Biology Reference
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was validated in a larger study. Our group identified and validated a panel
of proteins using an antibody microarray. Imanguli et al.
[17]
employed
complementary proteomic techniques (e.g., SELDI-TOF-MS, 2D-DIGE,
and ELISA) to investigate the salivary proteome of 41 patients undergo-
ing allo-HSCT. SELDI-TOF-MS was first applied to determine a pattern of
differential salivary peptide expression in pre- and post-transplantation
samples. A set of 30 peptides correlated with a diagnosis of aGVHD. Using
2D-DIGE, they next identified 13 spots that differed between pre- and post-
transplantation samples. Four of these spots were analyzed by MALDI-
TOF-MS/MS and identified as lactoferrin, cystatin, albumin, and salivary
amylase. In addition, the concentrations of four selected proteins [i.e., lac-
toferrin, secretory IgA, β2-microglobulin, and secretory leukocyte protease
inhibitor (SLPI)] were determined by ELISA, and the results suggested that
compared to pretransplantation levels, lactoferrin and SLPI levels increased
1 month after allo-HSCT, while secretory IgA concentrations decreased. The
concentration of β2-microglobulin was found to be significantly higher 6
months after transplantation compared to pretransplantation samples,
suggesting that elevated levels of this protein precede the chronic form of
GVHD. Hori et al.
[61]
used the SELDI technique to screen for plasma pro-
teins specific for aGVHD in a mouse model. One peak appeared to distin-
guish GVHD plasma from non-GVHD plasma and was selected for further
analysis. The peak was identified as CCL8. The concentration of human
CCL8 was subsequently quantified in the sera of 7 patients with aGVHD and
7 patients without aGVHD using ELISA. Elevated levels of CCL8 were signifi-
cantly increased in patients with aGVHD and correlated with aGVHD sever-
ity. Srinivasan et al.
[16]
used SELDI-TOF-MS and found an aGVHD-specific
peptide pattern in training samples that was then validated in an indepen-
dent set of aGVHD samples obtained on the day of onset of aGVHD symp-
toms. With this GVHD-specific pattern, they were able to distinguish aGVHD
samples from non-GVHD samples with 100% sensitivity and 100% specific-
ity
[16]
. Kaiser et al.
[24]
employed CE-MS to identify peptide patterns in
urine samples as early indicators of aGVHD development. For each sample,
more than 1000 different peptide sets were examined. Levels of 16 specific
peptide sets differed by more than 50% in aGVHD samples compared with
controls. Two prominent GVHD-indicative polypeptides were identified as a
1.85-kDa peptide from the leukotriene A4 hydrolase and a 1.83-kDa peptide
from albumin. In a subsequent study, the sample preparation protocol was
modified to achieve higher data reproducibility by excluding the largest and
most abundant molecules in urine from the analysis
[70]
. This modification
resulted in an improved aGVHD-specific peptide set pattern and was sub-
sequently used to screen 63 samples collected from 33 patients after allo-
HSCT. A pattern of 31 peptides distinguished samples at the time of aGVHD
from control samples with a sensitivity of 100% and a specificity of 98%. A
subsequent blind evaluation of 599 samples from 141 patients enabled the
prediction of aGVHD before clinical symptoms presented, with a sensitivity
of 83.1% and a specificity of 75.6%. Using MS/MS, 3 of the 31 peptides that
contributed to the aGVHD pattern were identified as fragments from colla-
gen α1 chain I (downregulated) and α1 chain III (upregulated).
464
The levels of individual blood proteins represent a summation of mul-
tiple, disparate events that occur in every organ system. In this regard, a
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