du poison vers l’homme empoisonné, nouvelles clinique .... laprévote gpco...mean (pmol/mg)...

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Du poison vers l’homme empoisonné, nouvelles approaches omiques en toxicology analytique

Pr. Olivier Laprévote

Université Paris descartes& Hôpital Lariboisière, AP-HP

Atelier annuel du GPCO (Groupe de Pharmacologie Clinique et Oncologique) « la pharmaco-métabolomique »26-27 novembre 2015

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� Paracelse (1493-1541):

�Dosis facit Venenum

�Poison is in everything, and no thing is without poison. The dosage makes it either a poison or a remedy.

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� Mathieu-Joseph-Bonaventure Orfila (1787-1853)

� 'Treaty of poisons from the mineral, vegetal and animal kingdoms or general toxicology'

� use of animals to describe adverse effects of chemicals

� development of analytical protocols to provide legal proof in forensic cases

� Marie Capelle was accused of having poisoned her husband, Mr Lafarge, with arsenic (1840)

'now, crime will be hunted successfully into its last refuge’

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The Marie Besnard case …the expert’s failure (1949)

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Toxicological analysis = analysis of the poison

� Forensic toxicology : identification of the poison and eventually its quantitation is a piece of the legal evidence

� Clinical Toxicology: identification of the poison and/or its metabolites and eventually their quantitation is essential for the diagnostic and management of the patient→ antidotal treatments available

� Environmental Toxicology: identification of environmental pollutants is essential to the risk assessment and the implementation of prevention policies

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Clinical toxicology analyses

� Drug-drug interactions (5% of hospital admissions per year)

� 1000 patients are treated in the Toxicological Critical Care

Department (Pr B. Megarbane) at Lariboisière hospital

� Suicide attempts are most often performed by mixtures of household

products or pharmaceutical drugs

� Without any particular information of the intoxication etiology, the

untargeted toxicological analyses (screening) is mandatory

� Chromatography / Mass Spectrometry hyphenation is the method of

choice

� Specificity, sensitivity, speed, $ (cost)

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1 : TR= 1.477min : caffein2 : TR= 3.056min : lidocaïne3 : TR= 4.545min : bromazepam metabolite 14 : TR=5.338min : bromazepam5 : TR=5.988min : bromazepam metabolite 26 : TR=6.574min : dextropropoxyphen

Exemple of a toxicological screening of a patient


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� Effective but….

� The suspected molecules are generally the intrinsically most toxic; not

necessarily the most often prescribed,

� The poisoning events observed in hospital are mainly due to

polyintoxications involving an average of 3 molecules, sometimes much

more

� Usually the mother-molecules are those which are detected and

quantified, not their metabolites,

� What can do the clinician with a list of 6 or 7 molecules in mutual

interactions ?


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To understand the toxic disease we have to focus on the poisoned person, not only to the poison

� Anamnesis

� Clinical data

� Metabolism capabilities of the patient

� detect the pharmacokinetic interactions by assaying molecules

and metabolites

� characterize and quantitate most prescribed molecules and not

only the most toxic

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To understand the toxic disease we have to focus on the poisoned person, not only to the poison

� Anamnesis

� Clinical data

� Metabolism of the patient

� detect the pharmacokinetic interactions by assaying molecules

and metabolites

� characterize and quantitate most prescribed molecules and not

only the most toxic

� Measure the individual capabilities of biotransformation

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Elimination pathways of the « top 200 » of the most prescribed drugs in 2002

Wienkers and Heath, 2005

Elimination pathways

Metabolism CYPs

Importance of cytochromes P450 in the metabolism of xenobiotics

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Xenobiotic

Absorption

Distribution

Metabolism

Elimination

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Challenge : developing a method for measuring the level of expression of the CYP in the patient

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Liquid Chromatography (LC)Separation of peptides depending on their physico-chemical properties (retention time)C18 column (reverse phase)Gradients: H2O, ACN, methanol

Mass Spectrometry (MS)

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Liquid Chromatography (LC)Separation of peptides depending on their physico-chemical properties (retention time)C18 column (reverse phase)Gradients: H2O, ACN, methanol

Mass Spectrometry (MS)

Tandem Mass Spectrometry (MS/MS) and MRM (Multiple Reaction Monitoring)

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Model organisms

intestineliver kidney

musclelungplasma heart

cells

brain

Method

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Model organisms



cells

brain

Method

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Sample prep

proteinEnzymatic digestion

(Trypsin)

