Bioinformatic treatment of human metabolome profile for diagnostics
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Bioinformatic Treatment of Human Metabolome Profile for Diagnostics. Dr. Petr Lokhov & Dr. Alexander Archakov. Institute of Biomedical Chemistry, RAMS. Human metabolome profile for diagnostics. ?. Set of small molecules (<1500Da) in biosample.

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Bioinformatic Treatment of Human Metabolome Profile for Diagnostics

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Bioinformatic treatment of human metabolome profile for diagnostics

Bioinformatic Treatment of Human Metabolome Profile for Diagnostics

Dr. Petr Lokhov & Dr. Alexander Archakov

Institute of Biomedical Chemistry, RAMS


Human metabolome profile for diagnostics

Human metabolome profile for diagnostics

?

Set of small molecules (<1500Da)

in biosample

Visual or/and numerical profile of set of small molecules

Biosample from human

Biofluid samples

(blood plasma)


Methods for metabolome profiling

Detection

Type of

Mass spectrometry

Methods for metabolome profiling

LC (liquid chromatography)

CE (capillary electrophoresis)

GC

NMR

MS (Mass spectrometry)

LC-MS

GC-MS

CE-MS

LC-NMR

Technique

Protocols of sample preparation

  • Ultrafiltration

  • Proteins sedimentation with organic solvent

Ionization

mode


Direct infusion electrospray esi mass spectrometry of blood plasma metabolites

Direct-infusion electrospray (ESI) mass spectrometry of blood plasma metabolites

1. Add methanol

Mass

spectrometry

2. Centrifuge

3. Take supernatant

Mass spectrometric

metabolome profile

blood plasma

100 µl

Soft method for protein precipitation (with methanol)

Direct-infusion of plasma metabolites in ion source

Electrosprey ionization

High accuracy MS

Reproducible, rapid and cheap method for metabolome profiling


Representative mass spectrum of blood plasma metabolites

Representative mass spectrum of blood plasma metabolites

x106

~ 2000 metabolite

ions are detected

diagnostic metabolites

lipidome

~ 2000 main metabolites

in human organism

Beecher C.W.W., in: Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis, Springer, 2003 pp. 311–335.

Da


Bioinformatic treament of metabolome profile

Bioinformatic treamentof metabolome profile

  • Normalization (isn’t required)

  • Baseline subtraction (isn’t required)

  • Mass spectrometry peaks alignment(common for mass spectrometry data processing)

  • Detection of ionic inconsistency in plasma samples

  • Dimensionality reduction of mass spectrometry data

  • Samples classification (diagnostics)


Ions in blood plasma samples that affect esi mass spectra

Ions in blood plasma samples that affect ESI-mass spectra

IonLevel in blood plasma sample Remark

H+ pH ~2.8sample + formic acid

Na+ 136–145 mM physiological conditions

K+ 3.5–60 mM plasma level (3-5 mM)

plus K+ leaked from

erythrocytes (80–120 mM)

Other ions - levels too low for influencing

ESI-mass spectra

potassium leaks from cells when plasma is not immediately separated from collected blood, or when blood has been temporarily stored, or plasma is handled roughly


Detection of ionic inconsistency in plasma samples

Detection of ionic inconsistency in plasma samples

K2Cl+ peaks in mass spectrum

Distribution of K+ in samples

good


Dimensionality reduction

Dimensionality reduction

Metabolite profile is multivariable characteristic of an organism.

&

To avoid overfitting,

the rule of 10–15 samples per variable should be followed.

The dimensionality of the mass spectrometry data should be reduced.


Dimensionality reduction by pca

Dimensionality reduction by PCA

disease

pathogenesis

case

PCA

PC1

PC3

X

PC2

2000 variables

(peak’s intensities)

control

risk factors

PC3

PC1

PC4

X

nutrition

PC5

age, sex…

X

Only PCs useful for diagnostics should be used


Samples classification diagnostics

Samples classification(diagnostics)

SVM

Diagnostics parameters

case

testing

  • Sensitivity TP/(TP+FN)

  • Specificity TN/(TN+FP)

  • Accuracy

PC3

control

PC1

Support Vector Machine (SVM) may classify multidimensional data by

formation of a hyperplane in a multidimensional space.


Biochemical context for diagnostics

Biochemical context for diagnostics

2000 variables

+

identification

Identified metabolites

0

PC3

accurate mass tag

isotopic pattern

MS/MS

MRM

Biochemical context for

metabolome-based

diagnostics

-

+

-

0

PC1


Ten leading cancer types 2010

Ten Leading Cancer Types, 2010

CA CANCER J CLIN 2010;60:277–300


Example 1 diagnostics of prostate cancer ii stage

Example 1: Diagnostics of prostate cancer II stage

Metabolome-based diagnostics

Sensitivity 95.0%

Specificity 96.7%

Accuracy 95.7%

PSA-based diagnostics

Sensitivity 35.0%

Specificity 83.3%

Accuracy 51.4%

Lokhov, Archakov et al.

Metabolic Fingerprinting of Blood Plasma from Patients with Prostate Cancer.Biochemistry (Moscow), 2010.

Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics. 2010


Example 2 diagnostics of lung cancer

Example 2: Diagnostics of lung cancer

Diagnostics

Cancer stage Sensitivity (%) Selectivity (%) Accuracy (%)

I 100.0 93.9

II 91.4 92.3

III 92.3 92.4

IV 93.2 92.5

I-IV 91.1 93.3

92.4

Lokhov, Archakov et al.Diagnosis of lung cancer based on direct-infusion electrospray mass spectrometry of blood

plasma metabolites. International Journal of Mass Spectrometry. 2011


Diagnostics of lung cancer identified metabolites

Diagnostics of lung cancer (identified metabolites)

Identified metabolites

(PC1)

Exposure to tobacco

smoke

  • Biotin sulfone

  • Creatinine

  • R-benzene

    • ethylbenzoic acid

    • аcetanisol

    • dimethylbenzoic acid

    • benzenepropionate

  • Permethrin

  • Halfenprox

  • ….

Metabolites reflecting exposure organism to tobacco smoke contribute in diagnostics


Risk of lung cancer development

Risk of lung cancer development

Cigaretteconsumption

Metabolome-based approach

Levels of:

Smoker/non-smoker

Cigarettes smoked per day

Biotin sulfone

Creatinine

R-benzene

Permethrin

Halfenprox

….

Individual differences in

how cigarettes are smoked

Ranges of nicotine intake per

cigarette

Age

Body weight

Objectively calculated

exposure to tobacco smoke

Averaged and inexactly

calculated exposure

to tobacco smoke

OR (odd ration) - Risk of disease development

OR (smokers/non-smokers)=4

OR (R-benzene)=38


Conclusions

Conclusions

Metabolome profile for diagnostics can be obtained by direct-infusion mass spectrometry of blood plasma sample.

The profile quality can be checked using data from profile itself.

Dimensionality reduction allows following to rule 10-15 samples per variable and select groups of metabolites useful for diagnostics.

Metabolome profile can be used for early diagnostics of lung and prostate cancers as well for calculation of risk of lung cancer development.

One metabolome profile is needed for diagnostics and prognosis of lung cancer.


Acknowledgements

Acknowledgements

Program “Proteomics for Medicine and Biotechnology” of Russian Academy of Medical Sciences.

Russian Foundation for Basic Research

Russian N.N. Blokhin Cancer Research Center


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