Next Generation Metabolic Screening is a novel technique that can be applied to body fluids as urine, plasma or cerebrospinal fluid for the diagnosis of inborn errors of metabolism. The technique gives a holistic view on metabolism (metabolomics) and uses LC_Qtof mass spectrometry. The technique was developed in Nijmegen, The Netherlands in the group of Prof Ron Wevers (ron.wevers@radboudumc.nl)

1. Next Generation Metabolic Screening
11th Middle East Metabolic
Group meeting - MEMG
22-25 October 2014,
Marrakech, Morocco
Prof. dr. Ron Wevers,
Department of Laboratory Medicine,
Radboudumc, Nijmegen, The Netherlands

2. 1997
There is no single analytical platform that can
measure all metabolites
The metabolome

3. NMR based metabolomics
BODY FLUID NMR SPECTROSCOPY IN INBORN ERRORS OF METABOLISM
Body fluid NMR
• Diagnosis of 106 IEM (1D
and 2D COSY)
• 1H resonances from 158
metabolites involved IEM
• Seven novel inborn errors
could be defined
Limitation
Sensitivity: low micromolar
range

4. Molecular complexity in Life Science
Number of Dynamic
molecular entities range__
Genomics 104 103
Proteomics 106 1010+
Metabolomics 104 106+
Typical eukaryotic organisms contain between 4000 and 50000
metabolites (Kegg: 16896; HMDB 2012: 7900; HMDB 3.6: 41808)

5. METABOLIC SCREENING
IN THE INDIVIDUAL PATIENT

6. Metabolomics – analysis of “all” metabolites
Human plasma, CSF
(urine)
Controls vs. patient
Agilent QTOF MS-data
- Reverse phase liquid chromatography
- Positive and negative mode
- Features
• Accurate mass (165.07898)
• Retention time
• Intensity
(New) biomarkers for diseases
XCMS
Alignment
Peak comparison
> 10,000 Features

7. Q-tof mass spectrometry
Molecular mass: 118.0266
C4H6O4
Methylmalonic acid

8. Accuracy of Q-tof analysis
Deviation from actual mass for 19 metabolites
(Mass range 90.0552 – 428.3737 Dalton)
 mass number of
metabolites
0.0000 2
0.0001 10
0.0002 4
0.0003 2
0.0004 1
19

9. Q-tof sensitivity
Sensitivity
Q-TOF
low nanomolar range
NMR
low micromolar range
Factor 1000 more
sensitive than
NMR!
Pipecolic acid
S/N=20
500 nmol/L
50 nmol/L
5 nmol/L
no addition
Pipecolic acid added to urine (diluted 50x)

10. Validation Next Generation Metabolic Screening (NGMS)
• Comparison signal intensity with
concentratios classical assays
• Mass accuracy on QTOF:
95%: ΔM < 0,0003 Da
(range: 90-425 Da; n=19)
• Sensitivity of UHPLC-QTOF MS assay in low nM range!
(~1000 fold more sensitive than NMR)
• Intra: CV in RT: <0.5%; CV in signal intensity: <15%
Inter: CV in RT: <1%; CV in signal intensity: <25-30%
• Clinical validation: diagnosis established in
16 individual patients with different IEMs
Standard operation procedure for plasma and CSF samples introduced in patient care in 2014

11. Where is/are the biomarker(s)?
10,480
features
10,480 Features (incl. adducts etc)
Mass, Retention time, Intensity

12. INPUT
 Endogenous metabolites
 Diet derived
 Intestinal flora input
 Medication
ANALYTICAL
 Original small molecules
 Adduct information (Na+, K+, NH4
+)
 In source fragments
 13C variants
How to explain 10.000 features in plasma?

