 32

MCAD 2 0.09 10 0.42 11 0.31 12 0.15 14 0.11 17 1.88 18 1.35 27 1.34 18

MCAD 3 0.18 7 0.63 8 0.41 8 0.11 10 0.08 13 0.46 14 0.54 15 1.68 11

MCAD 4 0.07 13 1.72 14 1.17 14 0.18 16 0.03 23 0.09 29 2.24 29 1.77 31

MCAD 5 0.10 14 1.79 15 1.18 15 0.12 17 0.03 29 0.05 42 0.28 47 2.17 25

SCAD 1 0.12 15 1.88 22 1.59 20 0.54 16 0.58 16 2.17 16 0.21 17 2.95 18

SCAD 2 0.16 8 1.50 9 0.66 9 0.24 9 0.36 9 2.78 15 0.04 22 0.89 13

SCAD 3 0.08 51 4.31 52 3.37 52 1.21 52 1.08 53 6.89 54 0.98 55 1.78 53

SCAD 4 0.14 18 2.75 19 1.23 20 0.24 19 0.28 20 0.24 21 0.03 68 2.98 22

SCAD 5 0.13 10 1.72 10 1.20 11 0.32 11 0.41 11 0.30 13 0.04 24 2.03 16

MCAD

SCAD

VLCAD-M

VLCAD-S

Calculated rate constants ki with precision (CV%) are reported in Table 1. The time constants measured for each mild and severe VLCAD, SCAD and MCAD patient compared to mean values of control group are showed in Figure 4, 5, 6 and 7 respectively.

[U-13C] C2-carnitine

0 24 48 72 960.0

0.5

1.0

1.5

2.0

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C4-carnitine

0 24 48 72 960.0

0.5

1.0

1.5

2.0

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C6-carnitine

0 24 48 72 960.0

0.1

0.2

0.3

0.4

0.5

0.6

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C8-carnitine

0 24 48 72 960.0

0.1

0.2

0.3

0.4

0.5

0.6

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C10-carnitine

0 24 48 72 960.0

0.1

0.2

0.3

0.4

0.5

0.6

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C12-carnitine

0 24 48 72 960.0

0.5

1.0

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C14-carnitine

0 24 48 72 960.0

0.5

1.0

1.5

2.0

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C16-carnitine

0 24 48 72 960

1

2

3

4

5

6

Time (h)

nm

ol/

mg

pro

tein

s

[U-13C] C18-carnitine

0 24 48 72 960.000

0.025

0.050

0.075

0.100

Time (h)

nm

ol/

mg

pro

tein

s

CTRL severe VLCADmild or late onset VLCAD MCADSCAD

The model was individually fitted to the data of each patient studied obtaining good fits, as shown in Fig. 3.

Equations describing the model and calculated paramenters are:

Fig.1 Quantitative acylcarnitine profiling as nmol/mg protein (mean±SD)

Dr. Antonina Gucciardi, Post doc Mass Spectrometry Laboratory Women’s and Children’s Health Department University of Padova via Giustiniani 3, 35128 Padova (Italy) Tel. 00390498211434; fax 00390498213503 e-mail antonina.gucciardi@unipd.it

Contacts

Fig.4 Time constants in Mild VLCAD fibroblasts Fig.5 Time constants in Severe VLCAD fibroblasts

Fig.6 Time constants in SCAD fibroblasts Fig.7 Time constants in MCAD fibroblasts

• A multicompartmental model has been developed and kinetic parameters of fatty acid β-oxidation have been measured.

• Kinetic parameters were different between the various groups. After validation with a large number of experiments, the kinetic parameters/genotype relationships will be investigated.

• The model should constitute a useful explanatory tool to describe the processes involved in the enzyme activities and to differentiate kinetic of subclasses within the same enzyme deficiency. As a final point, a web-based application for the collection and reporting of analytical results will be developed.

VLCAD-S and VLCAD-M showed τ values quite different, within and between the groups (Fig. 4-5). As expected, in VLCAD-S time required to reduce C16 to C14 is extremely high if compared to controls. VLCAD M1 and M3 path, with a limiting rate in C16:C14 compartment, and a more evident lower activity in C14:C12, C12:C10 and C10:C8 compartments. Differently, other subjects revealed a C16:C14 activity very high and comparable to controls, and in VLCAD M2 and M5 τ were similar to control values. SCAD exhibited elevated time constants at C6:C4 compartment, except the mild form (SCAD3) comparable with controls. All MCAD subject resulted in highest time constant C10:C8, from 9 to 35 hours (10-45 fold slower than controls).