Dataset Information

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

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.

SUBMITTER: Rawat P

PROVIDER: S-EPMC4946208 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Rawat Preeti P Bathla Shveta S Baithalu Rubina R Yadav Munna Lal ML Kumar Sudarshan S Ali Syed Azamal SA Tiwari Anurag A Lotfan Masoud M Naru Jasmine J Jena Manoj M Behere Pradip P Balhara Ashok K AK Vashisth Rajesh R Singh Inderjeet I Dang Ajay A Kaushik Jai K JK Mohanty Tushar K TK Mohanty Ashok K AK

Clinical proteomics 20160715

<h4>Background</h4>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 prot ...[more]

PMID: 27429603

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Project description:BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Dataset Information

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

Publications

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

Similar Datasets

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