Skip to main content
Download PDF

Identification of potential protein biomarkers for early detection of pregnancy in cow urine using 2D DIGE and label free quantitation

Clinical Proteomics volume 13, Article number: 15 (2016) Cite this article

Abstract

Background

An early, reliable and noninvasive method of early pregnancy diagnosis is prerequisite for efficient reproductive management in dairy industry. The early detection of pregnancy also help in to reduce the calving interval and rebreeding time which is beneficial for industries as well as farmers. The aim of this work is to identify potential biomarker for pregnancy detection at earlier stages (16–25 days). To achieve this goal we performed DIGE and LFQ for identification of protein which has significant differential expression during pregnancy.

Results

DIGE experiment revealed a total of eleven differentially expressed proteins out of which nine were up regulated having fold change ≥1.5 in all time points. The LFQ data analysis revealed 195 differentially expressed proteins (DEPs) out of 28 proteins were up-regulated and 40 down regulated having significant fold change ≥1.5 and ≤0.6 respectively. Bioinformatics analysis of DEPs showed that a majority of proteins were involved in regulation of leukocyte immunity, endopeptidase inhibitor activity, regulation of peptidase activity and polysaccharide binding.

Conclusion

This is first report on differentially expressed protein during various time points of pregnancy in cow to our best knowledge. In our work, we identified few proteins such MBP, SERPIN, IGF which were differentially expressed and actively involved in various activities related to pregnancy such as embryo implantation, establishment and maintenance of pregnancy. Due to their involvement in these events, these can be considered as biomarker for pregnancy but further validation of is required.

Background

An early and precise pregnancy diagnosis is an important criterion for better reproductive management in livestock like cows and buffaloes. Currently different methods (direct and indirect) are in use for diagnosis of pregnancy. The direct methods include per rectal palpation and ultrasonography. However, their application is limited in terms of accurate detection by day 45th and 30th day using per rectal palpation and ultrasonography respectively [1, 2]. Additionally, the expertise of experienced veterinarian is required for confirmed pregnancy diagnosis. The indirect methods include immunological based assay for detection and quantitation of target proteins (Pregnancy Associated Glycoprotein: PAG) and hormones such as progesterone (P4), pregnadiol, interferon tau related to pregnancy [3, 4]. However, these methods have inherent limitations of specificity and false positive results in ELISA. Worldwide, different research groups have used urine as a non-invasive source for detection of pregnancy and various other diseases in human being. Pregnancy diagnosis (PD) in dairy animals has remained elusive till date. In fact, dairy animals (cow, buffalo, sheep and goat) although domesticated since time immemorial offer inherent challenges in the understanding of their anatomy, physiology and behavior. Pregnancy in human being is currently detected by presence of human chorionic gonadotropin (HCG) in urine. However, this hormone is absent in bovine urine. Therefore, till date early pregnancy detection in bovine has not been possible [5–7]. After conception, numerous biomolecules such as steroids, prostaglandins and proteins are expressed during early pregnancy [8]. Many of these hormones and proteins are of fetal-placental origin rather than of maternal origin [9]. They are required for the successful establishment of pregnancy and the proliferation of normal and neoplastic cells. Early pregnancy factor (EPF) is one protein which has been observed in the serum of cows during early pregnancy. However, EPF is not confined to pregnancy specifically but also detected in the serum of patients and different animals bearing a variety of tumors [10].

The increased expression of PAG has also been reported in serum and milk during pregnancy in bovine. PAGs are expressed specifically in the maternal and embryonic regions of placenta and belong to aspartic protease family. Different isoforms of PAGs have been reported in bovine during various stages of gestation. The presence of this protein after 28th day post AI serves as an indicator of pregnancy [11]. However, this protein has inherent limitation because its maintenance of basal level of expression till 3 months after parturition. No other proteins till now have been suggested as suitable biomarker for early detection of pregnancy. Thus, although there have been many attempts to develop diagnostics for the detection of early pregnancy in cattle, no success has been achieved till date.

Progress in the field of protein separation and identification technologies has accelerated research into biofluids proteomics for protein biomarker discovery. Urine is considered as an ideal source of biological material for biomarker discovery as it is non-invasive in comparison to other body fluids [12]. Lack of reliable cow-side early pregnancy diagnosis method further aggravates the situation. Urine is an ideal and a rich source of biomarkers in proteomics to analyze the differential expression of urinary proteins in various altered physiological conditions such as pregnancy and different diseases [13] in livestock. The advancement of molecular techniques such as proteomics and their applications in animal research has given a new hope to look for pregnancy biomarkers. In the present investigation, we have identified and analyzed differentially expressed proteins urine of pregnant and non-pregnant cattle on different days of pregnancy using DIGE and Label Free Quantitation (LFQ).

