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Review Article
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Plasma Proteomics Analysis of Dairy Cows with Milk Fever Using SELDI-TOF-MS
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Shi Shu,
Cheng Xia,
Hongyou Zhang,
Zhaolei Sun,
Jiannan Liu
and
Bo Wang
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ABSTRACT
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Milk fever is an important metabolic disorder of dairy cows at calving and
is characterized by hypocalcemia during the transition period. The aim of this
study is to investigate novel changes in the plasma proteomics of cows with
milk fever. Surface-enhanced laser desorption/ionization time-of-flight mass
spectrometry (SELDI-TOF-MS) was used for many field as a novel proteomics teachnology.
So far, the plasma proteomics of milk fever has not been investigated using
SELDI-TOF-MS. Plasma samples were obtained from twenty-one Holstein cows with
milk fever (T) and fifty-nine Holstein cows without milk fever (C) at a dairy
farm in Heilongjiang, China. Twenty-four differential peptide peaks in the plasma
of T and C cows were isolated by SELDI-TOF-MS. Ten of these peaks were identified
using the Swissport Protein Database. The peptide peaks represented ten unique
proteins and showed significant alterations in their peaks as determined by
analysis using the Wilcoxon Rank Sum Test. The four up-regulated proteins were
identified as complement c3 frag, hepcidin, amyloid bata a4 protein, serum albumin
frag and fibrinogen. Complement c3 frag and hepcidin regulate the inflammatory
response. Amyloid beta a4 protein is involved in Alzheimers
disease. Serum albumin frag acts as a transport protein. Fibrinogen beta chain
participates in blood coagulation. The two down-regulated proteins were plasma
protease c1 inhibitor frag and apolipoprotein a-2 which are associated with,
respectively, blood coagulation and cardiovascular disease. The four proteins
that were both up-regulated and down-regulated were fibrinogen alpha chain frag,
neurosecretory protein vgf frag, serun amyloid a protein and cystatin-c. Based
on SELDI-TOF-MS, identify novel plasma proteins that may be closely associates
with milk fever in cows. These findings may reveal previously unidentified metabolic
changes in cows with milk fever.
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Received: July 22, 2013;
Accepted: January 18, 2014;
Published: March 11, 2014
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INTRODUCTION
Milk fever (MF) is a metabolic disease that occurs at calving or during the
transition period, especially in high-producing dairy cows (Horst
et al., 1997). MF is associated with many important diseases, such
as metritis, ketosis, displaced abomasums andretained placenta. The disease
is characterized by hypocalcaemia; this is a consequence of fetal development
and the formation of colostrum during the transition period which result in
considerable loss of blood calcium (Ca). Levels of parathyroid hormone and 1,
25-dihydroxyvitamin D3 (DHVD) increase as a result of the reduction of plasma
Ca concentration (DeGaris and Lean, 2008). Previous
studies suggest that many biochemical factors, such as phosphorous, magnesium,
alkaline phosphatase, hydroxyproline, osteocalcin and calcitionin, have a close
relationship with plasma Ca concentration (Horst et al.,
1997; DeGaris and Lean, 2008).
Surface enhanced laser desorption/ionization time of flight mass spectrometry
(SELDI-TOF-MS) is a novel technology of proteomic analysis which involves protein-chip
bond mass spectrometry. It can be used to search for peptides and/or proteins
on the surface of the protein chip that have undergone chemical modification
and it allows analysis of complex biological samples (Yuan
et al., 2012). SELDI-TOF-MS detects the protein spots on different
chromatographic surfaces and maintains their physicochemical properties (Lehmann
et al., 2005). Thus, it may eliminate the requirement for pre-purification
and the proteins can be purified easily in buffered salt or detergents. For
these reasons, SELDI-TOF-MS is easy and simple to perform, rapid and is able
to screen smaller peptides than 2-differential in-gel electrophoresis (2D-DIGE)
(Grus et al., 2005). In recent years, many fields
have research diseases of humans and rats by SELDI-TOF-MS. For example breast
cancer (Li et al., 2005), severe acute respiratory
syndrome (Yip et al., 2005) and in the discovery
of drugs (Ilyin et al., 2005).
The pathogenesis of MF has been reported in relation to physiology, biochemistry
and pathology in previous studies. However, there have been no reports about
the plasma proteome of cows with MF investigated using SELDI-TOF-MS. Therefore,
the aim of this study was to explore the plasma proteomic changes in cows with
MF using SELDI-TOF-MS, to provide new information on the pathogenesis of MF.
