Dual-Platform Proteomics Study of Plasma Biomarkers in Pediatric Patients Undergoing Cardiopulmonary Bypass

Article (PDF Available)inPediatric Research 67(6):641-9 · March 2010with44 Reads
DOI: 10.1203/PDR.0b013e3181dceef5 · Source: PubMed
Plasma samples from pediatric cardiac patients undergoing cardiopulmonary bypass (CPB) procedures were used to identify and characterize patterns of changes in potential biomarkers related to tissue damage and inflammation. These included proteins associated with systemic inflammatory response syndrome. Potential biomarkers were identified using a dual-platform proteomics approach requiring approximately 150 microL of plasma, which included two-dimensional difference gel electrophoresis (2D-DIGE) and a multiplexed immunoassay. Methods used in the dual approach measured levels of 129 proteins in plasma from pediatric CPB patients. Of these, 70 proteins changed significantly (p<0.05) between time points, and 36 of these retained significance after the highly stringent Bonferroni correction [p<0.001 for 2D-DIGE and p<0.00056 for multianalyte profile (MAP) assays]. Many of the changing proteins were associated with tissue damage, inflammation, and oxidative stress. This study uses a novel approach that combines two discovery proteomics techniques to identify a pattern of potential biomarkers changing after CPB. This approach required only 150 microL of plasma per time point and provided quantitative information on 129 proteins. The changes in levels of expression of these proteins may provide insight into the understanding, treatment, and prevention of systemic inflammation, thereby helping to improve the outcomes of pediatric CPB patients.
Dual-Platform Proteomics Study of Plasma Biomarkers in
Pediatric Patients Undergoing Cardiopulmonary Bypass
Department of Pediatrics [T.M.U., C.-J.K.L., J.L.M., J.B.C., N.J.T., A.U
., D.S.P.], Department of Pharmacology [W.M.F., K.E.V.],
Department of Surgery [J.L.M., J.B.C., A.U
.], Department of Public Health Sciences [N.J.T., V.M.C.], Department of Bioengineering
.], Pennsylvania State University College of Medicine, Hershey, Pennsylvania 17033
ABSTRACT: Plasma samples from pediatric cardiac patients under-
going cardiopulmonary bypass (CPB) procedures were used to iden-
tify and characterize patterns of changes in potential biomarkers
related to tissue damage and inflammation. These included proteins
associated with systemic inflammatory response syndrome. Potential
biomarkers were identified using a dual-platform proteomics ap-
proach requiring 150
L of plasma, which included two-
dimensional difference gel electrophoresis (2D-DIGE) and a multi-
plexed immunoassay. Methods used in the dual approach measured
levels of 129 proteins in plasma from pediatric CPB patients. Of
these, 70 proteins changed significantly (p 0.05) between time
points, and 36 of these retained significance after the highly stringent
Bonferroni correction [p 0.001 for 2D-DIGE and p 0.00056 for
multianalyte profile (MAP) assays]. Many of the changing proteins
were associated with tissue damage, inflammation, and oxidative
stress. This study uses a novel approach that combines two discovery
proteomics techniques to identify a pattern of potential biomarkers
changing after CPB. This approach required only 150
L of plasma
per time point and provided quantitative information on 129 proteins.
The changes in levels of expression of these proteins may provide
insight into the understanding, treatment, and prevention of systemic
inflammation, thereby helping to improve the outcomes of pediatric
CPB patients. (Pediatr Res 67: 641–649, 2010)
onsiderable evidence suggests that systemic inflammation
is responsible for many postoperative complications that
can lead to multiple organ dysfunction syndrome (1). Cur-
rently, there are no effective methods for preventing systemic
inflammatory response syndrome (SIRS) in cardiac surgery
patients (1). We hypothesize that identifying a panel of bi-
omarkers related to systemic inflammation associated with
cardiopulmonary bypass (CPB) could prove useful in predict-
ing patient outcome, identifying potential targets for treat-
ment, and reducing the incidence of vital organ dysfunction
and other complications.
We have established a multidisciplinary research team for
combining clinical, basic science, and engineering tools fo-
cused on minimizing vital organ injury during CPB proce-
dures in neonates, infants, and small children (2–5). In previ-
ous pilot reports, we have used two-dimensional difference gel
electrophoresis (2D-DIGE), a gel-based discovery proteomics
approach to study changes in plasma protein expression in
pediatric patients undergoing CPB (2,3). In these studies, we
have identified some proteins whose levels are consistently
altered by CPB.
In the current study, we used a dual-platform proteomics
approach to compare plasma protein samples before and after
cardiac surgery involving CPB to identify potential plasma
biomarkers, particularly those related to SIRS, and to charac-
terize the changes they undergo during CPB. One platform
used was 2D-DIGE, a gel-based discovery proteomics tech-
nique (6,7), coupled with matrix-assisted laser desorption
ionization-time-of-flight/time-of-flight (MALDI-ToF/ToF)
tandem mass spectrometry. This combination of methods for
protein quantification and identification of proteins has proven
very useful in doing quantitative comparisons of protein, and
our group has used it in several preliminary investigations of
plasma protein changes after CPB (2,3). We coupled our
2D-DIGE studies with a commercially available multianalyte
immunoassay capable of measuring 90 different proteins,
including many low abundance molecules and some proteins
that were also identified and quantified by 2D-DIGE. The
pairing of these techniques made it possible to simultaneously
analyze changes in expression of 129 proteins in plasma,
potentially allowing us to identify proteins and pathways,
which have not been explored in previous studies of CPB.