Methods

Animal selection and sampling

Karan Fries (KF) heifers from the dairy herd of National Dairy Research Institute, Karnal, India were maintained under expert veterinary supervision. For the present investigation, one litre urine was collected from individual animal (n = 6) in urine bags on different days of pregnancy (0, 16, 22 and 35 days). Day 0 represents the control (collection of urine before artificial insemination: AI). Following AI, urine was collected the cows till the 60th day of pregnancy. Immediately after urine collection, phenylmethylsulfonyl fluoride (PMSF, 0.01 %) was added to prevent proteolytic degradation.

Confirmation of pregnancy using transrectal-ultrasonography

The transrectal-ultrasonography (Aloka Prosound, Switzerland) was done on the 30th day after breeding and repeated after 45 days post breeding for confirmation. The scanning of the uterus and ovaries was done using a 6.5 MHz rectal linear probe (Aloka UST-5820-5, Switzerland). Pregnancy diagnosis was confirmed by observation of embryocoele and allantoic fluid [14]. The ovaries were scanned for the presence of corpus luteum also.

Sample preparation

Insoluble material in urine was removed by centrifugation at 6000 rpm for 30 min, followed by diafiltration with phosphate buffer saline (PBS, pH 7.5) (133 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4 and 2 mM KH2PO4) [12, 15]. The diafiltered urine was concentrated up to 100 ml by using 3 kDa hollow fibre cartridge in Marlow Benchtop System (GE healthcare, USA). Protease inhibitor cocktail (Sigma, USA) was added to the concentrated urine to prevent proteolysis and stored at −80 °C till further use.

Protein precipitation

Protein precipitation from the concentrated urine was performed by Proteo Spin Maxi Kit (Norgen Biotek, USA) following manufacturer’s instructions. Briefly, the pH of the urine sample was adjusted to 3.5 by adding a binding buffer. The Proteo Spin column was activated by adding 5 ml of the column activation and wash buffer and centrifuged for 3 min at 1000×g. The flow through was discarded and same step was repeated twice and 20 ml of the pH adjusted urine was loaded onto the column and centrifuged for 5 min at 1000×g. The column was again washed by applying column activation and wash buffer and centrifuged for 3 min at 1000×g. Protein was eluted with elution buffer (10 mM Na2HPO4, pH 12.5) in a fresh collection tube containing the neutralizer. The eluted proteins were concentrated and preserved at −80 °C until further analysis [16].

Clean up

Interfering substances such as salts, detergents, nucleic acid, etc. were removed from the precipitated urinary proteins using 2-D clean-Up kit (GE healthcare, USA) and the resulting pellet were rehydrated in lysis buffer (7 M Urea, 2 M Thiourea, 4 % CHAPS, 30 mM Tris). Protein concentration was estimated using 2-D Quant kit (GE Healthcare, USA) as per manufacturer’s instructions with bovine serum albumin as a standard.

1D SDS-PAGE

The individual proteins were precipitated and analysed by (10 × 10.5 cm) SDS-PAGE with 4 % stacking and 12 % resolving gel using MiniVE gel electrophoresis apparatus (GE healthcare, USA). The gels were stained with Coomassie Brilliant Blue G 250 (Bio-Rad Laboratories, USA) for 1 h and destained.

Sample labelling with fluorescent dyes

The sample pH was adjusted to 8.5 by 100 mM NaOH. An equal amount of proteins was pooled (n = 6) separately to make a final amount of 15 µg for each day of sample i.e. 0, 16, 22 and 35 days, the protein samples were labelled with 200 pmol Cy3 (non-pregnant) and Cy5 (pregnant) respectively. Internal standard (pooled sample, 7.5 µg each) was labelled with 200 pmol Cy2 dye. Dye swapping was done to avoid dye biasness by labelling with 200 pmol Cy5 (non-pregnant) and Cy3 (pregnant) respectively. The whole labelling procedure was performed on ice, after labelling samples are incubated in dark for 30 min. Subsequently, 1 µl of 10 mM lysine was added to quench the reaction. The samples were incubated for 10 min on ice in dark and mixed as per the experimental design (Table 1). The final sample volume was made 125 µl for each strip, by adding De Streak rehydration buffer (GE Healthcare). Six IPG (7 cm, pH 4–7, GE Healthcare) were rehydrated by passive rehydration with labelled sample for 16 h at room temperature following the protocol described by Jena et al [17].