MATERIALS AND METHODS
Experimental animals: All animals were selected from an intensive dairy
farm in accordance with the requirements of the Veterinary Medical Ethical Committee
of the Local Agricultural Department (Mishan, Heilongjiang, China). Twenty-one
Holstein cows were as assigned to the MF group (T, Ca<1.40 mmol L-1
and obviously clinical signs, such as depression, recumbency, unsciourseness,
etc.,) and fifty -nine Holstein cows to the control group (C, Ca>2.50 mmol
L-1 and no clinical signs) (DeGaris and Lean,
2008). Table 1 shows the age, parity and plasma Ca concentration
of the two groups of cows. The difference were very significant between two
groups for age, parity and plasma Ca concentration (p<0.01). All the cows
were fed a total mixed ration (TMR) at prepartum which consisted of 8.5 kg concentrated
feed, 18.5 kg silage maize, 4 kg hay and 350 g fat. The nutritional analysis
was 55.60% DM (dry matter), 16% crude protein, 1.75 mcal DM-1 NEL(net
energy for lactation), 5.60% fat, 39.10% NDF(neutral detergent fiber), 20.30%
ADF(acid detergent fiber), 180 g Ca and 116 g P.
Blood parameter analysis: All blood samples (10 mL) from MF and C groups
were collected by the caudal vein within 6 h after calving. Heparin (150 IU)
was added to each sample according to the International Guiding Principles for
Biomedical Research Involving Animals.
Table 1: |
Age, parity and plasma Ca concentration of tested cows (Mean±SD) |
 |
T: Milk fever group, C: Control group, **represents very significant
difference between two groups (p<0.01) |
The samples were centrifuged immediately at 3,000 rpm for 10 min, then frozen
in liquid nitrogen and stored at -80°C until subsequent analyses. The plasma
Ca concentration was detected using an automatic biochemical analyzer (Modular
DPP, Roche, Germany) using a commercial kit (651564-01, Roche, Germany).
SELDI-TOF-MS
Sample preparation: After thawing and centrifugation (10,000 rpm for 5
min) at 4°C, 10 μL of each serum sample was added to 10 μL U9
(5 mL 9 M urea, 2% CHAPS(3-[(3-cholesterol aminopropyl) dimethylammonio]-1-propane
sulfonic acid), 50 mM Tris-HCl, pH 9.0. Each sample was mixed by gently knocking
the bottom of the test tube with a finger. The samples were shaken for 30 min
in an ice-bath and gently knocked with the finger to mix them every 5 min. Subsequently,
180 μL NaAC (sodium acetate) (50 mM, pH 4.0) was added to 20 μL of
the treated sample, to give a final dilution of the serum sample of 20. The
sample was applied to a spot on the chip after mixing, avoiding the formation
of bubbles.
Chip processing: Each spot was washed with 10 mM HCL (5 μL) for
5 min. After removal of the acid, 5 μL HPLC (High-performance liquid chromatography)
water was added and the sample shaken at 250 rpm for 5 min. This procedure was
repeated once. The CM10 (weak cation exchanger array) ProteinChip was put into
a Bioprocessor; 100 μL elution buffer (50 mM NaAC, pH 4.0) was put into
each well and incubated at room temperature for 5 min with vigorous shaking
at 250 rpm. This procedure was repeated once. After rejecting the buffer, 100
μL of sample was added immediately to every spot and incubated for 60 min
with vigorous shaking at 250 rpm. After the content had been removed from each
spot, the spot was washed with buffer (50 mM NaAC, pH 4.0) for 5min at room
temperature with vigorous shaking at 250 rpm. This procedure was repeated once.
Each spot was washed with 100 μL HPLC water that was removed after 5 min
with vigorous shaking at 250 rpm. After air drying for 10-15 min, 1 μL
EAM (energy absorbing molelule) solution was applied to each spot.
Experimental apparatus: SELDI-TOF-MS PBSIIC (BIORAD, USA) and Protein
software were used to read the chips and analyze the data. The following settings
were used: Laser intensity 240, detector sensitivity 8, voltage 20000 v, vacuum
8.387e-007 Torr, peaks detected from 1000-50000 Da and collection 112 times
per spot.
Statistical analysis: In this study many tools were used for statistical
analysis, including Ciphergen ProteinChip Software (Version 3.1.1), the Swissport
protein database, MATLAB 2007b software, Hierarchical cluster using gene cluster
3.0 and tree view cluster software and rweka from bioconductor. One-way analysis
of variance was used for analysis the age, parity and plasma Ca concentration
of two groups of cows. The value of peaks was shown in table by the Ciphergen
ProteinChip software (Version 3.1.1). The Wilcoxon rank sum test was used for
assess the differences in the peaks between the T and C groups according to
P values.