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Model organisms



cells

brain

Method

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Sample prep


(Trypsin)

UPLCSeparation

BEH130 C18, 2,1 x 100 mm, 1,7 µm

MRM analysisTriple quad

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Model organisms



cells

brain

Method

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Sample prep


(Trypsin)

UPLCSeparation

BEH130 C18, 2,1 x 100 mm, 1,7 µm

MRM analysisTriple quad

0 1 2 3 4 5 6 7 8 90

255075

1000

10203006

121807

14212835 571,4 → 392,3

571,4 → 474,3

571,4 → 587,4

571,4 → 783,6

Temps (min)

5,17

Inte

nsité

x 1

06

200 400 600 800 10000

1

2

3

4

5

y+

10

1028,92y+

9

971,87

y+

8

884,91

y+

7

783,69

y+

6

686,56y+

4

474,28

y+

5

587,40

b+

2

170,94

Inte

nsité

x 1

05

m/z

y2+

7

392,38

MS/MS of [M+H]2+

Proteotypic peptide of CYP1A2 IGSTPVLVLSR [M+2H]2+ 571,4

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Peptide m/z precursor ionFragmentttype

m/z fragment

Scan time (ms)

Collision energy

(eV)

P1_2J2 VIGQGQQPSTAAR [M+2H] 2+ 656,91 y6+ 602,38 50 25

y7+ 730,44 50 23

y9+ 915,62 50 23

y11+ 1100,7 50 22

P17_3A4 EVTNFLR [M+2H] 2+ 439,74 y1+ 175,16 14 13

y3+ 435,28 14 14

y4+ 549,38 14 15

y5+ 650,3 14 15

P19_2C9 GIFPLAER [M+2H] 2+ 451,7 F+ 120,26 14 22

y3+ 375,2 14 20

y4+ 488, 3 14 20

y5+ 585,36 14 13

P16_2D6 DIEVQGFR [M+2H] 2+ 482,23 a2+ 201,1 14 17

y4+ 507,3 14 14

y5+ 606,38 14 15

y6+ 735,3 14 13

P3_1A2 IGSTPVLVLSR [M+2H] 2+ 571,41 y7+ 783,62 14 21

y5+ 587,43 14 22

y4+ 474,3 14 22

y72+ 392,32 14 20

P13_3A5 LDTQGLLQPEKPIVLK [M+2H] 3+ 598,1 y152+ 840,4 14 17

y122+ 668,18 14 16

y142+ 782,8 14 17

y8+ 923,6 14 23

GF EGVNDNEEGFFSAR [M+2H] 2+ 785,91 y3+ 333,18 14 24

y4+ 480,26 14 21

y12-NH32+ 684,48 14 25

y7+ 813,5 14 27

Method

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Mean (pmol/mg) Microsomes 87 ± 49,4 CV% 57 Min-Max 25-271 Mitochondrias 18,3 ± 9,1 50

�Considerable interindividual variation in terms of protein expression level

Results

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A. Al Ali et al.Anal. Bioanal. Chem. 406 (20), 4861-4874 (2014)

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Environmental toxicology

� Quantitative problem: the global production of chemicals increased

from 1 million tons in 1930 to 400 million now

� Qualitative problem: 60 000 substances are commonly used

� Problem of concentration dynamics:

� mg.L-1 : Nitrates

� µg.L-1 : Pesticides

� ng.L-1 : Pharmaceuticals

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Environmental toxicology

� Quantitative problem: the global production of chemicals increased

from 1 million tons in 1930 to 400 million now

� Qualitative problem: 60 000 substances are commonly used

� Problem of concentration dynamics:

� mg.L-1 : Nitrates

� µg.L-1 : Pesticides

� ng.L-1 : Pharmaceuticals

→ Need to characterize and measure substances in the environment

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Recupérer pres

pharma à Helène

0

20

40

60

80

100Fréquence de détection (%)

Chemical diversity

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� But…

� Problem of complex mixtures for which the additivity of toxic effects is

not always relevant

� Problem of low doses : le paradigm « dose makes poison » is not always

rekevant

� Problem of (very) long term exposures difficult to evaluate with cell or

animal models

� What to do with,… what to understand from a « Prévert inventory » ?