Cardiopulmonary bypass. Isoflurane and fentanyl facilitated with pancu-
ronium was used to maintain anesthesia, and a median sternotomy was used
for all operations. The CPB circuit used a Jostra HL-20 heart-lung machine
(Jostra USA, Austin, TX), a Stockert heart-cooler system (Sorin Group USA,
Arvada, CO), Capiox hollow fiber membrane oxygenators, and a Capiox
Received September 8, 2009; accepted January 17, 2010.
Correspondence: Akif U
ndar, Ph.D., Departments of Pediatrics, Surgery and Bioengi-
neering, Penn State Hershey College of Medicine, 500 University Drive, Hershey, PA
17033; e-mail: aundar@psu.edu
Supported by the Children’s Miracle Network (to A.U
.) and by a grant from the
Barsumian Foundation (to W.M.F.).
. and D.S.P. contributed equally to this work.
Supplemental digital content is available for this article. Direct URL citations appear
in the printed text and are provided in the HTML and PDF versions of this article on the
journal’s Web site (www.pedresearch.org).
Abbreviations: CPB, cardiopulmonary bypass; MALDI-ToF/ToF, matrix-
assisted laser desorption ionization-time-of-flight/time-of-flight; MAP, mul-
tianalyte profile; MS/MS, mass spectrometry and tandem mass spectrometry;
SIRS, systemic inflammatory response syndrome; 2D-DIGE, two-dimen-
sional difference gel electrophoresis
Vol. 67, No. 6, 2010
Copyright © 2010 International Pediatric Research Foundation, Inc.
Printed in U.S.A.
pediatric 32
m arterial filter (Terumo Cardiovascular Systems, Ann Arbor,
MI). Conventional nonpulsatile flow was used in all patients. Circuit priming
solution comprising lactated Ringer’s, albumin, and blood was provided.
Jenkins (8) risk categories [RACHS-1 (risk adjustment for congenital heart
surgery)] were used for analyzing severity of surgical procedures. Cardiople-
gia is administered through a cannula inserted into the ascending aorta placed
between the heart and the aortic cross-clamp. The composition of the solution
is four parts oxygenated blood to one part Buckberg cardioplegic solution
(cold induction formula) administered at 6 8°C for 4 min to arrest the heart.
The flow is adjusted to deliver the cardioplegia at a system pressure of
90 –100 mm Hg. Subsequent maintenance doses using the “multidose for-
mula” are administered every 20 –30 min for 2 min. Characteristics of the
patients and CPB parameters and clinical data are tabulated in Table 1.
Sample collection and storage. This study was approved by the Penn State
College of Medicine Institutional Review Board, and consent forms were
signed for all enrolled patients. In addition, a Data Safety Monitoring Board
has been established for this study. Blood samples were collected in tubes
containing potassium EDTA from 10 pediatric patients undergoing CPB. A
baseline sample was collected for each patient 30 min before incision and
another sample 24 h after weaning from CPB. The blood was centrifuged, the
plasma was removed, and the aliquots were stored at 80°C until analysis.
Depletion of plasma for 2D-DIGE. Removal of 14 high abundance serum
proteins (albumin, IgG,
-1-antitrypsin, IgA, transferrin, haptoglobin, fibrin-
-1-acid glycoprotein, IgM, apolipoprotein A-I,
apolipoprotein A-II, complement C3, and transthyretin) was performed using
a Human 14 Multiple Affinity Removal System (MARS) column, 4.6 100
mm (Agilent Technologies, Inc., Palo Alto, CA) following the manufacturer’s
instructions. Briefly, 40
L of plasma was diluted to a final volume of 500
with buffer A (Agilent Technologies) and filtered through a 0.22-
m filter
before applying to the column. The proteins that did not bind were subjected
to 2D-DIGE. Plasma depletion was performed only on samples to be analyzed
by 2D-DIGE, and no depletion was done on those to be analyzed by the
multianalyte immunoassay.
Sample preparation for 2D-DIGE. Preparation of samples and 2D-DIGE
procedures were described previously (7). Additional information about the
2D-DIGE study is provided in a form that is in concordance with the
minimum information about a proteomics experiment (MIAPE)– gel electro-
phoresis standards (9), which are currently under development by the Human
Proteome Organization Proteomics Standards Initiative (HUPO-PSI) (Table
S1: http://links.lww.com/PDR/A59). Depleted samples (50
g) were sub-
jected to minimal labeling with CyDye DIGE Fluors for 2D-DIGE following
the manufacturer’s instructions (GE Healthcare). Individual samples were
randomly labeled with either Cy3 or Cy5 in equal numbers, and a normal-
ization pool containing an equal amount of each sample was created and
labeled with Cy2. A second pool containing equal amounts of each sample
was also created and left unlabeled to be used for the preparative/picking gel
for identification of protein spots by mass spectrometry.