RESULTS
Detection using SELDI-TOF-MS and predicted proteins: The raw data were
got from 81 peaks by Ciphergen ProteinChip software (Version 3.1.1) between
the group of MF and control. The threshold of the p value was 0.01 and the differential
peaks were determined with a p value <0.01. Thirty-seven differential peaks
were selected (Fig. 1a). Figure 1b, c
show the most significant difference peak (m/z:3898.65) which have the minimum
P value, via Trace view and Gel view.
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Fig. 1(a-c): |
Detection using SELDI-TOF-MS and predicted proteins. The differential
peaks between milk fever (T) and C) control (groups). On the right of the
(a) Group I is the test (T) with milk fever, and Group III is control (C).
The abscissa is m/z and the ordinate is the peak value (b) The abscissa
is m/z and the ordinate is the peak value (b) is enlarged from (a) for the
m/z 3868 (amyloid beta a4 protein). The top is the test (T) with milk fever,
and the bottom is control (C) and (c) The abscissa is m/z in Gel view. The
top is the test (T) with milk fever, and the bottom is control (C). The
dark color indicates high content and the light color indicates low content |
The true mass to charge ratio (m/z) of the thirty-seven differential peaks
and the theoretical m/z of polypeptides from the Swissport protein database,
were used to predict the most similar protein for each differential peak.
Table 2: |
Status of the up-regulated proteins in the T group |
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T: Milk fever group, C: Control group |
Table 3: |
Status of the down-regulated proteins in the T group |
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T: Milk fever group, C: control group |
Table 4: |
Status of the up-regulated and down-regulated proteins in
the T group |
 |
T: Milk fever group, C: Control group |
From these results, twenty-four differential peaks were predicted, of which
ten were identified. In the T group four proteins were up-regulated in Table
2, two proteins were down-regulated in Table 3 and four
proteins were both up-regulated and down-regulated in Table 4.
The P value of all peaks was under 0.01.
The p values were compared between the two groups and the m/z ratios of the
top five peaks were 2126.23, 2959.68, 3416.05, 3898.65 and 3912.85, respectively,
as shown by the boxplot in Fig. 2.
PCA analysis: Principal Component Analysis (PCA), used to detect compounds
with significant differences, can be used to show the relationships among many
samples by compressing the characteristics of the samples in low-dimensional
space.
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Fig. 2: |
Detection using SELDI-TOF-MS and predicted proteins. The p-values
were compared between the two groups and the m/z ratios of the top five
peaks respectively, as shown by the boxplot. Pink box color shows the test
(T) group; gray box color shows the control (C). The abscissa is m/z and
the ordinate is the peak value. The boxplot displays differences in intensity
of 2128.23, 2959.85, 3416.05, 3898.65 and 3912.85 m/z respectively between
the T and C groups |
In this study, the data for twenty-four differential peaks were converted
mathematically to PC1 (principal component 1) and PC2 (principal component 2)
and then demonstrated by score plot using MATLAB 2007b software. The result
indicated that the T and C groups were able to be differentiated clearly in
Fig. 3.
Cluster analysis: The relationship of all samples was shown by mode
of Hierarchical cluster using Gene cluster 3.0 and tree view cluster software.
Figure 4 displayed major of samples in T group lied in left
side and of C group lied right. Results suggested that the peaks had significant
difference between two groups. However, there were some overlap between two
groups and it may be relative to samples difference from same group.
Decision tree analysis: The decision tree analysis was completed using
Rweka from Bioconductor.
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Fig. 3: |
Principal component analysis (PCA). Group I is the test (T)
with milk fever. Group III is control (C). PC1 is principal component 1
and PC2 is principal component 2. The different groups could be distinguished
clearly by PC1 and PC2. The result indicated that the T and C groups were
able to be differentiated clearly PCA plot |
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Fig. 4: |
Cluster analysis of samples. Group I is test (T) with milk
fever. Group III is control (C). Each horizontal line is a kind of compound
and each vertical line is a sample. Red indicates high content of sample
and green indicates low content of sample. Results of Cluster analysis suggested
that the peaks had significant difference between two groups |
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Fig. 5: |
Decision tree analysis of samples. Results of gecision tree
modeling allocate the samples to different groups; Group I is test (T) with
milk fever. Group III is control (C). The node is labeled sequentially and
shows splitting criteria. In this figure M2959_85 = 4.108 would mean that
subjects with peak intensities of = 4.108 at an m/z ratio of 2959.86 Da
would move down the left side and all other subjects would move down the
right side |
In this study, only one protein peak (2959.85) was used for discriminate cows
with MF from healthy cows (Fig. 5) according to decision tree
modeling to allocate the samples to different groups; the model had 90.5% sensitivity,
93.2% specificity and 92.5% accuracy.