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� But…


not always relevant


rekevant


animal models


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� But…


not always relevant


rekevant


animal models


Need to pair the analytical data with epidemiology

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� But…


not always relevant


relevant


animal models


Need to pair the analytical data with epidemiology

Need to find biomarkers of toxic effects in exposed population

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Exemple : lipid metabolism disruption induced by phtalates

• Plasticizers of PVC : food packaging, paints, clothing, cosmetics, medical equipment or medications DEHP

MEHP

• Endocrine disruptors• Deleterious effects on reproductive and

development functions • Ligands of peroxisome proliferator-activated

receptors (PPAR)

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PPAR-γ- Highly expressed in human placenta

- Directly involved in placental cells differentiation and placenta functions- Important role in placental lipid metabolism

→ Differential untargeted lipidomics study of placental cell cultures exposed or not to MEHP

Exemple : lipid metabolism disruption induced by phtalates

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MEHP quantitation : Method

Extraction of MEHP

Solid Phase Extraction –Mixte mode

OASIS WAX cartridge

Sample prep

Cell pelletsMechanical grinding

Culture supernatantDilution 1/10

MEHP analysis

UPLC-HRMS

- C18 column - Electrospray – Negative ion mode- Retention time: 1.9 min- m/z 277,1445- Internal standard: 13C-MEHP- Analysis time: 5 min

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Data treatment

Multivariate statistical Analysis

Extraction des lipides

Liquid/Liquid extraction(tert-Butylmethylether

+ Methanol)

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Analysis of lipid extracts

UPLC-HRMS : ESI+/ ESI-

Sample preparation

Mechanical grindingCell pellets

Quality Controls

QC (Pools) 1/1, 1/3, 1/6

Lipidomics Analysis : Method

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Chromatograms raw data

Tridimensional Matrix Intensity of each variable (m/z, tR)

In each sample

VariablesESI+: 4285ESI-: 1150

72 Injections (40 samples +MIX+ CQ)

Multivariate statistical analysis

Data Extraction• Detection • Pairing• Alignment• Integration

Normalisation / Filtration

VariablesESI+: 1500ESI-: 900

Annotation of discriminating

variables

Univariate statistical analysis

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Representation: Score scatter plot of the PCA model

Treated Control

�� Separation of MEHP and Control groups

� Unsupervised multivariate statistical analysis:

� Principal components analysis (PCA)

� Comparison of MEHP-treated samples vs control samples

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Separation within

groups

Separation

between

groups

treated

Control

�� Confirmation of the two groups separation depending on their exposure to MEHP

Representation : Score scatter plot of the PLS-DA Model

� Supervised multivariate statistical analysis

� Partial least squares Discriminant Analysis (PLS-DA)

� Comparison of MEHP-treated samples vs control samples

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S-plot OPLS-DA

�� 157 discriminating variables

Statistical weight of variables

Variables significance

� Supervised multivariate statistical analysis:

� Representation of the variables in function of their relative « weight » and

"significance" in the OPLS-DA model

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Family Number of

lipids

Ratio MEHP/Control

increase decrease

Triacylglycerols (TG) 18 1,6 (+51%) -

Phosphatidylethanolamines (PE)6 PE 1,1 (+14%) 0,8 (-17%)

3 PE-P - 0,8 (-14%)

Phosphatidylcholines (PC)

7 PC 1,2 (+10%) 0,9 (-6%)

6 PC-O - 0,8 (-16%)

8 PC-P - 0,7 (-24%)

Sphingomyelines (SM) 4 - 0,8 (-16%)

Diacylglycerols (DG) 2 - 0,8 (-14%)

Phosphatidylserine (PS) 1 1,2 (+17%) -

Phosphatidylglycerol (PG) 1 - 0,7 (-29%)

MEHP induces in-deep disturbances of lipid composition of placental cells when exposed to MEHP

� 65 identified lipids are differently « expressed » in MEHP and control groups

� 56 lipids species show a statistically significant abundance

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PART III

DECIPHER THE HISTONE CODE: AN AGNOSTIC PROCESS TO UNMASK BIOMARKERS OF EXPOSURE TO XENOBIOTICS

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Epigenetics (C.H. Waddington, 1942)regulation of gene expression without ADN sequence adulteration

HISTONES

methylation ADN

Micro-RNA

+ =

1 genome

n épigénomes

n phenotype

Epigenome : • What genes are expressed at time t, in a given cell and under defined environmental conditions ?• Involvement in many chronic diseases: cancers, neurodegenerations,…

environment

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sub-types (H2A, H2B, H3, H4) and variants

High diversity of post-translational modifications (acetylation, methylation,

phosphorylation,…)

