Adductomics: validation and progress

David H. PhillipsGeorge Preston

King’s College London

MRC-PHE Centre for Environment and Health, London

26 November 2014

Adductomics

What are adducts?

► Addition products; covalent addition of electrophilic species (reactive intermediates) of endogenous or exogenous origin to cellular macromolecules (DNA, protein)

What is an adductome?

► The totality of adducts in a defined cell type / tissue / etc.

Key questions

• Is the adduct profile characteristic of an exposure scenario or a disease state?

• Can adduct species be identified that may shed light on disease aetiology?

DNA adducts – limited amount of material available from biobanksProtein adducts – abundant quantities of albumin present in human serum

Serum albumin adducts as biomarkers of exposure

Rappaport, Williams et al., Toxicol. Lett., 2012, 213, 83-90

• Stephen Rappaport’s group (UC Berkeley) have been profiling adducts of human serum albumin.

Rationale for using HSA

► Present in circulating fluid; is a ‘systemic’ biomarker.

► High levels in serum (30 mg mL−1; ~ 0.5 mM).

► Long residence time (mean = 28 d)

► One major reactive locus (Cys-34) – simplifies analysis.

► Tryptic digest generates a 21-residue peptide (T3) containing the reactive locus (or an adduct thereof).

DAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQ C PFEDHVKLVNEVTEFAKTCVADESAENCDKSLHTLF GDKLCTVATLRETYGEMADCCAKQEPERNECFLQHKDDNPNLPRLVRPEVDVMCTAFHDNEETFLKKYLYEIARRHPYFYAPELLFFAKRYKAAFTECCQAADKAACLLPKLDELRDEGKASSAKQRLKCASLQKFGERAFKAWAVARLSQRFPKAEFAEVSKLVTDLTKVHTECCHGDLLECADDRADLAKYICENQDSISSKLKECCEKPLLEKSHCIAEVENDEMPADLPSLAADFVESKDVCKNYAEAKDVFLGMFLYEYARRHPDYSVVLLLRLAKTYETTLEKCCAAADPHECYAKVFDEFKPLVEEPQNLIKQNCELFEQLGEYKFQNALLVRYTKKVPQVSTPTLVEVSRNLGKVGSKCCKHPEAKRMPCAEDYLSVVLNQLCVLHEKTPVSDRVTKCCTESLVNRRPCFSALEVDETYVPKEFNAETFTFHADICTLSEKERQIKKQTALVELVKHKPKATKEQLKAVMDDFAAFVEKCCKADDKETCFAEEGKKLVAASQAALGL

Rationale for using HSA

► Present in circulating fluid; is a ‘systemic’ biomarker.

► High levels in serum (30 mg mL−1; ~ 0.5 mM).

► Long residence time (mean = 28 d)

► One major reactive locus (Cys-34) – simplifies analysis.

► Tryptic digest generates a 21-residue peptide (T3) containing the reactive locus (or an adduct thereof).

DAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQ C PFEDHVKLVNEVTEFAKTCVADESAENCDKSLHTLF GDKLCTVATLRETYGEMADCCAKQEPERNECFLQHKDDNPNLPRLVRPEVDVMCTAFHDNEETFLKKYLYEIARRHPYFYAPELLFFAKRYKAAFTECCQAADKAACLLPKLDELRDEGKASSAKQRLKCASLQKFGERAFKAWAVARLSQRFPKAEFAEVSKLVTDLTKVHTECCHGDLLECADDRADLAKYICENQDSISSKLKECCEKPLLEKSHCIAEVENDEMPADLPSLAADFVESKDVCKNYAEAKDVFLGMFLYEYARRHPDYSVVLLLRLAKTYETTLEKCCAAADPHECYAKVFDEFKPLVEEPQNLIKQNCELFEQLGEYKFQNALLVRYTKKVPQVSTPTLVEVSRNLGKVGSKCCKHPEAKRMPCAEDYLSVVLNQLCVLHEKTPVSDRVTKCCTESLVNRRPCFSALEVDETYVPKEFNAETFTFHADICTLSEKERQIKKQTALVELVKHKPKATKEQLKAVMDDFAAFVEKCCKADDKETCFAEEGKKLVAASQAALGL

An adductomics workflow

Li, Rappaport et al., Mol. Cell Proteomics, 2011, 10, 3, M110.004606

Proteins

Concept

Albumin level

Peptide level

Amino acid level

Serum level

A V IAL L F Q LQA Y QCPFEDHV

K

SR

A V IAL L F Q LQA Y QCPFEDHV

K

SR

Metabolites

NH

O

N

O

OHN

O NH2

S

R What is the mass of R?