Two-dimensional difference gel electrophoresis. For 2D-DIGE, 50
a Cy3-labeled sample, 50
g of a Cy5-labeled sample, and 50
Cy2-labeled pool were mixed for each gel. Samples were applied to prehy-
drated 24-cm pH 4 –7 Immobiline DryStrips (GE Healthcare) using cup
loading and subjected to isoelectric focusing. After focusing, strips were
equilibrated and sealed on second-dimension 10% polyacrylamide gels (20.5
cm-L 25.5 cm-W 1 mm-D) using agarose and the focused proteins
separated by molecular weight. Gels were then imaged with a Typhoon 9400
fluorescent imager (GE Healthcare) using a different emission filter for each
CyDye, and the images were analyzed using Progenesis SameSpots (version
2.0; Nonlinear USA, Durham, NC). Preparative/picking gels were stained
using Deep Purple Total Protein Stain (GE Healthcare) and then, also, imaged
using the Typhoon 9400.
Protein identification by mass spectrometry. Identification of proteins by
mass spectrometry was performed as described previously (7). Briefly, protein
spots were picked from preparative/picking gels using the Ettan Spot Picker
(GE Healthcare) and the gel plugs digested with trypsin. The digested
proteins/peptides were extracted from the gel plugs, cleaned and concentrated
using C
ZipTips (Millipore, Billerica, MA), and spotted onto 384-well
MALDI plates with matrix. Peptides were analyzed by mass spectrometry
using the 4800 MALDI ToF/ToF Proteomics Analyzer (Applied Biosystems)
in the Mass Spectrometry Core at the Penn State College of Medicine. GPS
Explorer 3.6 software (Applied Biosystems) was used to submit the mass
spectrometry (MS) and tandem mass spectrometry (MS/MS) data to the
MASCOT search engine and the NCBI nonredundant database, and human
taxonomy was used for identification. MASCOT confidence interval scores of
95% were considered a positive protein identification. For proteins having
multiple isoforms, statistical analyses were done on the sum of all identified
isoforms for that protein.
Multianalyte profile assays. An undepleted 100
L aliquot of each sample
was sent to Rules Based Medicine (Austin, TX), a clinical laboratory im-
provement amendments (CLIA)-certified laboratory. Analysis of the samples
was performed using the HumanMAP version 1.6 Antigen panel, capable of
detecting 90 different human antigens related to tissue damage, inflammation,
or other pathologies.
Statistical and bioinformatic analysis. Information about the acquisition
and processing of data from the 2D-DIGE studies are provided in the form
recommended for MIAPE Gel Informatics currently being developed by the
HUPO-PSI (http://www.psidev.info/index.php?q node/83) (Table S2:
http://links.lww.com/PDR/A59). Multivariate statistical analysis of the pro-
teomic data for the protein spots was done using Progenesis Stats (Nonlinear
USA) to perform principal components analysis (PCA), power analysis of the
data, and for t tests for the individual spots. Protein measurements from
both the 2D-DIGE and multianalyte profile (MAP) methods were analyzed
separately, and their values were transformed to natural logarithms to
better approximate normality. A repeated measurements analysis of vari-
ance was applied to compare samples collected at the two time points
using SAS version 9.1 (SAS Institute, Cary, NC). Significance was
reported both with and without Bonferroni adjustments.
Proteins identified as having significant changes (p 0.05 by repeated
measurements) either by 2D-DIGE or MAP were then combined for bioin-
formatic analyses. Ontological groupings were analyzed for these proteins
using the PANTHER database (www.pantherdb.org) (SRI International) (10).
All proteins undergoing significant changes were then imported into the
Ingenuity Pathway Analysis (IPA) system (Ingenuity Systems, Redwood
City, CA) to visualize and examine potential networks of proteins and
canonical pathways.
2D-DIGE analysis. Passage of plasma samples through the
Human-14 MARS column removed 95% of the original
protein content containing the 14 most abundant plasma pro-
teins. The completeness of removal of these proteins was
confirmed by the absence of spots corresponding to them on
the resulting 2D-DIGE gels.
A total of 556 protein spots were visualized and matched in all
gels. Of the 556 matched spots, 175 (31.5%) changed signifi-
cantly from the baseline (pre-CPB) time point to the 24 h
post-CPB time point (t test, p 0.05) with 75 (13.5%) increases
and 100 (18.0%) decreases. By using MALDI-ToF/ToF, we
identified (with MASCOT confidence interval scores of 95%)
the proteins that comprised 269 of the 556 matched spots. The
identified spots accounted for 48.4% of the total spots and 88.0%
of the total expressed protein resolved by the gel.
Many plasma proteins consist of a number of isoforms
corresponding to multiple gel spots. Therefore, after analysis
and identification of individual gel spots, we added together
the normalized volumes for all gel spots/isoforms that were
determined by MALDI-ToF/ToF to be isoforms of the same
protein and performed statistical analysis on the new values
representing the whole proteins. A list of proteins, including
NCBI and Swiss-Prot accession numbers and the biologic
processes and molecular functions assigned to each by the
PANTHER database for all identified 2D-DIGE proteins, can
be found in the Table S3 (http://links.lww.com/PDR/A59).
