TO DOWNLOAD A COPY OF THIS POSTER VISIT WWW.WATERS.COM/POSTERS
INTRODUCTION
HIV is one of the main health risks in today’s society in many regions of the world. Most current methods of treatment are based on HIV-reverse transcriptase and protease inhibition to reduce the rate of HIV replication. There is however a clear need for more effective treatment methods and drug-candidates to effectively treat the disease. Some of these target the interaction between virus and T-cell, the primary cell type infected by HIV. Whereas classical virology studies show interesting anti-HIV effects, it remains unclear whether or not the drug candidate may have other consequences on the T-cell itself. One modern way of studying the molecular mode of action of a potential next generation class of anti-HIV compounds, as well as to get a first insight into possible side effects (safety issues) is to look at the effect on the proteins expressed by compound treated T-cells versus untreated ones.
A promising compound (CADA, Figure1) was selected for a feasibility study and a large batch of CD4+T-cells – a human T-cell line SUPT-1 – were grown1,2. Part of the culture was treated with an effective dose of compound and harvested at a relevant time thereafter. Their protein content was comparatively analyzed with that of an equivalent amount of untreated T-cells from the very same culture.
The results presented here are from a label-free quantitative LC-MS analysis of Human T-cell line proteins. The effect of the treatment was monitored by utilizing the relative quantification results, which provided invaluable input for the investigation on the mechanism of action of the compound on targeted and non-targeted cellular components.
QUANTITATIVE LABEL-FREE NANOSCALE LC-MS ANALYSIS OF HIV DRUG-TREATED HUMAN T-CELL CULTURES
Johannes P.C. Vissers1, Kurt Vermeire2, Dominique Schols2, Thérèse McKenna1, Timothy Riley,1 Jim. I. Langridge1, Peter D. Verhaert3 1 Waters Corporation, MS Technologies Center, Manchester, United Kingdom
2 Rega Institute for Medical Research, K.U. Leuven, Belgium 3 Delft University of Technology, Delft, Netherlands
METHODS Sample preparation
To study the effect of CADA on surface CD4 receptor expression, human T-cells were incubated with the compound, stained with anti-CD4 monoclonal antibody and analysed by flow cytometry as illustrated in Figure 2. The effect of CADA on T-cell HIV infection is shown in Figure 3. Membrane and soluble protein fractions have been studied. In this study, the focus is on the soluble proteins. The soluble protein content of the investigated cell line was extracted and subsequently denatured with RapiGestTM SF surfactant (0.1%) (Waters Corp.), reduced (10 mM DTT), alkylated (10 mM IAA) and enzymatically digested with trypsin, 1:50 (w/w) enzyme:protein ratio. LC-MS conditions
LC-MS quantification experiments were conducted using a 1.5 hr reversed phase gradient from 5 to 40% acetonitrile (0.1% formic acid) at 250 nL/min on a nanoACQUITY UPLCTM System (Waters Corp.). An Atlantis® 3μm C18 75 μm x 15 cm nanoscale LC column (Waters Corp.) was used, with all samples run in triplicate. Typical on-column sample loads were 0.5 μg protein digest—which is the equivalent of less than 50,000 cells. The Q-Tof PremierTM mass spectrometer (Waters Corp.) was programmed to step between normal (5 eV) and elevated (20-35 eV) collision energies on the gas cell, using a scan time of 1.5 s per function over the m/z range 50-1990. Protein identifications and quantitative information were generated by the use of dedicated algorithms (Waters® Protein Expression Informatics), and searching against a human species-specific database.