T3

Implementation

‘Uncoupled LC-MS’

Study design: Piscina-2

• 120 serum samples (60 pairs; pre and post)

• Each pair randomly assigned to one of 12 batches (five pairs per batch).

• Blinded processing order is achieved by switching pre/post pairs at random.

PRE

POST

PRE

POST

PRE

POST

1 2 1 2 1 2

PRE

POST

PRE

1 2 1

POST

2

097 029 034 049 035Batch of five random pairs

Switch pairs at random

Reassign with new labels

Albumin extraction

Quantitation; digestion

HPLC fractionation (serial)

Mass spectrometry (serial)

Data processing

QC spiked serum

Internal standard only

QC serum extract

(13 × 12 = 156 samples for MS analysis)

Albumin extraction

• Batch extraction (5 × pre/post pairs + 1 × QC = 11 samples)

• In our hands, enrichment procedure proved time consuming and displayed limited efficacy.

• For Piscina-2 (and Exposomics pilot), albumin processed without enrichment.

Protein quantitation

• Protein recovery (i.e., concentration × volume) is an interesting metric.

• Extract-to-extract variability =

1.7% (RSD for batch QCs; n =

12).

• Variability within unknowns =

8.5% (RSD; n = 120).

• Some correlation between

samples from same individual.3.50 4.00 4.50 5.00 5.50 6.00 6.50

3.50

4.00

4.50

5.00

5.50

6.00

6.50

Protein recovered from ‘first’ sample / mg

Pro

tein

re

co

ve

red

fro

m ‘s

ec

on

d’ s

am

ple

/ m

g

Pressure-assisted digestion

• Batch digestion using Barocycler NEP2320

• Twelve digests per cartridge (5 × pre/post pairs + 2 × QCs)

• Run time = 30 min

• Generates high hydrostatic pressures via compressed air.

• High pressure stabilises partially-folded protein structures.

Fractionation

• Serial fractionation using RP-HPLC

• Fraction start/end defined by peptide tracers (prepared

in-house)

• Run time = 9.5 min

• Carry-over = nil (by UV210)

• Important ‘check-point’ for monitoring sample composition prior to MS.

Fractionation

4.514.614.714.814.915.015.115.215.315.41

Retention time stability for Piscina-2

INTSTDDigest marker

Sample running order

Rete

ntion

tim

e /

min

Fractionation

0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.0000.000

200.000400.000600.000800.000

1000.0001200.0001400.000

QCs introduced in digestion step (same extract, different digests)

BatchA_NormA210 BatchB_NormA210BatchC_NormA210 BatchD_NormA210BatchE_NormA210 BatchF*_NormA210BatchG_NormA210 BatchH_NormA210BatchI_NormA210 BatchJ_NormA210BatchK_NormA210 BatchL_NormA210BatchM_NormA210

0.000 1.000 2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.0000.000

200.000400.000600.000800.000

1000.0001200.0001400.000

Fully-processed QCs (different extracts, different digests) BatchA_NormA210 BatchB_NormA210

BatchC_NormA210 BatchD_NormA210BatchE_NormA210 BatchF*_NormA210BatchG_NormA210 BatchH_NormA210BatchI_NormA210 BatchJ_NormA210BatchK_NormA210 BatchL_NormA210BatchM_NormA210

• UV chromatograms of QC samples are a qualitative indicator of consistency

Triple-quadrupole mass spectrometry

0 2 4 6 8 10 12 14Time (min)

0

50

100

Re

lativ

e A

bu

nd

an

ce

6.752.46 12.862.19 6.916.072.91 9.80 12.605.82 13.551.98

1.771.691.59

1.54

NL: 2.11E4TIC F: + c NSI SRM ms2 835.280@cid20.00 [994.490-994.690] MS 25246

Strategy

• First round of analyses focused on a limited range of added masses (72–212 Da).