Figure 1 depicts the reference gel with the identified pro-
teins and all of their isoforms circled and numbered. Forty-
four proteins were identified by 2D-DIGE and MALDI-ToF/
ToF. These were compared, and it was determined that 25
(56.8%) changed significantly (p 0.05 by repeated measure-
ments analysis) between time points with 10 (22.7%) protein
increases and 15 (34.1%) protein decreases. These proteins are
listed in Table 2. We then reevaluated these changes after
Table 1. Patient characteristics and CPB parameters
description of
amount of
in prime
in prime
Hb, 5 min
of on-pump
weaning off
6 8.6 0.39 F 2 Tetralogy of
Fallot repair
94 78 32 10.2/30 25 0 550 6.2 84 (2) 215 250 4.25 18 65
19.3 7.7 0.39 F 2 Atrial septal
defect repair
66 41 34 9.2/27 0 0 700 6.3 52 (2) 300 250 4 5 22
12 4.4 0.26 F 2 Hemi-Fontan 126 30 28 10.2/30 0 0 500 6 54 (1) 200 250 2.2 13 110
29.8 10.5 0.49 M 3 Completion of
92 0 32 8.2/24 30 0 400 400 250 0 1 97
5.6 4.9 0.28 F 2 Atrial and/or
septal defect
88 68 32 10.2/30 200 0 500 4.8 76 (2) 150 250 2.5 5 20
3.6 4.7 0.28 F 2 Hemi-Fontan 118 60 32 9.5/28 40 0 400 6 58 (2) 350 350 2.5 14 91
59.1 15 0.64 F 3 Extracardiac
181 74 31 8.8/26 60 400 700 7.3 200 (3) 500 200 7.5 10 99
30.8 11 0.51 F 2 Subaortic
165 86 32 9.2/27 40 200 500 6.2 226 (3) 150 250 5.5 5 70
11.3 8.6 0.41 F 2 Ventricular
septal defect
209 165 32 10.2/30 85 400 850 6.2 342 (7) 150 250 4.5 5 46
4.3 4.2 0.25 F 3 Atrioventricular
canal and
tetralogy of
Fallot repair
128 97 28 11.2/33 43 200 500 6.4 84 (4) 150 250 2.1 12 52
All patients were given 50 mL of 25% albumin, 1000 units of heparin, and 15 mEq of NaHCO
as part of the priming solution. No platelets were given after bypass.
RACHS (8).
BSA, body surface area; Hb, Hemoglobin; Hct, hematocrit; PRBC, packed red blood cells.
applying the Bonferroni correction for multiple comparisons,
and the number of significant proteins (p 0.001) was
reduced to 17 (38.6%) with 8 (18.2%) increases and 9 (20.4%)
decreases. These proteins are listed in the upper part of Table
2 and designated by “*.” A complete listing of all proteins
identified by 2D-DIGE, their values, and percent change can
be found in Table S3 (http://links.lww.com/PDR/A59).
MAP analysis. MAP analysis was performed by Rules Based
Medicine using the HumanMAP version 1.6 Antigen panel,
which detected a total of 90 proteins. A list of proteins, including
NCBI and Swiss-Prot accession numbers, and PANTHER as-
signments for all proteins measured by the MAP assay, can be
found in Table S4 (http://links.lww.com/PDR/A59). As with
the 2D-DIGE study, we first compared the time points by
repeated measurements. Of these 90 proteins, 49 (54.4%) were
shown to change significantly (p 0.05 by repeated measure-
ments) between the time points with 26 (28.9%) increases and
23 (25.5%) decreases. These proteins are listed in Table 3. We
then applied the Bonferroni adjustment, and the number of
significant changes (p 0.00056) was reduced to 21 (23.3%)
with 16 (17.8%) increases and 5 (5.5%) decreases. These
proteins are listed in the upper part of Table 3 and designated
by “*.” A complete listing of all proteins quantified by the
MAP assay, their values, and percent change can be found in
Table S4 (http://links.lww.com/PDR/A59).
Combined analysis of 2D-DIGE and MAP proteins. Of the
129 proteins identified and quantified in this study, five were
quantified by both methods, and the percent change for pro-
teins detected by both platforms was similar, although not
identical (Tables S3 and S4: http://links.lww.com/PDR/A59).
Proteins undergoing significant changes between time points
with 50% change are shown in Figure 2.
We used the PANTHER database to gain insight into the
functional implications of the changes in protein expression
after CPB. For this analysis, only proteins with significant
changes were entered into PANTHER to determine their
potential impact on molecular functions and biologic pro-
cesses. These are depicted in Figure 3 with proteins separated
into those that increased (Fig. 3A and C) and those that
decreased (Fig. 3B and D). Note that PANTHER may attribute
many functions and/or processes to a given protein. Many of
the proteins with significant changes in expression are cate-
gorized by the molecular function of “signaling molecule
(SIGN),” “select regulatory molecule (SRM),” and “defense/
immunity protein (DIP);” with biologic processes of “signal
transduction (ST),” “protein metabolism and modification
(PMM),” and “immunity and defense (ID),” respectively. The
molecular functions (Fig. 3A and B) and biologic processes
(Fig 3C and D) attributed to the proteins by the PANTHER
database are shown in pie charts for all plasma proteins
with significant changes in expression between time
points. The data in Tables 2 and 3 and Table S3 and S4
(http://links.lww.com/PDR/A59) reveal that most of the pro-
teins with the greatest changes in expression (200%) are
included in the above-mentioned PANTHER categories, indi-
cating that these categories are not only the ones with the most
significant protein changes but that they also contain the
proteins with the largest changes in expression.