Table 1 Experimental design for DIGE experiment (0, 16, 22 and 35 day of pregnancy)
Full size table

2D GE and image scanning

Isoelectric focusing (IEF) was performed with the parameters 150 V for 1 h 20 min (step), 300 V for 20 min (grad), 5000 V for 1 h 40 min (grad), 5000 V for 25 min (step) with a total of 7000 Vh. Thereafter, strips were equilibrated with an equilibration buffer (6 M Urea, 50 mM Tris pH 8.8, 2 % SDS, 30 % Glycerol and 0.02 % Bromophenol Blue) containing 1 % DTT for 15 min (reduction) and followed by an equilibration buffer containing 2.5 % iodoacetamide for another 15 min (alkylation). SDS-PAGE of 6 gels was performed in MiniVE (GE healthcare, USA) electrophoresis system (10 × 10.5 cm) with 12 % resolving gel. After electrophoresis, gels were scanned with typhoon Trio+ variable mode imager (GE Healthcare) by using the parameters followed earlier with minor modifications [17, 18]. Briefly, the gels were scanned with 100 µm resolution and normal sensitivity. Cy2 images were scanned with 575 nm (blue) laser and 520 BP40 emission filter, Cy3 images were scanned with 515 nm (green) laser and 580 BP30 emission filter and Cy5 images were scanned with 490 nm (red) laser and 670 BP30 emission filter.

Image analysis and spot picking

Scanned images were analyzed in the Decyder 2-D software (version 7.0, GE Healthcare) to identify expression of proteins. Estimated number of spots was set to 2000 and in individual gel spots were detected by Differential In-Gel analysis (DIA). All images from 6 different gels were matched through Biological Variation Analysis (BVA) which provides statistical data for differentially expressed proteins (above 1.5 fold, p ≤ 0.05) between three experimental groups. A total of 11 differentially expressed protein spots were identified.

Preparative gel and spot digestion

A preparative gel having 320 µg pooled (n = 6) proteins from various days of pregnant animals (0, 16, 22 and 35 days) was carried out using the same parameters used for DIGE as mentioned above and stained with Coomassie Brilliant Blue (R-350) followed by destaining. Selected spots were picked from preparative gel and transferred into 1.5 ml Eppendorf tubes, spots were washed with Milli-Q water and 40 mM NH4HCO3 in 50 % ACN (1:1) and for rehydration 100 µl of 100 % ACN were added to each tube and incubated for 10 min, ACN was carefully discarded and for reduction 10 mM DTT in 40 mM NH4HCO3 buffer was added and incubated for 15 min, then alkylation was done in 55 mM iodoacetamide in 40 mM NH4HCO3 buffer. Spots were washed and rehydrated. For tryptic digestion spots were covered with trypsin solution (12.5 ng/µl in 50 mM NH4HCO3) for 45 min in ice. Trypsin digestion was performed overnight at 37 °C and stopped by adding 5 % formic acid. The extracted peptides were dried in a Speed-Vac and desalted by using Ziptip (Millipore, USA) and identified by Nano-LC-MS/MS.

In- solution digestion

For in-solution digestion, 20 μg of pooled samples (n = 6) from non-pregnant and pregnant cows (0, 16, 22 and 35 days) were collected on different days of pregnancy was processed. In solution digestion method was performed as reported earlier with slight modification [16]. In brief, 45 mM DTT in 50 mM NH4HCO3 was used to reduce disulfide bonds followed by alkylation of cysteine residues using 10 mM IAA in 50 mM NH4HCO3. Digestion was carried out overnight using trypsin (1:20) (modified sequencing grade; Promega, USA) at 37 °C. The reaction was subsequently stopped with 10 % TFA, peptides were vacuum dried, desalted by zip tip and stored at −80 °C.

LC-MS/MS and data analysis for label free quantitation (LFQ)

The digested peptides were reconstituted in 0.1 % formic acid in LC/MS grade water and subjected to nano-LC (Nano-Advance, Bruker, Germany) followed by identification in captive spray-Maxis-HD qTOF (Bruker, Germany) mass spectrometer (MS) with high mass accuracy and sensitivity. The peptides were enriched by nano trap column (Bruker Magic C18AQ, particle size-5 μm, pore size-200 Å) and separated on an analytical column (Bruker Magic C18AQ, 0.1 × 150 mm, 3 μm particle size, and 200 Å pore size) at flow rate 800 nl/min and eluted using a linear gradient of 5−45 % acetonitrile over 135 min. The MS/MS scan was carried out at m/z range of 400–1400 followed in data dependant mode. For each cycle, the six most intense precursor ions from survey scan were selected for MS/MS [16]. The identification and quantitation was done using MS/MS spectra.