DISCUSSION
The result of study was shown for the first time the different proteins that
are expressed in the plasma of cows with MF, using SELDI-TOF-MS which combines
protein-chip analysis and TOF-MS. A result of high sensitivity and adaptability
was obtained quickly and easily (Grus et al., 2005).
In this study, four proteins were up-regulated, two proteins were down-regulated
and four proteins were both up-regulated and down-regulated in the T group.
The four up-regulated proteins were complement c3 frag (C3), amyloid beta a4
protein (Aβ), Serum Albumin Frag (SALB) and Hepcidin (HC). C3 is secreted
by activated macrophages and adipocytes at sites of inflammation and has a central
role in the immune system (Goralski and Sinal, 2007;
Zarkadis et al., 2001). In the activated complement
system C3 also plays an important role. Activation of complement involves three
pathways; each pathway has a distinct mechanism, but all three pathways require
activated C3 (Onat et al., 2011; Pangburn
et al., 2008). Aβ is a transmembrane protein that is produced
by amyloid precursor protein (APP) on the cell membrane (Zetterberg
et al., 2010) and it has the structure of a cell-membrane receptor-like
protein (Mattson, 2004). Aβ is an important risk
factor for Alzheimers disease (AD) since its polymeric product forms a
neurotoxin by deposition on the cell matrix which may result in AD (Walsh
and Selkoe, 2007; Hampel et al., 2010). HC,
an 84-amino acid protein, is an important regulatory protein produced by the
liver. It can inhibit erythrocyte function by iron loading and inflammation,
leading to anemia, anoxia and increased inflammatory response (Coyne,
2011; Young and Zaritsky, 2009). SALB is a single
peptide chain with 580-585 amino acids which is the most common protein produced
by the liver in the blood. It has many biological functions, including maintenance
of osmotic pressure, transport of molecules such as hormones, fat-soluble vitamins,
free fatty acids and drugs, binding of calcium ions (Ca2+) and buffering
pH (Peters, 1975). The up-regulation of C3 and hepcidin
found in this study may be associated with the development of inflammation during
MF because the complement system is activated by the C3 pathway and high levels
of hepcidin can enhance the inflammatory response. This may explain why affected
cows easily succumb to infectious diseases, such as mastitis or metritis. Some
studies have reported that the susceptibility of immunocytes to stimulation
may decrease in cows with MF, leading to the inflammatory responses involved
in endometritis and mastitis (Kimura et al., 2006).
However, the relationship between milk fever and hepcidin still is unclear.
In addition, the up-regulated Aβ which produces a neurotoxin leading to
AD in humans, may be involved in the signs of depression and paralysis seen
in cows with MF. Likewise, the relationship between Aβ and milk fever needs
further confirmation. Furthermore, there has been no report about the relationship
of serum album with MF and more evidence is required.
The two down-regulated proteins were plasma protease c1 inhibitor frag (C1INH)
and apolipoprotein a-2 (ApoA-II). C1INH is a heavily glycosylated single chain
polypeptide with a molecular weight of ~405 kD which is an important inhibitor
of the inflammatory response by means of bonding with many proteases (Jackson
et al., 1989; Parikh and Riedl, 2011). In
the complement system, it plays an important regulatory role and prevents overactivation
of the complement cascade (Emonts et al., 2007).
It can promote blood coagulation because C1INH can stimulate the synthesis of
kallikrein and blood coagulation factor XII which play an important role in
blood coagulation (Matsushita et al., 2000;
Cai et al., 2005). ApoA-II is the second major
protein component of High-density Lipoprotein (HDL), accounting for about 20%
of HDL protein. Recent studies have shown that it can protect against Cardiovascular
Disease (CVD) by transporting various proteins (Blanco-Vaca
et al., 2001; Birjmohun et al., 2007).
However, the function of ApoA-II is not totally clear (Winkler
et al., 2008). The down-regulation of C1INH may be an important factor
in the inflammatory process of cows with MF because of the inhibitory role of
this protein in the inflammatory response. The down-regulation of ApoA-II may
be related to the tachycardia and arrhythmia seen in cows with MF, because of
its role in prevention of CVD. Nevertheless, further confirmation of the relationship
of both these proteins to MF is required.