We then performed an Ingenuity Pathways analysis on the
combined list of all significantly changing proteins identified
by 2D-DIGE and MAPs. This analysis demonstrated that the
canonical “acute phase response signaling” pathway was the
most highly regulated (p 6.75E-27) of the pathways incor-
porating these set of proteins (Fig. 4). Patterns of expression
for the identified proteins closely match those predicted by the
acute phase response pathway and suggest that interleukin
(IL)-6 may be an important mediator in the up-regulation of
many of the proteins, possibly acting through the JAK2/
STAT3 pathway.
Because of the potentially serious sequelae that SIRS can
cause, there have been many studies attempting to identify
biomarkers that could be used to predict, diagnose, and mon-
itor its clinical course. SIRS may occur in a wide variety of
conditions, so defining subgroups could enhance the develop-
ment of treatment options (11,12). A number of biomarkers
for SIRS have been used in the past including, but not limited
to, IL-6, procalcitonin, and C-reactive protein (CRP) (13–15).
These three proteins showed the greatest changes in the
Figure 1. Reference gel image of depleted plasma proteins identified by
2D-DIGE. All identified proteins are circled and numbered. Note that all gel
spots within each circle were independently identified. 1, Afamin; 2, alpha-
1-antichymotrypsin; 3, alpha-1-B-glycoprotein; 4, alpha-2-antiplasmin; 5,
alpha2-HS glycoprotein (fetuin-A); 6, angiotensinogen; 7, antithrombin (an-
tithrombin III); 8, apolipoprotein A-I
; 9, apolipoprotein A-IV; 10, apoli
poprotein E; 11, apolipoprotein J (clusterin); 12, apolipoprotein-H (beta-2-
; 13, ceruloplasmin; 14, CFI protein (complement factor I);
15, coagulation factor II (thrombin); 16, coagulation factor XIIIb; 17,
complement component 1; 18, complement component 6; 19, complement
component C4; 20, complement factor B; 21, complement factor H; 22,
complex-forming glycoprotein HC (AMBP); 23, EPC-1 (PEDF) (pigment
epithelium-derived factor); 24, Fibrinogen gamma chain
; 25, gelsolin;
26, hemopexin; 27, inter-alpha (globulin) inhibitor H2; 28, inter-alpha-trypsin
inhibitor H4; 29, keratin 9; 30, keratin 10; 31, kininogen 1 isoform 1; 32,
kininogen 1 isoform 2; 33, LBP precursor; 34, plasma retinol-binding protein
(retinol-binding protein 4); 35, plasminogen (angiostatin); 36, serum amyloid
P component
; 37, serum paraoxonase; 38, SHBG (sex hormone-binding
; 39, solute carrier family 25, member 42; 40, tetranectin (TN); 41,
transferrin (serotransferrin); 42, vitamin D binding protein (group-specific
component); 43, vitronectin; 44, zinc-alpha-2-glycoprotein. Proteins that were
also identified by MAP analysis (M).
current study (Fig. 2; 800 –2300%). However, recently, the
value and specificity of some of these has been questioned
(16,17). It is conceivable that there may be other more specific
biomarkers or combinations of biomarkers, which may be
more useful as predictors and monitors of SIRS. Moreover,
these could be useful in terms of dividing patients with SIRS
into subgroups. The analysis of a data pattern instead of
several individual parameters has been shown to be advanta-
geous for individualized predictions on postoperative recovery
in cardiac surgery (18). The use of multiple parameters to
assess statistical significance by data pattern analysis has also
been helpful in a human model for individual risk assessment
(19). Studies have successfully used multivariate logistic re-
gression to combine data from sets of individual biomarkers to
form a single-value composite index (20), along with algo-
rithms and decision tree models (21) to accurately diagnose
disease. It is also important to note that the limited size of this
pilot study may have prevented some changes from reaching
statistical significance, but it would be premature to eliminate
these proteins from CPB studies at this time.
The dual-platform proteomics approach chosen here, which
coupled 2D-DIGE and MALDI-ToF/ToF with multianalyte
immunoassays, allowed us to identify and/or quantify a com-
bined total of 129 different proteins between the two plat-
forms: 44 by 2D-DIGE and 90 by MAPs, with five proteins
being identified by both (Tables 2 and 3) and only required the
use of 150
L of plasma. This total excludes the 14 most
abundant plasma proteins, which we immunodepleted from
our samples before 2D-DIGE. Note that the dual approach
permits quantification of known (but very rare) protein analyte
species using the MAP platform, simultaneously illuminating
novel (but more abundant) proteins using the 2D-DIGE assay.
It is likely that future 2D-DIGE studies will expand our list of
identified proteins. Some of the proteins identified in this
study, taken individually or together, may serve as more
reliable potential biomarkers for predicting and assessing
SIRS and other CPB-related complications. They could also
provide additional insight into the mechanisms responsible for
some of these complications. We eventually plan to incorpo-
rate these biomarkers into state-of-the-art microfluidic devices
that can be used for real time measurement of the protein
biomarkers of systemic inflammation (22). This would permit
study of the time course of systemic inflammation, thereby
enabling the development of treatment modalities to reduce or
eliminate systemic inflammation and/or minimize its sequelae,
thereby reducing the risks of CPB.
To enhance our understanding of the potential impact of the
proteomic changes we observed with CPB, we focused on the
70 proteins that exhibited significant changes in expression
using the PANTHER gene ontology database. Ontological
analysis indicated altered expression of proteins from three
major categories for molecular function (SIGN, SRM, and
DIP) included 65% (46 of 71) of the significantly altered
proteins and three major categories for biologic processes (ST,
PMM, and ID) included 79% (56 of 71; Fig. 3). It is interest-
ing to note that many of the proteins falling into these highly
represented categories were also the proteins that showed the
greatest changes in levels of expression.