References
1. K. Vermeire and Schols, D. (2005) Cyclotriazadisulfonamides: promising new CD4-targeted anti-HIV drugs, J. Antimicrobial Chemotherapy 56, 270-272
2. K. Vermeire and Schols, D. (2003) Specific CD4 down-modulating compounds with potent HIV activity, J. Leukocyte Biology 74, 1-9
3. Silva, J.C., Denny R., Dorschel, C.A., Gorenstein, M., Kass, I.J., Li, G.-Z., McKenna, T., Nold, M.J., Richardson, K., Young P., and Geromanos, S.J. (2005) Quantitative proteomic analysis by accurate mass retention time pairs, Anal. Chem. 77, 2187-2200
4. Hughes, M.A., Silva, J.C. Geromanos, S.J. and Townsend, C.A. (2006) J. Proteome Res. 5, 54-63
5. Silva, J.C., Gorenstein, M.V., Li, G.-Z., Vissers, J.P.C., Geromanos, S.J. (2006) Absolute quantification of proteins by LCMSE; a virtue of parallel MS acquisition, Mol. Cell. Proteomics 5, 144-156
Figure 3. Correlation between anti-HIV potency and CD4 down-modulating capability of CADA. T-cells were infected with HIV in the presence of different doses of CADA. After 4 days, supernatant was collected and analyzed for its p24 (HIV antigen) content (vertical bars). In parallel — uninfected T-cells were treated with the same doses of CADA — and CD4 expression was analyzed flow cytometrically after 4 days of incubation. The MFI of the Leu3a-FITC staining is depicted for the different doses of CADA (line).
RESULTS & DISCUSSION LC-MSE data
Low and elevated spectra and chromatograms are obtained in a parallel fashion. Typical spectra are shown in Figure 4. The low energy data are used for the quantification of the peptides and subsequently proteins, whereas the high-energy information is utilized for qualitative, identification purposes3.
CONCLUSION • The analytical protocols used in this study are capable of
reproducibly measuring the intensities of peptides in a very complex protein mixture allowing subsequent quantification
• The soluble protein content of the investigated T-cell line is hardly affected by CADA treatment — i.e. no significant peptide and protein regulation has been observed by means of the employed label free LC-MS technique.
• The biochemical data are not reflected in these results (CD4 is a membrane protein and therefore not likely to be represented in the currently studied sample)
• Absolute LC-MS quantification methods offer means to assess the amount of detected protein
Relative Quantification
The intensity measurements were normalized on the intensity measurement of the internal standard peptides utilizing the three best ionizing peptides identified to a protein5. The normalized intensity measurements were subsequently expressed as relative values of which the results are shown in Figures 8 to 10. Figure 1. Chemical structure of cyclotriazadisulfonamide (CADA)
Figure 2. CD4 down-modulation in human T-cells after incubation with CADA (5 μg/ml). Cell surface CD4 expression of untreated and CADA-treated cells after staining with the specific anti-CD4 mAb shown. Mean fluorescence intensities are indicated between brackets. An isotype control is included to measure the background staining.
Figure 4. Example low (bottom pane) and elevated energy mass spectra (top pane) taken at 36.9 min of the treated T-cell sample. The quality of the clustered LC-MS data was accessed prior to quantifica-tion. Parameters that are typically measured are retention time reproduci-bility, mass precision and intensity variation of accurate mass/retention time clusters3,4. The quality assessment results are illustrated in Figures 5, 6 and 7, respectively.
Figure 10. Common and uniquely identified proteins in both investigated conditions. Blue = up regulated in the treated sample; red = down regu-lated in the treated sample; grey = condition unique identification
Figure 11. Number of identified proteins across both investigated sam-ples — i.e. control and CADA-treated T-cells
CH2
N N
N
SSO
O O
H
+
H3C CH3
O
Cl-
10 2 0.4 0.08 0CADA (μg/ml)
0
100
200
300
400
500
HIV
-1 p
24 (n
g/m
l)
0
20
40
60
80
100
CD
4 M
FI
CD4 MFIp24
Binned ppm0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
0
200
400
600
800
1000
1200
1400
1600
Figure 6. Mass precision distribution accurate mass/retention time clus-ters (replication rate ≥ 3 out of 6).
Figure 8. Accurate mass/retention time clusters (elog intensity control vs. elog intensity treated). The blue annotated clusters are significantly regu-lated peptides — student T-test.
Figure 9. Relative protein concentration (2log ratio) of both conditions (control and treated) commonly identified proteins (2 ≥ fragment ions/peptide, 2 ≥ peptides identified; replication rate ≥ 2)
Figure 5. Retention time reproducibility distribution accurate mass/retention time clusters (replication rate ≥ 3 out of 6).
