Post on 12-Jan-2016
transcript
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