Table 2. 2D-DIGE proteins with significant changes
Gel no. Protein ID Percent change Baseline 24 hr Post-CPB
2 Alpha-1-antichymotrypsin* 110.5 4.45 (0.777) 9.36 (0.892)
5 Alpha2-HS glycoprotein (fetuin-A)* 24.5 11.05 (2.405) 8.88 (1.909)
6 Angiotensinogen* 29.7 2.65 (0.445) 3.44 (0.553)
14 CFI protein* 17.0 6.69 (1.408) 5.72 (0.863)
15 Coagulation factor II (thrombin)* 10.3 1.06 (0.071) 0.96 (0.051)
16 Coagulation factor XIIIb* 23.5 4.26 (0.491) 3.45 (0.418)
18 Complement component 6* 10.1 0.92 (0.140) 1.02 (0.125)
26 Hemopexin* 21.0 26.71 (4.866) 22.07 (3.731)
27 Inter-alpha (globulin) inhibitor H2* 24.7 1.06 (0.228) 0.85 (0.126)
31 Kininogen 1 isoform 1* 21.0 5.32 (0.597) 4.40 (0.412)
33 LBP precursor* 132.3 0.62 (0.160) 1.45 (0.258)
36 Serum amyloid P component*† 71.4 5.86 (1.801) 10.04 (2.117)
37 Serum paraoxonase* 65.1 3.15 (0.911) 5.20 (1.297)
38 SHBG*† 57.2 1.26 (0.374) 0.80 (0.247)
40 TN* 34.0 1.21 (0.184) 0.90 (0.129)
43 Vitronectin* 79.1 2.92 (0.396) 5.23 (0.743)
44 Zinc-alpha-2-glycoprotein* 38.9 2.72 (1.332) 3.78 (1.066)
4 Alpha-2-antiplasmin 17.1 2.12 (0.400) 1.81 (0.210)
9 Apolipoprotein A-IV 27.2 9.75 (3.238) 7.66 (2.503)
10 Apolipoprotein E 28.4 9.78 (2.045) 12.55 (2.439)
11 Apolipoprotein J (clusterin) 24.1 25.93 (3.564) 20.89 (4.544)
12 Apolipoprotein-H (beta-2-glycoprotein-I)† 8.3 8.84 (1.344) 9.57 (0.852)
13 Ceruloplasmin 7.1 13.45 (2.254) 12.56 (2.281)
25 Gelsolin 29.0 5.46 (1.386) 4.23 (0.784)
All proteins quantified by 2D-DIGE and undergoing significant changes are listed. Values at each time point are means (SD) of the normalized volumes for
proteins determined to be significant (p 0.05) by repeated measures analysis of natural logarithms for each group (n 10/group). For proteins with multiple
isoforms, the normalized volumes of all isoforms were added together. The percent change between time points is shown.
* Proteins that remain significantly different after the Bonferroni adjustment (p 0.001) are listed in the upper portion of the table.
† Proteins also quantified by MAP. A complete listing of all of the proteins identified by 2D-DIGE can be found in Table S3 (http://links.lww.com/PDR/A59).
CFI, complement factor I; SHBG, sex hormone-binding globulin; TN, tetranectin.
Further analysis with Ingenuity Pathways indicated that
many of the protein changes after CPB are related to the acute
phase response signaling pathway (p 6.755E-27; Fig. 4).
This included both positive and negative acute phase ele-
ments. Given the known association of CPB with inflamma-
tion and SIRS, this was expected. IL-6, which increases by
more than 10-fold, is at the proximal end of the cascade and
apparently affects the regulation of a number of the proteins
identified in this study through the JAK2/STAT3 pathway,
including CRP and lipopolysaccharide binding protein (LBP).
A number of other canonical pathways were also implicated
by the Ingenuity program on the basis of identified proteins.
