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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
Clinical toxicology analyses
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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 ?
Clinical toxicology analyses
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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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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)
Tandem Mass Spectrometry (MS/MS) and MRM (Multiple Reaction Monitoring)
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Challenge : developing a method for measuring the level of expression of the CYP in the patient
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Model organisms
intestineliver kidney
musclelungplasma heart
cells
brain
Method
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Model organisms
intestineliver kidney
musclelungplasma heart
cells
brain
Method
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Sample prep
proteinEnzymatic digestion
(Trypsin)
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Model organisms
intestineliver kidney
musclelungplasma heart
cells
brain
Method
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Sample prep
proteinEnzymatic digestion
(Trypsin)
UPLCSeparation
BEH130 C18, 2,1 x 100 mm, 1,7 µm
MRM analysisTriple quad
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Model organisms
intestineliver kidney
musclelungplasma heart
cells
brain
Method
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Sample prep
proteinEnzymatic digestion
(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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→ Need to characterize and measure substances in the environment
� 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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→ Need to characterize and measure substances in the environment
� 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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→ Need to characterize and measure substances in the environment
� 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 » ?
Need to pair the analytical data with epidemiology
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→ Need to characterize and measure substances in the environment
� 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
relevant
� 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 » ?
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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Lipidomics Analysis : Method
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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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Lipidomics Analysis : Method
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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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Lipidomics Analysis : Method
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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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Lipidomics Analysis : Method
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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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Lipidomics Analysis : Method
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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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