High number of modified residues (Arg, Lys, Ser, Tyr, Thr)

Interrelation between modified residues

Combinatorial information lost in conventional proteomics approaches

High sequence identity between variants and subtypes = very few proteotypic peptides

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The « histone code »

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control exposed

Biological samples histones profiling UPLC-MS Data treatment

Matrix of variables (rt/m/z)

Univariate statistics Multivariate analysis: Pattern recognition

extraction

normalisation

Deciphering the histone-code : analytical strategy

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UPLC-ESI-QTOFtR, m/z and intensity

Histones profiling: Mass Spectrometry

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Human placental model : BeWo cell line (clone b30)

- syncitium formation

- endocrine functions

Exposition at sub-confluence during 24h (37°C, 5% C O2)

Experimental conditions

Sodium Butyrate (SB) Benzo[a ]pyrene (B[a ]P)

Universal inhibitor of HDAC

Exposure to 1 & 2,5 mM vs vehicle (CM)

Genotoxic chemical pollutant (PAH)

exposure 1 µM vs vehicle (DMSO)

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Sodium Butyrate

Hierarchical classification43

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Sodium butyrate: unsupervised analysis

Principal component analysis (PCA)

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training set, n = 20test set, n = 10100% correct predictions

Sosium Butyrate: supervised analysis (OPLS-DA)

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Sodium butyrate : univariate statisticsPutative Identity

UniProtKB Accession

number

Observed Mass (Da) ∆m (Da) PTMs

VIP Scores

RSD (%)FDR (q-value) Ratio *

H2A-1B/E P04908 14005 3x14 1 ac 1.62 9.3 8.7E-08 1.38

H2B-1K O60814 13758.5

0 *** 1.55 11.9 2.0E-07 -1.54

3x14 1 ac 1.65 16.7 6.1E-07 1.63

4x14 1 ac + 1 me1 2.07 4.9 2.0E-14 1.66

5x14 1 ac + 2 me1 / 1 me2 2.27 9.6 2.3E-09 1.77

6x14 2 ac 2.37 9.6 2.9E-07 1.87

H2B-1M Q99879 13857.5 6x14 1 ac + 1 me2 + 1 me1 1.79 18 7.8E-05 1.60

H4 P62805 11236.5

3x14 1 ac 2.51 16.7 3.9E-14 -2.22

4x14 1 ac + 1me1 2.13 9 9.1E-13 -1.72

5x14 1 ac + 1me2 1.64 4.5 6.7E-14 -1.37

8x14 2 ac + 1me2 1.77 5.2 6.5E-13 1.48

9x14 3 ac 3.01 11.3 7.9E-09 3.10

11x14 3 ac + 1me2 3.08 7.7 8.1E-13 3.17

12x14 4 ac 2.84 7.6 9.4E-11 2.45

14x14 4 ac + 1me2 3.19 16.3 4.7E-14 3.35

15x14 5 ac 2.08 9.6 4.8E-09 1.74

18x14 6 ac 2.81 12.7 1.1E-07 2.59

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Benzo[a]pyrène : results

*

*

Monoacetylated H2A.Z = Biomarker of B[ a]P exposure

AUC = 0,984

ROC curve: performance of a binary classifier system as its discrimination threshold is varied

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Conclusion

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� In Analytical Toxicology, the characterization and quantitation of the

toxic compounds will always remain necessary but…

� They must be accompanied by the characterization of EFFECT

BIOMARKERS

� Metabolic biomarkers (lipids), proteins and enzymes, epigenetics,…

� From data to knowledge : system Biology has to play the major role

� Goals

� Measure and anticipate the risk at the population level : go to a molecular

epidemiology

� Measure and anticipate the risk at the individual scale : personalized

toxicology

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• Sylvie Gillet

• Nicolas Auzeil

• Raphaël Bilgraer

• Julia Petit

• Justine Lanzini

• Nouzha Oussedik

• Danièle Evain-Brion

• Sophie Gil

• Thierry Fournier

And their co-workers

Many thanks to

• Philippe Beaune

• Marie-Anne Loriot

• Isabelle de Waziers

• Ahmad Al Ali

And their co-workers

• Alain Brunelle

• David Touboul

• Jean-Pierre Le Caër

• Isabelle Schmitz-Afonso

• And their co-workers

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Thank you for your attention

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du poison vers l’homme empoisonné, nouvelles clinique .... laprévote gpco...mean (pmol/mg)...

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