Data processing and bioinformatics analysis

MS data were analyzed using MaxQuant [19] software version 1.5.0.8 and searched with UniProt Bos taurus and Bubalus bubalis database along with common contamination sequences. Database search was performed in MaxQuant environment integrated with Andromeda. For searching, the enzyme specificity was set to trypsin with the maximum number of two missed cleavages. The precursor mass tolerance was assigned to 0.07 Da for the first search and 0.006 Da for the main search. Mass tolerance for matching peaks to theoretical ion series was set to 40 ppm. The false discovery rate (FDR) for PSM, protein, and site decoy fraction was set to 1 %. The search included variable modifications of protein N-terminal acetylation, methionine oxidation, and carbamidomethylation of cysteines was searched as a fixed modification. The maximal number of modifications per peptide was set to be 6. The minimum peptide length of 6 was set, and the ‘peptide re-quantification’ function was enabled. To validate and transfer identifications across different runs, the ‘match between runs’ option in MaxQuant was enabled with a retention time window of 0.7 min and an alignment time window of 20 min. Subsequent bioinformatics analysis were performed using Protein Analysis Through Evolutionary Relationships (PANTHER) to compare the GOBP, GOCC, GOMF and GOPC. The obtained PANTHER [20] data was further analyzed and graphs were prepared using MS Excel 2007. The mass spectrometry proteomics data have been deposited to the ProteomeXchange consortium with the PRIDE partner repository with the database identifier PXD004122.

Result and discussion

Urine is considered to be the best source of biological material for diagnosis of altered physiological and various patho-physiological conditions due to its non-invasive nature and collection in large volume [12]. It is a well known fact that pregnancy affects the protein expression in maternal serum and urine. Furthermore, the quantitative difference in protein expression during pregnancy is useful for the detection of biomarkers related to pregnancy. In the present investigation, we have used gel based (DIGE) and non-gel based approaches (LFQ) to identify differentially expressed proteins during early pregnancy in cattle (Fig. 1). The present study aimed to identify protein biomarkers which can possibly be used for detection of pregnancy at an earlier stage (16–25 days) in cow urine samples which will be beneficial for dairy farmers.

Fig. 1
figure 1

Workflow of the DIGE and LFQ for identification of differential expressed proteins during various time points of pregnancy

Full size image

Identification of differentially expressed proteins (DEP) using DIGE

We used DIGE approach to identify the differentially expressed proteins during different days of pregnancy, such as days 0 (non-pregnant control), 16, 22 and 35 post breeding. A representative image of the DIGE gel in the present investigation is shown in Fig. 2a, b. Additional figures of all DIGE gels are shown in Additional file 1: Figure S1. After analysis of the DIGE gel in Decyder software, we observed a total of 11 differentially expressed proteins (DEPs) having fold change of ±1.5 (p ≤ 0.05). Out of 11 DEPs, 9 proteins were up-regulated (Table 2). We have discussed functional relevance of few selected proteins namely Alpha 2HS Glycoprotein (A2HS), AMBP, Renin, Mannan-binding protein which may have role in pregnancy associated events. Alpha-2-HS (Heremans-Schmid) glycoprotein also known as Fetuin-A is a phosphoprotein which is mainly expressed in the liver, the tongue, and placenta in humans [21]. It is expressed in higher concentrations in serum and amniotic fluid during fetal life and is also involved in development-associated regulation of calcium metabolism and osteogenesis. The increased expression of this protein has been reported during pregnancy in women [13]. Interestingly, we observed secretion of this protein in the urine of pregnant cows during early pregnancy. The renin–angiotensin system (RAS) is mainly associated with the regulation of blood pressure and ion homeostasis. Angiotensin II (Ang II) which is generated due to the proteolytic action of rennin has been reported to influence oviductal gamete movements and foetal development. The pre-implanted embryo responds to Ang II from mothers rather than from embryos. It has been suggested that maternal RAS influences blastocyst hatching and early embryonic development [22]. Alpha-2 Macroglobulin (AMBP) is a protease inhibitor and has been reported to prevent excessive trophoblastic invasion. AMBP reportedly influences trophoblast invasion in human pregnancy, which would be reflected in its increased production in the decidua basalis [23]. We also observed up-regulation of Mannan-binding protein (MBP) in our experiment. MBP is a mannan-binding lectin which is secreted into the amniotic fluid and its functional activity is mediated through the formation of mannose-binding lectin and mannose-binding lectin-associated serine protease 2 complexes (MBL-MASP2 complex). This complex is actively involved in mannose-binding lectin complement pathway resulting in antibody-independent recognition and clearance of pathogen in the amniotic cavity during pregnancy [24, 25]. Increased secretion of MBP in urine during early pregnancy suggests its possible application as a potential biomarker.