• This range avoids potential artefacts from sample prep (e.g., Cys oxidation products) and mass

spectrometry (e.g., metal adducts of unmodified T3).

• Preliminary profiles were acquired by filtering out responses below three times the mean

instrumental noise.

Example data [1]

72.176.6

81.185.6

90.194.6

99.1103.6

108.1112.6

117.1121.6

126.1130.6

135.1139.6

144.1148.6

153.1157.6

162.1166.6

171.1175.6

180.1184.6

189.1193.6

198.1202.6

207.1211.6

0.000.200.400.600.801.001.201.40

Group E

Transition added mass / Da

Ap

pa

ren

t q

ua

nti

ty /

pm

ol

Example data [1]

72.176.6

81.185.6

90.194.6

99.1103.6

108.1112.6

117.1121.6

126.1130.6

135.1139.6

144.1148.6

153.1157.6

162.1166.6

171.1175.6

180.1184.6

189.1193.6

198.1202.6

207.1211.6

0.000.200.400.600.801.001.201.40

Group E

Transition added mass / Da

Ap

pa

ren

t q

ua

nti

ty /

pm

ol

QC1QC2

Blank run (indicates no carry over)

Unknowns – 5 × pre/post pairs (still blinded)

~ 2 pmol of QC peptide

Example data [2]

72.176.6

81.185.6

90.194.6

99.1103.6

108.1112.6

117.1121.6

126.1130.6

135.1139.6

144.1148.6

153.1157.6

162.1166.6

171.1175.6

180.1184.6

189.1193.6

198.1202.6

207.1211.6

00.20.40.60.8

11.21.4

Group A

Transition added mass / Da

Ap

pa

ren

t q

ua

nti

ty /

pm

ol

Example data [3]

72.176.6

81.185.6

90.194.6

99.1103.6

108.1112.6

117.1121.6

126.1130.6

135.1139.6

144.1148.6

153.1157.6

162.1166.6

171.1175.6

180.1184.6

189.1193.6

198.1202.6

207.1211.6

0.000.200.400.600.801.001.201.40

Group D

Transition added mass / Da

Ap

pa

ren

t q

ua

nti

ty /

pm

ol

Observations

• Hits for certain added masses (e.g., 72, 108, 135 and 158 Da) were observed repeatedly.

• Most hits were of low intensity (i.e., approaching lower limit of detection). Further work

(ongoing) to accurately define dynamic range / extent of linearity / instrument precision for

low intensity adducts.

• Until validated, treatment of the data restricted to qualitative only.

• Blank runs confirm zero carry-over.

Summary and Conclusions

• By modifying elements of the original workflow, we have arrived at a robust method that is

adapted for higher throughput.

• The new workflow includes provision for QC.

• Sample quality and quantity can be checked at various stages of the workflow.

• Measurements made for Piscina-2 samples indicate a high level of consistency throughout

sample processing.

• Automated sample delivery for MS has been recently installed (July 2014) and optimised

(August 2014).

Adductomics – progress to dateSamples/cohort

n= MTA Samples received

Albumin Extraction

Protein Quant

Digestion Prep for HPLC*

Preparative HPLC

Mass spec

Data processing

Berkeley 18 √ √ √ √ √ √ √ (√)

Maastricht 60 √ √ √ √ √ √ √

MCC 123+ √ √ (√)

Piscina-2 120 √ √ √ √ √ √ √ √ (√)

Tapas-2 120 √ √ √

Oxford St √

HuGeF 127 √ √

* Involves acidification, dissolution, addition of internal standard and filtration through 0.45 µm

• Dr Osman SozeriEnvironmental Carcinogenesis Group, King’s College London

• Dr Anna CaldwellProf. John HalketMS facility, King’s College London

• Prof. Stephen Rappaport Dr Harry LiSamantha LuUniversity of California, Berkeley

• Equipment grant from the MRC-PHE Centre for Environment and Health

Acknowledgements