Utility, limitations, and promise of proteomics in animal science

doi:10.1016/j.vetimm.2010.10.003

Veterinary Immunology and Immunopathology

Volume 138, Issue 4, 15 December 2010, Pages 241-251

https://doi.org/10.1016/j.vetimm.2010.10.003 Get rights and content

Abstract

Proteomics experiments have the ability to simultaneously identify and quantify thousands of proteins in one experiment. The use of this technology in veterinary/animal science is still in its infancy, yet it holds significant promise as a method for advancing veterinary/animal science research. Examples of current experimental designs and capabilities of proteomic technology and basic principles of mass spectrometry are discussed. In addition, challenges and limitations of proteomics are presented, stressing those that are unique to veterinary/animal sciences.

Section snippets

Background: transition from genomic studies to proteomic studies

Proteomics has rapidly moved from a relatively new technology to a rapidly maturing essential tool in the omics age. Its existence is largely due to the success of the genome projects as well as rapid advancements in commercial mass spectrometers. The field of proteomics could not exist without the success of the genome projects. The genome projects of the various domestic animals will continue to increase our ability to associate desired traits with the necessary genes/proteins. Genome

Utility: what questions does a proteomics experiment ask?

Proteins play many fundamental roles in all biological processes. Some functions of proteins include: structural building blocks, conduits of information, controllers of chemical reactions, and antimicrobial defense mechanisms. The functional abilities of cells are dynamic as cells respond to stimuli or stresses. Much of a cell's response to stimuli or stress is manifest by the alteration of the expression levels of various proteins. Identification and understanding of proteins involved in

Limitations: what are some difficulties of a proteomic experiment?

The promise of proteomics does come with a number of difficulties that must be addressed or acknowledged to reduce the limitations of this technology. Some of the factors that limit proteomics are the quality of the genomic databases, the complexity and dynamic range of proteins in a sample, the capabilities of various mass spectrometers, and the cost of these experiments.

Promise: what will proteomics be able to do?

Despite the limitations that the field of proteomics currently has to deal with, it has become an extremely important tool in biological sciences. The first unique advantage of this technology is the fact that a fairly large number of proteins can be identified and quantitated at one time, without any prior knowledge that any specific protein might exist in a sample. Analyzing a proteomic dataset can often lead to surprising results, and the unexpected may be the most interesting observation.

Conflict of interest

Authors have no financial or other relationships that would inappropriately influence this work.

References (57)

L. Anderson et al.
Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins
Mol. Cell. Proteomics
(2006)
B. Boeckmann et al.
Protein variety and functional diversity: Swiss-Prot annotation in its biological context
C. R. Biol.
(2005)
S. Brunet et al.
Organelle proteomics: looking at less to see more
Trends Cell Biol.
(2003)
S.A. Carr et al.
The need for guidelines in publication of peptide and protein identification data
Mol. Cell. Proteomics
(2004)
W.C.S. Cho
Proteomics technologies and challenges
Genomics Proteomics Bioinform.
(2007)
T.J. Griffin et al.
Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae
Mol. Cell. Proteomics
(2002)
X. Han et al.
Mass spectrometry for proteomics
Curr. Opin. Chem. Biol.
(2008)
J.R. Kettman et al.
Proteome, transcriptome and genome: top down or bottom up analysis?
Biomol. Eng.
(2001)
J.E. Kornalijnslijper et al.
Bacterial growth during the early phase of infection determines the severity of experimental Escherichia coli mastitis in dairy cows
Vet. Microbiol.
(2004)
J.D. Lippolis et al.
Proteomic survey of bovine neutrophils
Vet. Immunol. Immunopathol.
(2005)

I.H. Mather

A review and proposed nomenclature for major proteins of the milk–fat globule membrane

J. Dairy Sci.

(2000)

E.A. Panisko et al.

The postgenomic age: characterization of proteomes

Exp. Hematol.

(2002)

J. Ptacek et al.

Charging it up: global analysis of protein phosphorylation

Trends Genet.

(2006)

T.A. Reinhardt et al.

Developmental changes in the milk fat globule membrane proteome during the transition from colostrum to milk

J. Dairy Sci.

(2008)

K. Takemura et al.

Efficacy of immunization with ferric citrate receptor FecA from Escherichia coli on induced coliform mastitis

J. Dairy Sci.

(2002)

N. Zolotarjova et al.

Combination of affinity depletion of abundant proteins and reversed-phase fractionation in proteomic analysis of human plasma/serum

J. Chromatogr. A

(2008)

R.H. Aebersold et al.

Mass spectrometry-based proteomics

Nature

(2003)

M. Bantscheff et al.

Quantitative mass spectrometry in proteomics: a critical review

Anal. Bioanal. Chem.

(2007)

R.B. Cole

Some tenets pertaining to electrospray ionization mass spectrometry

J. Mass Spectrom.

(2000)

J. Colinge et al.

Introduction to computational proteomics

PLoS Comput. Biol.

(2007)

A.L. Cox et al.

Identification of a peptide recognized by five melanoma-specific human cytotoxic T cell lines

Science

(1994)

B.F. Cravatt et al.

The biological impact of mass-spectrometry-based proteomics

Nature

(2007)

M.P. DeLisa et al.

DNA microarray-based identification of genes controlled by autoinducer 2-stimulated quorum sensing in Escherichia coli

J. Bacteriol.

(2001)

B. Domon et al.

Mass spectrometry and protein analysis

Science

(2006)

J.E. Elias et al.

Target–decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Nat. Methods

(2007)

A.-C. Gingras et al.

Analysis of protein complexes using mass spectrometry

Nat. Rev. Mol. Cell Biol.

(2007)

J. Granger et al.

Albumin depletion of human plasma also removes low abundance proteins including the cytokines

Proteomics

(2005)

S.P. Gygi et al.

Correlation between protein and mRNA abundance in yeast

Mol. Cell. Biol.

(1999)

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Published by Elsevier B.V.

Utility, limitations, and promise of proteomics in animal science

Abstract

Section snippets

Background: transition from genomic studies to proteomic studies

Utility: what questions does a proteomics experiment ask?

Limitations: what are some difficulties of a proteomic experiment?

Promise: what will proteomics be able to do?

Conflict of interest

Mol. Cell. Proteomics

C. R. Biol.

Trends Cell Biol.

Mol. Cell. Proteomics

Genomics Proteomics Bioinform.

Mol. Cell. Proteomics

Curr. Opin. Chem. Biol.

Biomol. Eng.

Vet. Microbiol.

Vet. Immunol. Immunopathol.

J. Dairy Sci.

Exp. Hematol.

Trends Genet.

J. Dairy Sci.

J. Dairy Sci.

J. Chromatogr. A

Mass spectrometry-based proteomics

Nature

Quantitative mass spectrometry in proteomics: a critical review

Anal. Bioanal. Chem.

Some tenets pertaining to electrospray ionization mass spectrometry

J. Mass Spectrom.

Introduction to computational proteomics

PLoS Comput. Biol.

Identification of a peptide recognized by five melanoma-specific human cytotoxic T cell lines

Science

The biological impact of mass-spectrometry-based proteomics

Nature

DNA microarray-based identification of genes controlled by autoinducer 2-stimulated quorum sensing in Escherichia coli

J. Bacteriol.

Mass spectrometry and protein analysis

Science

Target–decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Nat. Methods

Analysis of protein complexes using mass spectrometry

Nat. Rev. Mol. Cell Biol.

Albumin depletion of human plasma also removes low abundance proteins including the cytokines

Proteomics

Correlation between protein and mRNA abundance in yeast

Mol. Cell. Biol.