Fig. 2
figure 2

a Images of DIGE gels scanned using Typhoon Scanner. b Image of preparative gel (320 µg protein on 7 cm IPG strip having pI-4-7 and 12 % separating gel) used for picking differential expressed proteins

Full size image
Table 2 List of differentially expressed proteins
Full size table

Identification of differentially expressed proteins by LFQ

Analysis of the LFQ results using Maxquant software revealed 195 (Additional file 2: Table S1) differentially expressed proteins, out of which 28 proteins were up-regulated and 40 proteins were down-regulated having fold change ≥1.5 and ≤0.6 respectively which were considered for further analysis (Tables 3, 4; Fig. 3). The analysis revealed some important proteins which play a role in pregnancy-associated events such as embryo implantation, establishment, and maintenance of pregnancy. Expression of important proteins such as Hormone-binding globulin, Haptoglobin, SerpinB3 like, Uromodulin, Cathelicidin, Mannan-binding protein, uteroglobin, Vitamin-binding protein and Insulin-like growth factor-binding protein II (IGFBP-II) increased significantly during the early days of pregnancy (16–22 days). Uterine serpins are produced by uterine endometrium and regulate the immune function or participate in the trans-placental transport. Expression of Serpin was decreased on day 10 but subsequently increased on day 16 [26]. Another study revealed that there is increased expression of serpin in the endometrium of pregnant cows compared to cyclic heifers during pregnancy recognition period (16–18 days) [27].

Table 3 LFQ (max quant) list of up regulated proteins
Full size table
Table 4 List of down regulated proteins
Full size table
Fig. 3
figure 3

Bar graph of differentially expressed proteins having fold change ≥1.5 and ≤0.6 revealed by Max quant Software for LFQ data

Full size image

The success of pregnancy is dependent on the uterine environment which is mediated by different hormones and growth regulators. Insulin-like growth factors are expressed in embryo and reproductive tract of cow and sheep. They are reportedly involved in blastocyst formation, implantation and embryo growth [28, 29]. We observed up-regulation of IGFBP-II during early pregnancy. IGFBPs bind IGFs with high affinity, regulating the availability of free IGFs. Higher expression of IGFBP-II during early pregnancy suggests that it binds to IGF-II for its optimal bioavailability to embryos during implantation and embryo growth. Haptoglobin is a glycoprotein expressed in uterine epithelium during implantation period [30]. We observed increased expression of this protein during early pregnancy the present study. We also observed increased expression of the vitamin D-binding protein in the urine during early pregnancy. Vitamin D-binding protein belongs to albumin family of proteins and is present in plasma, cerebrospinal and ascitic fluids and on cell surface of many cell types. This protein binds to various plasma metabolites and transport to their targeted sites. Higher expression of Vitamin D-binding protein has been reported in uterus and placenta of bovine during pregnancy [31]. It has been reported that vitamin D-binding protein is also involved in active transport of Ca+ which is crucial for the fetal developmental events such as bone mineralization, neuro muscular activities and blood coagulation. The up-regulation of Vitamin D-binding protein in urine during early pregnancy suggests its potential as a biomarker for early detection of pregnancy in cattle. We also observed up regulation of MBP which correlates well with our DIGE data. Expression of uromodulin was also upregulated during early pregnancy in urine which is in agreement with the observation reported earlier [32]. We also identified many proteins during early pregnancy (Table 3) which may be playing important role in pregnancy associated events such as transfer of embryo from fallopian tube, hatching of blastocyst, maintenance and implantation of embryo and fetal development.

Functional classification of protein

The functional characterization of identified proteins (195 proteins) was based on Gene Ontology (GO) using PANTHER 8.0 bioinformatics software platform, which generated information regarding cellular localization, metabolic and biological process. The classification based on cellular component (Fig. 4a) revealed that the majority of proteins are present at extracellular region (54 %), followed by extracellular complex (17 %), cytoplasmic (13 %), organellar fraction (8 %), membrane and macromolecular complex (4 %). Classification based on molecular function (Fig. 4b) showed that a large majority of the proteins are involved in binding (32 %), catalytic activity (28 %), enzyme regulator activity (15 %), transporter activity (5 %), structural molecule activity (4 %) and translation regulator activity (1 %). On the basis of biological processes (Fig. 4c), the proteins were classified into those involved in metabolism and cellular processes (18 %), biological regulation (12 %), response to stimulus (11 %), immune system processes (10 %), biological adhesion, localization and developmental process (8 %), multicellular organismal process biogenesis, reproduction and apoptotic process (1 %).

Fig. 4
figure 4

Gene Ontology classification of proteins on the basis of their involvement in a cellular component, b molecular function, c biological process using PANTHER 7.0 software