13. Which features are the IEM biomarkers
10,480 features
Experiment
Alignment
Peak Comparison
Raw data
Corrected t-test
Intensity/P Ranking
Dataanalyses
Identification T20
Verification
DataPreprocessing
&Pretreatment
Data
interpretation

14. sG sT Feature
Patient 7
Feature
Intensity P-value Mass Ret
1 74 1909 24490807 2E-17 166.08634 3.67
2 51 1695 8386343 8E-21 120.08065 3.67
3 47 1683 2915430 6E-21 167.08956 3.67
4 62 1780 751272 2E-19 121.08385 3.67
5 56 1765 556095 3E-20 103.05433 3.67
6 38 1611 480141 1E-22 120.12527 3.67
7 57 1956 460491 3E-20 166.19191 3.67
8 55 1761 403223 3E-20 131.04910 3.67
9 35 1559 355404 2E-23 188.06796 3.67
10 3 1137 303853 8E-40 295.12893 5.88
11 65 4994 291156 5E-19 188.12476 3.66
12 49 1762 269007 7E-21 107.04902 3.67
13 43 1659 252382 6E-22 168.09171 3.67
14 52 1727 250238 8E-21 149.05937 3.67
15 32 1492 140562 6E-25 120.15785 3.67
16 76 2854 135373 2E-17 189.07304 1.04
17 16 1018 126136 7E-29 328.13918 4.01
18 23 1516 96662 1E-26 120.17050 3.67
19 135 265 93406 1E-10 104.10735 12.85
20 96 670 77111 4E-14 397.23525 14.31
21 128 294 76090 3E-11 104.10733 12.67
22 120 79 64437 3E-12 467.25918 7.45
23 66 1719 61501 6E-19 204.04192 3.67
24 125 89 60054 2E-11 467.00845 7.45
25 121 91 57801 3E-12 622.67550 7.45
26 108 87 57092 9E-13 622.34113 7.45
27 4 1139 51484 2E-37 296.13220 5.88
28 97 65 51462 5E-14 405.22508 12.55
▼
Patiënt 7
Feature 1909
Controls
P7
P7
PKU
HMDB: phenylalanine
Feature 1137
Controls
P7
P7
PKU
HMDB: glutamylphenylalanine
PKU: two biomarkers

15. Phenylketonuria
Patiënt Metaboliet rI rP
PKU-1 (BB07/01056)
Phe=204 (ref 17-140)
L-Phenylalanine
F8091 (m152.94715; 18.34)
F1018 (m328.13918; r4.01)
Glutamylphenylalanine
N-Acetyl-L-phenylalanine
1
5
15
16
134
285
141
28
51
162
PKU-2 (BB09/01551)
Phe=202 (ref 17-140)
L-Phenylalanine
Glutamylphenylalanine
F1018 (m328.13918; r4.01)
N-Acetyl-L-phenylalanine
1
16
20
126
447
42
28
86
PKU-3 (BB09/01183)
Phe=355 (ref 17-140)
L-Phenylalanine
Glutamylphenylalanine
F1018 (m328.13918; r4.01)
N-Acetyl-L-phenylalanine
1
14
15
128
100
9
8
116
PKU-4 (BB09/00590)
Phe=528 (ref 17-140)
L-Phenylalanine
Glutamylphenylalanine
F1018 (m328.13918; r4.01)
N-Acetyl-L-phenylalanine
1
9
12
42
1
10
8
12
PKU-5 (BB09/01951)
Phe=384 (ref 17-140)
L-Phenylalanine
Glutamylphenylalanine
F1018 (m328.13918; r4.01)
N-Acetyl-L-phenylalanine
1
58
59
142
204
93
66
67
PKU-6 (BB09/02152)
Phe=241 (ref 17-140)
L-Phenylalanine
F1018 (m328.13918; r4.01)
Glutamylphenylalanine
1
13
14
101
19
32
PKU-7 (BB10/00030)
Phe=359 (ref 17-140)
L-Phenylalanine
Glutamylphenylalanine
F1018 (m328.13918; r4.01)
N-Acetyl-L-phenylalanine
1
10
17
160
74
3
16
117
Control
PKU
P6
P6
N-Acetyl-L-phenylalanine
[F1031; m208.0968, r7.65]
I
F1018 is an as yet unidentified
biomarker for PKU
II
PKU: but also two as yet unknown biomarkers