Among these were the coagulation system, the complement
system, and cytokine-mediated signaling and glucocorticoid
Table 3. MAP proteins with significant changes
Protein ID Percent change Baseline 24 hr Post-CPB
Adiponectin (
g/mL)* 43.5 10.41 (5.149) 7.25 (2.349)
Alpha-1 antitrypsin (mg/mL)* 49.3 1.66 (0.497) 2.49 (0.490)
Apolipoprotein-H (beta-2-glycoprotein-I) (
g/mL)*† 27.0 112.23 (22.07) 142.51 (33.45)
Calcitonin (pg/mL)* 2865.6 5.82 (4.071) 172.66 (354.2)
CD40 ligand (ng/mL)* 69.6 0.06 (0.012) 0.03 (0.011)
Creatine kinase-MB (ng/mL)* 735.5 0.75 (0.259) 6.27 (3.699)
g/mL)* 800.3 2.30 (6.017) 20.74 (9.514)
EN-RAGE (S100 calcium binding protein A12) (ng/mL)* 655.9 2.71 (3.024) 20.52 (12.46)
Ferritin (ng/mL)* 408.8 26.51 (22.55) 134.87 (81.21)
G-CSF (pg/mL)* 442.4 6.71 (14.41) 36.41 (39.70)
IL-16 (lymphocyte chemoattractant factor) (pg/mL)* 67.3 491.80 (262.5) 822.70 (297.6)
Insulin (
IU/mL)* 689.8 0.40 (0.343) 3.17 (1.965)
MDC (CCL22) (pg/mL)* 81.2 687.20 (189.4) 379.20 (168.0)
MMP-9 (matrix metalloproteinase 9) (gelatinase B) (ng/mL)* 240.3 158.84 (160.9) 540.50 (275.3)
Myeloperoxidase (ng/mL)* 87.0 126.22 (80.64) 236.00 (53.62)
Serum amyloid P component (
g/mL)*† 96.2 5.67 (3.696) 11.13 (3.937)
SHBG (nmol/mL)*† 37.1 72.72 (19.55) 53.04 (14.21)
TIMP-1 (ng/mL)* 122.3 63.30 (13.32) 140.72 (26.55)
TNF RII (TNF receptor superfamily member 14) (ng/mL)* 30.0 3.51 (1.906) 4.56 (1.680)
Thyroid stimulating hormone (alpha; beta)* (
IU/mL) 418.5 6.70 (2.892) 1.29 (1.574)
von Willebrand factor (
g/mL)* 100.7 17.69 (8.133) 35.50 (10.64)
Alpha-2 macroglobulin (mg/mL) 10.7 0.79 (0.074) 0.72 (0.040)
Brain-derived neurotrophic factor (ng/mL) 628.3 4.74 (4.052) 0.65 (0.562)
ENA-78 (ng/mL) 430.5 1.79 (1.016) 0.34 (0.364)
Eotaxin (CCL11) (pg/mL) 83.2 243.30 (69.30) 132.78 (70.03)
Fatty acid binding protein (ng/mL) 273.0 2.14 (2.576) 7.99 (7.807)
Factor VII (coagulation factor VII) (ng/mL) 26.2 370.64 (140.3) 293.60 (112.2)
Fibrinogen (alpha chain, beta chain, gamma chain†) (mg/mL) 21.6 2.37 (0.858) 2.89 (0.538)
Haptoglobin (mg/mL) 257.3 0.54 (0.550) 0.15 (0.200)
IFN-gamma (pg/mL) 584.0 19.12 (16.17) 2.80 (4.144)
IgE (ng/mL) 148.2 6.62 (5.835) 16.43 (16.78)
IgM (mg/mL) 32.3 0.36 (0.191) 0.48 (0.115)
IL-2 (pg/mL) 67.0 30.17 (12.82) 18.07 (5.477)
IL-5 (CSF, eosinophil) (pg/mL) 56.3 8.93 (1.924) 5.71 (2.604)
IL-6 (interferon, beta 2) (pg/mL) 1075.9 2.50 (4.769) 29.35 (24.89)
IL-8 (pg/mL) 61.0 30.18 (6.667) 48.60 (13.69)
IL-13 (pg/mL) 20.1 84.31 (18.47) 70.21 (12.02)
IL-15 (ng/mL) 41.0 0.99 (0.628) 0.70 (0.482)
Leptin (ng/mL) 178.7 1.87 (2.707) 5.22 (6.522)
Lipoprotein (a) (
g/mL) 147.0 17.00 (12.88) 41.97 (36.89)
MCP-1 (CCL2) (pg/mL) 63.1 212.40 (122.9) 130.24 (44.36)
MIP-1beta (CCL4) (pg/mL) 40.5 216.20 (63.79) 153.90 (50.44)
MMP-2 (gelatinase A) (
g/mL) 42.5 3.31 (0.962) 2.32 (0.759)
Prostate specific antigen, free (Kallikrein-related peptidase 3) (ng/mL) 50.9 0.04 (0.021) 0.06 (0.017)
RANTES (CCL5) (ng/mL) 527.5 21.64 (16.59) 3.45 (3.475)
Stem cell factor (KIT ligand) (pg/mL) 38.5 392.00 (116.9) 283.10 (85.03)
TNF-alpha (pg/mL) 55.5 8.92 (4.891) 5.74 (1.933)
Thrombopoietin (ng/mL) 75.9 3.31 (0.984) 1.88 (0.909)
VEGF (pg/mL) 23.4 769.10 (218.5) 948.90 (173.3)
All proteins quantified by MAP and undergoing significant changes are listed. Values are means (SD) of the concentrations for proteins determined to be
significant (p 0.05) by repeated measures analysis of natural logarithms for each group (n 10/group).
* Proteins that remain significantly different after the Bonferroni adjustment (p 0.00056) are listed in the upper portion of the table.
† Proteins were also quantified by 2D-DIGE. A complete listing of all of the proteins identified by MAP can be found in Table S4 (http://links.lww.com/PDR/A59).
G-CSF, colony stimulating factor (granulocyte); MDC, macrophage-derived chemokine; SHBG, sex hormone-binding globulin; TIMP, tissue inhibitor of
metalloproteinases; ENA-78, epithelial-derived neutrophil activating protein 78; IFN, interferon; Ig, immunoglobulin; MCP, monocyte chemoattractant protein;
MIP, macrophage inflammatory protein; MMP, matrix metalloproteinase; TNF, tumor necrosis factor; VEGF, vascular endothelial growth factor A.
receptor signaling, although there were fewer significant
changes in protein expression in the proteins associated with
these pathways.