Full size image

Network generation and visualization

To create protein–protein interaction network for identified urinary proteins, offline software tool Cytoscape was used along with plug-in ClueGO. The annotations network of ClueGO provides the biological significance of identified differentially expressed 195 bovine urinary proteins. ClueGO initially generates a binary gene-term matrix with the particular terms and their associated partner genes. The generated network shows the proteins as nodes that are linked through edges. During the search, the majority of the proteins were clustered into pathways (Fig. 5). From these results, four discrete pathways were recognized comprising regulation often do peptidase inhibitor activity, complement coagulation cascades, polysaccharide-binding positive regulation of peptidyl-tyrosine phosphorylation and protein kinase B signalling cascade. Regulation of these events is associated with various immunological functions. This protects the system from systemic infection and employs a number of strategies for the recognition and clearance by the host immune system [33]. Pregnancy is an event when a foreign body starts growing in the womb of pregnant mother and the system reacts to the foreign body by activation of complement C pathway and induction of endopeptidases. Concomitantly, a set of endogenous protease inhibitors are also expressed in the system which may possibly protect the embryo and young foetus from the proteolytic assault and immune rejection. A large number of peptidase inhibitors e.g. AGT, AHSG, AMBP, C3, COL6A3, GAS6, KNG1, LOC784932, PAPLN, SERPINA1, SERPINF2 were identified which are involved in controlling the activity of various serine and cysteine-type endopeptidase. These protease inhibitors possibly maintains the immune system from proteolytic insult.

Fig. 5
figure 5

Network construction for protein–protein interaction study was done using Cytoscape software with ClueGO plug-in

Full size image

Conclusion

Although we have identified a good number of differentially expressed proteins, further validation is required to authenticate their suitability as potential biomarkers for early detection of pregnancy. The validation with advancement in high throughput mass spectrometry targeted proteomics approach is an ideal method to validate these potential biomarkers which will be part of another study. To the best of our knowledge, the present investigation reports gel based (DIGE) and non-gel based (LFQ) differential proteome profiling in pregnant vis-a-vis non-pregnant Karan Fries cows for the first time. It provides us important information on differentially expressed urinary proteins during early pregnancy which possibly encourages the research community and dairy industry for development of urine based pregnancy diagnostic assay for early detection of pregnancy in cattle.

Abbreviations

DIGE:

difference gel electrophoresis

MS:

mass Spectrometry

LFQ:

label free quantitation

DEP:

differentially expressed proteins

MBP:

mannan binding protein

IGF:

insulin like growth factor

PAG:

pregnancy associated glycoprotein

HCG:

human chorionic gonadotropin

PD:

pregnancy diagnosis

EPF:

early pregnancy factor

AI:

artificial insemination

DIA:

differential In-Gel analysis

BVA:

biological Variation Analysis

GO:

gene Ontology

References

  1. Garcia A, Neary MK, Kelly GR, Pierson RA. Accuracy of ultrasonography in early pregnancy diagnosis in the ewe. Theriogenology. 1993;39:847–61.

    Article  CAS  PubMed  Google Scholar 

  2. Denecke B, Graber S, Schafer C, Heiss A, Wöltje M, Jahnen W. Tissue distribution and activity testing suggest a similar but not identical function of fetuin-B and fetuin-A. J Biol Chem. 2003;376(1):135–45.

    CAS  Google Scholar 

  3. Ranilla MJ, Sulon J, Carro MD, Mantecon AR, Beckers JF. Plasmatic profiles of pregnancy associated glycoprotein and progesterone level during gestation in Churra and Merino sheep. Theriogenology. 1947;42:537–45.

    Article  Google Scholar 

  4. Vandaele L, Verberckmoes S, El Amiri B, Sulon J, Duchateau L, Van Soom A, Beckers JF, de Kruif A. Use of homologous radioimmunoassay (RIA) to evaluate the effect of maternal and foetal parameters on pregnancy-associated glycoprotein (PAG) concentrations in sheep. Theriogenology. 2005;63:1914–24.

    Article  CAS  PubMed  Google Scholar 

  5. Shaked G, Shaked Y, Kariv-Inbal Z, Halimi M, Avraham I, Gabizon R. A protease-resistant prion protein isoform is present in urine of animals and humans affected with prion diseases. J Biol Chem. 2001;276:31479–82.

    Article  CAS  PubMed  Google Scholar 

  6. Pyo J, Hwang S, Oh J, Lee S, Kang S, Kim J, Lim J. Characterization of a bovine pregnancy-associated protein using two-dimensional gel electrophoresis, N-terminal sequencing and mass spectrometry. Proteomics. 2003;3(12):2420–7.

    Article  CAS  PubMed  Google Scholar 

  7. Simon L, Lamoureux L, Plews M, Stobart M, LeMaistre J, Ziegler U, Graham C, Czub S, Groschup M, Knox D. The identification of disease-induced biomarkers in the urine of BSE infected cattle. Proteome Sci. 2008;6:23.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Knickerbocker J, Thatcher W, Bazer F, Drost M, Barro D, Fincher K. Proteins secreted by Day-16 to -18 bovine conceptuses extend corpus luteum function in cows. Reproduction. 1986;77(2):381–91.

    Article  CAS  Google Scholar 

  9. Reis F, D’antona D, Petraglia F. Predictive value of hormone measurements in maternal and fetal complications of pregnancy. Endocr Rev. 2002;23(2):230–57.