16. rI rP Feature
Patient 7
Feature
Intensity P-value Mass Ret
1 74 1909 24490807 2E-17 166.08634 3.67
2 51 1695 8386343 8E-21 120.08065 3.67
3 47 1683 2915430 6E-21 167.08956 3.67
4 62 1780 751272 2E-19 121.08385 3.67
5 56 1765 556095 3E-20 103.05433 3.67
6 38 1611 480141 1E-22 120.12527 3.67
7 57 1956 460491 3E-20 166.19191 3.67
8 55 1761 403223 3E-20 131.04910 3.67
9 35 1559 355404 2E-23 188.06796 3.67
10 3 1137 303853 8E-40 295.12893 5.88
11 65 4994 291156 5E-19 188.12476 3.66
12 49 1762 269007 7E-21 107.04902 3.67
13 43 1659 252382 6E-22 168.09171 3.67
14 52 1727 250238 8E-21 149.05937 3.67
15 32 1492 140562 6E-25 120.15785 3.67
16 76 2854 135373 2E-17 189.07304 1.04
17 16 1018 126136 7E-29 328.13918 4.01
18 23 1516 96662 1E-26 120.17050 3.67
19 135 265 93406 1E-10 104.10735 12.85
20 96 670 77111 4E-14 397.23525 14.31
21 128 294 76090 3E-11 104.10733 12.67
22 120 79 64437 3E-12 467.25918 7.45
23 66 1719 61501 6E-19 204.04192 3.67
24 125 89 60054 2E-11 467.00845 7.45
25 121 91 57801 3E-12 622.67550 7.45
26 108 87 57092 9E-13 622.34113 7.45
27 4 1139 51484 2E-37 296.13220 5.88
28 97 65 51462 5E-14 405.22508 12.55
▼
222
Compound Adduct In-source
L-Phenylalanine M+H
L-Phenylalanine +
L-Phenylalanine 13C
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine M+Na
Glutamylphenylalanine M+Na
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine +
L-Phenylalanine +
?
?N-(1-Deoxy-1-fructosyl)phenylalanine
L-Phenylalanine +
?
?
?
?
L-Phenylalanine M+K
?
?
?
Glutamylphenylalanine 13C
?
Patient 7: Phe=359 µmol/L (ref 17-140)

17. PKU1
66
PKU5
197
PKU6
64
PKU3
73
PKU4
90
PKU7
80
PKU2
195
45 features
(7 metabolites)
- Phenylalanine
- Glutamylphenylalanine
- Phenylalanine + C6H10O5
- Mass 278.17518 Unknown
-Prolyl-L-phenylalanine + H2
- N-Acetyl-L-phenylalanine
- Mass 424.17163 Unknown
Plasma biomarkers for PKU patients

18. Untargeted metabolomics: Xanthinuria
Xanthine Uric acid
Child, 2.5 months with kidney stones; urine metabolome analysis
Xanthine 
Xanthine oxidase
Uric acid 
CONCLUSIONS:
• Xanthinuria I (isolated xanthine oxidase)
• Xanthinuria II (combined xanthine oxidase
+ aldehyde oxidase defect)

19. Targeted analysis of markers specific for xanthinuria type II
CONCLUSION:
Xanthinuria type II (diagnosed in urine sample without allopurinol loading)
Hydantoin 5-propionate Pyridoxal

20. The clinical validation
Amino acid
disorders
Fatty acid
oxidation
Organic acidurias Miscellaneous
PKU MCAD MSUD Xanthinuria II
Hyperprolinemia II VLCAD HMG-CoA lyase Amino acylase I
Hyperlysinemia MCC Antiquitin (ATQ) def.
MAT I/III IVA Beta-ketothiolase
Alcaptonuria Dimethylglycinuria
Ureidopropionase
Diagnosis on plasma samples
Current status: 23 inborn errors