0.5 ug treated SupT1 cells
m/z50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250 1300 1350
%
0
100
%
0
100TUD_023b 479 (36.997) 2: TOF MS ES+
2.94e3110.0575
86.0851
175.1082
569.2693348.1500
320.1596227.0944
234.1418244.1222
481.2371384.1819
438.2312
552.2664
682.3596
570.2899
576.2735
592.3289
631.2910
804.3717
683.3710
718.3120719.3300
945.4714
805.3862
874.4122
1151.5668946.5031
947.5012 1152.56761153.5439
TUD_023b 478 (36.917) 1: TOF MS ES+ 5.21e3576.2294
569.2781
498.2523348.1569
86.0885110.0614 175.1131 249.1527 471.2386427.2130562.2729
576.7515
577.2739
577.7965
804.3927640.3157 682.3788
704.3524 1151.5416805.4175939.4174
elevated energy MS
low energy MS
Absolute Quantification
The absolute amounts of all identified proteins — not taking into account post-translational or chemical modifications — were estimated using a recently published absolute concentration formula5: The total amount of protein that could be quantified for the control sample equaled 0.349 μg/0.5 μg injected with 2 ≥ fragment ions/peptide, 2 ≥ peptides identified, and replication rate ≥ 2. For the treated sample the justifiable amount was 0.311 μg/0.5 μg.
[ ]standard internalstandard internalintensity peptide normalized
protein xintensity peptide normalizedn
1i
n
1i ⋅∑
∑
=
=
The results shown in Figures 9 and 10 suggest that the regulation of the commonly identified proteins is very mild and that the number of significantly regulated proteins very low (~ 5 - 10). Furthermore, certain proteins are identified in only one of the investigated conditions— see Figure 11. To date it has not been established if this is a reflection of the biology or the experiment — above/below detection threshold due to sample dynamic range constraints.
124 54 35
coun
t
mass precision (ppm)
Binned %CV RT0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2
0
1000
2000
3000
4000
5000
coun
t
% CV tr
%CV bin
0
10
20
30
40
50
60
70
80
90
100
rela
tive
num
ber (
%)
% CV cluster intensity
Figure 7. Relative and cumulative distribution of the variance coefficient of the intensities for the accurate mass/retention time clusters of the com-plete dataset.
[control Sup T1 cells]_control Sup T1 cells_Log Ave Normal Int2 2.5 3 3.5 4 4.5 5 5.5 6
2
2.5
3
3.5
4
4.5
5
5.5
up-regulated peptides
down-regulated peptides
e log
inte
nsity
trea
ted
T-ce
lls
elog intensity control T-cells
index
0 20 40 60 80 100 120
-1.5
-1
-0.5
0
0.5
1
1.5
up-regulated proteins
down-regulated proteins
index #
2 log
treat
ed/c
ontro
l
2log ratio treated/control-1.5 -1 -0.5 0 0.5 1 1.5
143B_HUMAN (P31946) 14-3-3protein beta/alpha (Protein kina
ANX1_HUMAN (P04083)Annexin I (Lipocortin I) (Calpactin
II)
ENPL_HUMAN (P14625)Endoplasmin precursor (94 kDa
glucose-r
HS9B_HUMAN (P08238) Heatshock protein HSP 90-beta (HSP
84)
MDHC_HUMAN (P40925) Malatedehydrogenase
PDX1_HUMAN (Q06830)Peroxiredoxin 1 (Thioredoxin
peroxidase
Q16351 (Q16351)SB1.8/DXS423E protein
Q9UPN4 (Q9UPN4) KIAA1118protein (Fragment)
RL9_HUMAN (P32969) 60Sribosomal protein L9
SAP_HUMAN (P07602)Proactivator polypeptide
precursor [Cont
THIO_HUMAN (P10599)Thioredoxin (ATL-derived factor)
(ADF)
2log treated/control
Monocytes
U937
CD4 EXPRESSION
CEL
L N
UM
BER
Lymfocytes
THP-1
(178)
(41)(26)
(16)
(6)(4)
(39)
(7)(4)
Isotype controlIsotype control
Cells + CADA Cells + CADA
Cells + MEDIUM Cells + MEDIUM
(6)
(103)(15)(6)
(103)(15)
CD4 Expression
cell
num
ber
Isotype control
Cells + CADA
Cells + medium
0 40
0
(6) (15)
(103)
100 101 103 104 102