    Article  CAS  PubMed  Google Scholar 

  10. Guang F, Qun Z, Guang F. A study of early pregnancy factor activity in preimplantation. Am J Reprod Immunol. 1997;37:359–64.

    Article  Google Scholar 

  11. Friedrich M, Holtz W. Establishment of an ELISA for measuring bovine pregnancy-associated glycoprotein in serum or milk and its application for early pregnancy detection. Reprod Domest Anim. 2010;45(1):142–6.

    Article  CAS  PubMed  Google Scholar 

  12. Adachi J, Kumar C, Zhang Y, Olsen VJ, Mann M. The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biol. 2006;7(9):R80.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zheng J, Liu L, Wang J, Jin Q. Urinary proteomic and non-prefractionation quantitative phosphoproteomic analysis during pregnancy and non-pregnancy. BMC Genom. 2013;14:777.

    Article  CAS  Google Scholar 

  14. Abdullah M, Mohanty TK, Kumaresan A, Mohanty AK, Madkar A, Baithalu R, Bhakat M. Early pregnancy diagnosis in dairy cattle: economic importance and accuracy of ultrasonography. Adv Anim Vet Sci. 2014;2(8):464–7.

    Article  Google Scholar 

  15. Marimuthu A, O’Meally R, Chaerkady R, Subbannayya Y, Nanjappa V, Kumar P, Kelkar D, Pinto S, Sharma R, Renuse S, Goel R, Christopher R, Delanghe B, Cole R, Harsha H, Pandey A. Comprehensive map of the human urinary proteome. J Proteome Res. 2011;10(6):2734–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bathla S, Rawat P, Baithalu R, Yadav ML, Naru J, Tiwari A, Kumar S, Balhara A, Singh S, Chaudhary S, Kumar R, Lotfan M, Behare P, Phulia S, Mohanty TK, Kaushik JK, Nallapeta S, Singh I, Ambatipudi S, Mohanty AK. Profiling of urinary proteins in Karan Fries cows reveals more than 1550 proteins. J Proteomics. 2015;127:193–201.

    Article  CAS  PubMed  Google Scholar 

  17. Jena M, Janjanam J, Naru J, Kumar S, Kumar S, Singh S, Mohapatra S, Kola S, Anand V, Jaswal S, Verma A, Malakar D, Dang AK, Kaushik JK, Reddy V, Mohanty AK. DIGE based proteome analysis of mammary gland tissue in water buffalo (Bubalus bubalis): lactating vis-a-vis heifer. J Proteomics. 2015;119:100–11.

    Article  CAS  PubMed  Google Scholar 

  18. Janjanam J, Singh S, Jena M, Varshney N, Kola S, Kumar S. Comparative 2D-DIGE proteomic analysis of bovine mammary epithelial cells during lactation reveals protein signatures for lactation persistency and milk yield. PLoS One. 2014;9(8):e102515.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–72.

    Article  CAS  PubMed  Google Scholar 

  20. Varaprasad K, Paul J, Suzette M, Sevgi T, Olav L, Mahesh C, Sinuhe H. Quantitative proteomics analysis of maternal plasma in down syndrome pregnancies. J Biomed Biotechnol. 2010. doi:10.1155/2010/952047 (Article ID 952047).

    Google Scholar 

  21. Dickson IR, Poole AR, Veis A. Localization of plasma alpha 2HS glycoprotein in mineralising human bone. Nature. 1975;256(5516):430–2.

    Article  CAS  PubMed  Google Scholar 

  22. Pijacka W, Hunter M, Pipkin F, Luck M. Expression of renin–angiotensin system components in the early bovine embryo. Endocr Connect. 2012;01:22–30.

    Article  Google Scholar 

  23. Siu S, Choy M, Leung T, Lau T. Lack of site-specific production of decidual alpha-2 macroglobulin in human pregnancy. J Soc Gynecol Investig. 2006;13:491–6.

    CAS  PubMed  Google Scholar 

  24. Malhotra R, Willis A, Bernal A, Thiel S, Sim R. Mannan-binding protein levels in human amniotic fluid during gestation and its interaction with collectin receptor from amnion cells. Immunology. 1994;82(3):439–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Csuka D, Molvarec A, Derzsy Z, Vaga L, Rigo J, Prohaszka Z. Functional analysis of mannose binding lectin complement pathway in normal pregnancy and preeclampsia. J Reprod Immuol. 2010;87(1–2):90–6.

    Article  CAS  Google Scholar 

  26. Forde N, Bazer F, Spencer T, Lonergan P. Proteomic analysis of uterine fluid during the pre-implantation period of pregnancy in cattle. Reprod Fertil. 2014;147(5):575–87.

    Article  CAS  Google Scholar 

  27. Forde N, Bazer F, Spencer T, Lonergan P. Conceptualizing the endometrium: identification of conceptus-derived proteins during early pregnancy in cattle. Bioreproduction. 2015;10:1095.