An interesting phenomenon noted in this studies was that four proteins were
both up-regulated and down-regulated in the T group. They were fibrinogen alpha
chain Frag (FG), neurosecretory protein Vgf Fra (VGF), Serun Amyloid a protein
(SAA) and Cystatin-c (CYS-C). FG is a plasma glycoprotein that plays an important
role in the inflammatory response and blood coagulation. It is widespread in
the tissues during injury and inflammation (ODonovan
et al., 2012; Flick et al., 2004). During
Ca2+-dependent blood coagulation, it is converted into fibrin. Therefore,
in this study the up-regulation of FG was possibly related to the inflammatory
response of cows with MF because FG has an inhibitory function in inflammation.
Furthermore, the down-regulation of FG may also be related to blood coagulation
because the conversion of fibrinogen into fibrin is accompanied by massive consumption
of Ca2+. This process may be a reason for the decreased Ca2+
concentration in cows with MF. VGF, a 617-amino acid protein, has the characteristics
of a neuropeptide precursor. It is a response gene for neurotrophic factor and
is regulated by Nerve Growth Factor (NGF) in PC12 cells (Canu
et al., 1997; Riedl et al., 2009).
It is widely distributed in the nervous system and exists selectively in neurons
and other nervous tissue (Salton et al., 2000).
VGF is up-regulated during injury and inflammation of nerves (Riedl
et al., 2009) and down-regulated in patients with AD, Amyotrophic
Lateral Sclerosis (ALS) and frontolateral dementia (Selle
et al., 2005; Carrette et al., 2003).
Therefore, the up-regulation of VGF may play a role in the inflammatory response
of cows with MF. In addition, the down-regulation of VGF, like Aβ, may
play a role in the regulation of nerve function related to the signs of depression
shown by cows with milk fever. However, this hypothesis requires further research.
In humans, SAA, a 12-14 kDa protein, is one of a group of apolipoproteins in
HDL that are precursors to the amyloid A protein found in amyloidosis. Its levels
can increase 1,000-fold in the 24-36 h after injury or infection and returns
to normal levels in 10-14 days (Bahk et al., 2010).
It can promote the catabolism of HDL, reduce the level of esterified serum cholesterol
and change the distribution of HDL subpopulations (Salazar
et al., 2001). Thus, in this study the up-regulation of SAA may be
related to the presence of secondary infections during the development of MF.
The down-regulated SAA has the same role as ApoA-II. CYS-C is a 13 kDa protein
which is a member of the cysteine inhibitor family, produced by karyocytes.
It is widely distributed in body fluids and is an effective inhibitor of cathepsin
(Comnick and Ishani, 2011; Lafarge
et al., 2010). It can also inhibit antigen processing and presentation,
thus reducing the immune response. It can increase filtration in the glomerulus,
showing complete reabsorption and catabolism, so that the plasma CYS-C concentration
is considered to represent the Glomerular Filtration Rate (GFR) (Taglieri
et al., 2009). Thus, the immune function of cows with MF usually
decreases which may have a close association with the up-regulation of CYS-C.
However, this finding also needs further confirmation. In general, the four
proteins which were both up-regulated and down-regulated in affected cows in
this study are possibly associated with the different physiological and pathological
pathways involved in MF. This suggests that these proteins may play dual roles
in the development of MF and are worthy of further research attention in the
future.
Finally, the ten differential proteins identified in this study were not able
to establish a new model for diagnosis according to the decision tree analysis.
However, the compound with m/z of 2959.85 may be considered as a diagnostic
biomarker to establish a new model for the diagnosis of MF, due to its high
accuracy (92.5%), sensitivity (90.5%) and specificity (93.2%) (Fig.
5). However, it was not identified by the SELDI-TOF-MS analysis. Therefore,
it will be necessary to investigate this compound further using other methods.
CONCLUSION
In summary, this study is the first to explore the plasma proteomics of cows
with MF, using SELDI-TOF-MS and to identify successfully ten differential proteins.
The results suggest that these ten proteins are altered when cows develop MF.
This study may contribute to researcher understanding of the relationship between
MF and these ten proteins.
ACKNOWLEDGMENTS
This study was supported by the National Science and Technology Foundation
of China (30972235) and Heilongjiang Province Nature Science and Technology
Foundation of China (C200916). Thanks to Shanghai Sensichip infotech Co., Ltd.,
Shanghai, 200433, China for the SELDI-TOF-MS analysis and European International
Science Editing for manuscript revision.
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