21. Where is Wally?

22. 2012
Patient
Targeted
Metabolic
screen
Targeted
gene
analysis
Diagnosis
+ follow-up
2013 / 2014
Patient
Whole
exome
sequencing
Targeted
confirmatory
metabolite +
enzyme
testing
Diagnosis
+ follow-up
Targeted assays vs holistic approach
Next generation
metabolic
screening
Times are changing… whole exome analysis

23. Nijmegen four day march
The bridge between the exome and the metabolome
The Nijmegen approach

24. Conclusions
• Next Generation Metabolic Screening (NGMS) introduced
in diagnostics of the individual patient
• NGMS will change the metabolic laboratory
• NGMS bridges whole exome sequencing and metabolic
diagnostics: integrative biology in a functional genomics
setting
• The technique we have developed is also of interest outside
the field of inborn errors

25. METABOLIC SCREENING
 The individual patient suspected for an IEM
 WES and NMGS in parallel together with dept. Genetics Radboudumc

26. C.D.G Huigen
E. van der Heeft
U.F.H. Engelke
R.A. Wevers
L.A.J. Kluijtmans
Nijmegen metabolomics
C. van Karnebeek,
Vancouver
J. Engel
S. Wortmann-Hagemann

28. The human metabolome HMDB
Food constituents FooDB
Medication Drugbank
Genetic disease OMIM
Pathway analysis Pathman-SMP
(Kegg)
Challenge - 1
Integrate databases

29. Challenge - 2
The issue
Apolar
LC-Qtof This lecture (reversed phase)
* column options Zhang T; Anal Chem 2012
Polar
GC-(tof)MS Fiehn O; Trends in Anal Chem 2008
Capillary Electrophoresis-(tof)MS Ramataur R; Mol Biosyst 2011
HILIC Nijmegen, 2014

30. Challenge - 3
The big data
• Pathway analysis
• How to integrate Whole Exome Sequencing data with the
metabolomics data?
METABOLIC SCREENING
IN THE INDIVIDUAL PATIENT
DNA SEQUENCING
IN THE INDIVIDUAL PATIENT
Integrating
software
The next step
The coding data of the human
genes
10,480

31. Challenge - 4
The intracellular metabolome

33. HILIC-ToF-MS analysis urine samples (XBridge amide)
Reproducibility retention times
Standard solution polar compounds (n = 4)
Verbinding Retentietijd
gemiddelde %CV
Arginine 10.07 0.08
Asparagine 8.59 0.10
Aspartic acid 9.47 0.41
Glutamic acid 8.69 0.33
Glutamine 8.37 0.18
Histidine 9.84 0.46
Isoleucine 5.77 0.48
Leucine 5.50 0.57
Lysine 10.33 0.14
Methionine 6.07 0.37
Phenylalanine 5.35 0.34
Proline 6.52 0.49
Serine 8.46 0.20
Threonine 7.81 0.17
Tryptophan 5.31 0.11
Tyrosine 6.44 0.00
Valine 6.39 0.08
Quality requirement RT: CV ≤ 0.5%

34. HILIC-ToF-MS analyse van urinemonsters. Kolom: XBridge amide
Herhaalbaarheid retentietijden
Analyse standaardoplossing polaire metabolieten (n = 2)
Eis: retentietijd %CV ≤ 0.5%Verbinding Retentietijd
gemiddelde ∆rt
Cytosine 4.33 0.04
Kreatinine 3.61 0.05
Betaine 5.92 0.09
Nicotinic acid 3.94 0.17
Kreatine 7.47 0.06
Adenine 3.28 0.01
Hypoxanthine 3.60 0.06
Xanthine 3.97 0.04
Carnitine 6.75 0.09
2,8-Dihydroxyadenine 7.36 0.03
Cafeïne 1.42 -0.01
Cytidine 5.41 0.08
2'-deoxyadenosine 3.08 0.01
2'-deoxyinosine 3.96 0.06
2'-deoxyguanosine 4.93 0.04
Adenosine 3.68 0.02
Inosine 4.78 0.08
Guanosine 5.76 0.07
Xanthosine 5.52 0.12