    Google Scholar 

  28. Ciftci H. The effect of insulin-like growth factor system on embryo growth and development. Int J Anim Vet Adv. 2011;3(5):291–9.

    CAS  Google Scholar 

  29. Gray CA, Taylor KM, Ramsey WS, Hill JR, Bazer FW, Bartol FF, Spencer TE. Endometrial glands are required for preimplantation conceptus elongation and survival. Biol Reprod. 2001;64:1608–13.

    Article  CAS  PubMed  Google Scholar 

  30. Hoffman L, Winfrey V, Blaeuer G, Olson G. A haptoglobin-like glycoprotein is produced by implantation-stage rabbit endometrium. Biol Reprod. 1996;55:176–84.

    Article  CAS  PubMed  Google Scholar 

  31. Sprekeler N, Kowalewski M, Boos A. TRPV6 and Calbindin-D9k-expression and localization in the bovine uterus and placenta during pregnancy. Reprod Biol Endocrinol. 2012;10:66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Balhara AK, Gupta M, Singh S, Mohanty AK, Singh I. Early pregnancy diagnosis in bovines: current status and future directions. Sci World J. (Article ID 958540).

  33. Kline K, Schwartz D, Lewis W, Hultgren S, Lewis A. Immune activation and suppression by group B streptococcus in a murine model of urinary tract infection. Infect Immun. 2011;79(9):3588–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Authors’ contributions

AT and RB collected the urine samples, PR, MJ and JN carried out the sample preparation and 2D DIGE experiment; SB and MLY carried out MS based identification and label free quantitation; SB and SYA performed the bioinformatics analysis; SK, PB, AKM and AKB analyzed the data; AKM, TKM, AKD, JKK and IS conceived the ideas, designed the experiments and arranged the funds; RV helped in collection of MS data and provided technical support. All authors read and approved the final manuscript.

Acknowledgements

The authors acknowledge their thanks to the National Agricultural Science Fund (NASF), Indian Council of Agricultural Research (ICAR), and Government of India for the funding support.

Availability of data and materials

Data are freely available for the readers.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

The authors gave their consent for publication of the research results.

Ethics approval and consent to participate

The experiment did not require any ethics approval as no invasive producer was followed.

Author information

Author notes

    Authors and Affiliations

    1. National Dairy Research Institute, Karnal, 132001, India

      Preeti Rawat, Shveta Bathla, Rubina Baithalu, Munna Lal Yadav, Sudarshan Kumar, Syed Azamal Ali, Anurag Tiwari, Masoud Lotfan, Jasmine Naru, Manoj Jena, Pradip Behere, Ajay Dang, Jai K. Kaushik, Tushar K. Mohanty & Ashok K. Mohanty

    2. Central Institute for Research on Buffaloes, Hisar, 125001, India

      Ashok K. Balhara & Inderjeet Singh

    3. Bruker Daltonics, Bangalore, India

      Rajesh Vashisth

    Authors
    1. Preeti Rawat
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Shveta Bathla
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Rubina Baithalu
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Munna Lal Yadav
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Sudarshan Kumar
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Syed Azamal Ali
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Anurag Tiwari
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Masoud Lotfan
      View author publications

      You can also search for this author in PubMed Google Scholar

    9. Jasmine Naru
      View author publications

      You can also search for this author in PubMed Google Scholar

    10. Manoj Jena
      View author publications

      You can also search for this author in PubMed Google Scholar

    11. Pradip Behere
      View author publications

      You can also search for this author in PubMed Google Scholar

    12. Ashok K. Balhara
      View author publications

      You can also search for this author in PubMed Google Scholar

    13. Rajesh Vashisth
      View author publications

      You can also search for this author in PubMed Google Scholar

    14. Inderjeet Singh
      View author publications

      You can also search for this author in PubMed Google Scholar

    15. Ajay Dang
      View author publications

      You can also search for this author in PubMed Google Scholar

    16. Jai K. Kaushik
      View author publications

      You can also search for this author in PubMed Google Scholar

    17. Tushar K. Mohanty
      View author publications

      You can also search for this author in PubMed Google Scholar

    18. Ashok K. Mohanty
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Ashok K. Mohanty.

    Additional information

    Preeti Rawat and Shveta Bathla contributed equally to this work

    Additional files

    Rights and permissions

    Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Rawat, P., Bathla, S., Baithalu, R. et al. Identification of potential protein biomarkers for early detection of pregnancy in cow urine using 2D DIGE and label free quantitation. Clin Proteom 13, 15 (2016). https://doi.org/10.1186/s12014-016-9116-y

    Download citation

    • Received:

    • Accepted:

    • Published:

    • DOI: https://doi.org/10.1186/s12014-016-9116-y

    Keywords

    Clinical Proteomics

    ISSN: 1559-0275