Study limitations. Although all of these 10 patients fit our
inclusion criteria in terms of risk stratification, body weight, and
age, the duration of CPB and cross-clamp varied patient to
patient. This is the limitation of our study. Another limitation of
this study is that we compared only two samples (baseline versus
24 h post-CPB); therefore, we may miss the early peak of several
proteins. Because of the limitation of blood samples taken from
Figure 2. Histogram of significant proteins
with greater than 50% change in expression. All
proteins changed significantly and underwent at
least a 50% change. Proteins that remain signif-
icantly different after the Bonferroni adjustment
are indicated by “*.”
Figure 3. Pie charts of PANTHER molecular function and biologic process. The molecular functions (A, B) and biologic processes (C, D) assigned to the
combined list of significant proteins identified by 2D-DIGE and MAP are shown, along with the percentage that each group constitutes. Only the proteins
determined to be significant by repeated measurements analysis are included and are separated by increases (baseline to 24 h post-CPB) (A, C) and decreases
(B, D). A complete list of the proteins with their functions can be found in Tables S3 and S4 (http://links.lww.com/PDR/A59).
pediatric patients, in particular from neonates and infants, we
were able to take only a limited number of samples. Several
previous studies (including ours) have showed significant
changes in levels of biomarkers during CPB and at 1, 3, 6, 12,
and 18 h post-CPB. On the basis of our past experience, usually
most of the “classical” biomarkers return to normal levels after
24 h post-CPB. This was one of the reasons to collect the last
sample at 24 h post-CPB. However, we have discovered that 70
proteins remained significantly changed after 24 h post-CPB
even though the sample size was small. In addition, this study is
a pilot study and will not have statistical power to correlate the
levels of biomarkers and tissue damage. Rather, this is a study
using a novel approach that combines two discovery proteomics
techniques to identify a pattern of potential biomarkers using
only 150
L of plasma.
This study uses a novel approach that combines two pow-
erful proteomics techniques to identify patterns of potential
biomarkers that change after CPB. The identification and
quantification of these biomarkers using only 150
Figure 4. Acute phase signaling pathway. Ingenuity pathways analysis of a combined list of all 129 proteins identified by 2D-DIGE and MAP. The canonical
“acute phase response signaling” pathway was determined to be the most highly regulated (p 6.75E-27) pathway by this set of proteins. Proteins with significant
increases in expression are shown in red, proteins with significant decreases are shown in green, and proteins that were identified but did not change significantly
are shaded in gray.
plasma may provide additional insight into the mechanisms
responsible for CPB-related complications, particularly sys-
temic inflammation, and enable preventative measures or
treatments to be used during and after CPB procedures to help
reduce the systemic effects of CPB and improve the outcomes
of pediatric CPB patients.
Acknowledgments. We thank research nurses (Sarah Stur-
gis, RN, MS, Jennifer Stokes, RN, Heidi Watts, RN, Jessica
Beiler, MPH, and Amyee McMonagle, RN) from the Pediatric
Clinical Research Office of the Department of Pediatrics for
the collection of blood samples.
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    • "These procedures have been described in detail previously [29,30,123]. Information about the 2D-DIGE study is provided in a form that complies with the most recent version <http://www.psidev.info/miape/MIAPE_GE_1_4.pdf> of Minimum Information About a Proteomics Experiment – Gel Electrophoresis (MIAPE-GE) standards currently under development by the Human Proteome Organization Proteomics Standards Initiative (see Additional file 6). "
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    • "We have used this procedure in previous studies for other types of protein samples [26,46,143] but a detailed account, including many modifications and refinements appears below. For identification of spots, all 791 protein spots from the experiment were picked from the picking gel using a robot-directed spot picker (Ettan Spot Picker, GE Healthcare). "
    [Show abstract] [Hide abstract] ABSTRACT: Mice lacking surfactant protein-A (SP-A-/-; knockout; KO) exhibit increased vulnerability to infection and injury. Although many bronchoalveolar lavage (BAL) protein differences between KO and wild-type (WT) are rapidly reversed in KO after infection, their clinical course is still compromised. We studied the impact of SP-A on the alveolar macrophage (AM) proteome under basal conditions. Male SP-A KO mice were SP-A-treated (5 micrograms/mouse) and sacrificed in 6 or 18 hr. The AM proteomes of KO, SP-A-treated KO, and WT mice were studied by 2D-DIGE coupled with MALDI-ToF/ToF and AM actin distribution was examined by phalloidon staining. We observed: a) significant differences from KO in WT or exogenous SP-A-treated in 45 of 76 identified proteins (both increases and decreases). These included actin-related/cytoskeletal proteins (involved in motility, phagocytosis, endocytosis), proteins of intracellular signaling, cell differentiation/regulation, regulation of inflammation, protease/chaperone function, and proteins related to Nrf2-mediated oxidative stress response pathway; b) SP-A-induced changes causing the AM proteome of the KO to resemble that of WT; and c) that SP-A treatment altered cell size and F-actin distribution. These differences are likely to enhance AM function. The observations show for the first time that acute in vivo SP-A treatment of KO mice, under basal or unstimulated conditions, affects the expression of multiple AM proteins, alters F-actin distribution, and can restore much of the WT phenotype. We postulate that the SP-A-mediated expression profile of the AM places it in a state of "readiness" to successfully conduct its innate immune functions and ensure lung health.
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