J O U RN A L OF P R O TE O MI CS 7 4 (2 0 1 1 ) 2 8 2–2 9 3
available at www.sciencedirect.com
www.elsevier.com/locate/jprot
Review
Farm animal proteomics — A review Emøke Bendixen a,⁎, Marianne Danielsen a , Kristin Hollung b , Elisabetta Gianazza c , Ingrid Miller d a
Department of Animal Health and BioScience, Faculty of Agricultural Sciences, Århus University. P.O. Box 50, 8830 Tjele, Denmark Nofima Mat AS, Osloveien 1, NO-1430 Ås, Norway c Gruppo di Studio per la Proteomica e la Struttura delle Proteine, Dipartimento di Scienze Farmacologiche, Università degli Studi di Milano, via G. Balzaretti 9, I - 20133 Milano, Italy d Department for Biomedical Sciences, University of Veterinary Medicine Vienna, Veterinärplatz 1, A-1210 Vienna, Austria b
AR TIC LE I N FO
ABS TR ACT
Article history:
In agricultural sciences as in all other areas of life science, the implementation of
Received 17 August 2010
proteomics and other post-genomic tools is an important step towards more detailed
Accepted 5 November 2010
understanding of the complex biological systems that control physiology and pathology of
Available online 26 November 2010
living beings. Farm animals are raised in large-scale operations, with the aim to obtain animal products for human consumption. Hence, understanding the biological traits that
Keywords:
impact yield and quality of these products is the specific aim of much biological
Proteomics
experimentation. However, most of the data gathered from experiments on e.g. swine
Meat
and cattle are relevant not only for farm animal sciences, but also for adding to our
Milk
understanding of complex biological mechanisms of health and disease in humans.
Health
The aim of this review is to present an overview of the specific topics of interest within farm
Cattle
animal proteomics, and to highlight some of the areas where synergy between classic model
Pig
organism proteomics and farm animal proteomics is rapidly emerging. Focus will be on
Systems biology
introducing the special biological traits that play an important role in food production, and on how proteomics may help optimize farm animal production. © 2010 Published by Elsevier B.V.
Contents 1. 2. 3.
Introduction . . . . . . . . . . . . . . . . . . . Biology and production traits in farm animals . Proteome markers for meat and milk quality . 3.1. Meat quality . . . . . . . . . . . . . . . 3.2. Milk quality . . . . . . . . . . . . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
283 283 284 284 285
Abbreviations: 2-DE, two-dimensional electrophoresis; BSE, bovine spongiform encephalopathy; CSF, cerebrospinal fluid; iTRAQ, isobaric tags for relative and absolute quantitation; LPS, lipopolysaccharide; MFGM, milk fat globule membrane; NEC, necrotizing enterocolitis; pig-MAP, inter-alpha-trypsin inhibitor heavy chain H4; PMWS, post-weaning multisystemic wasting syndrome; PRRS, porcine respiratory and reproductive syndrome; QTLs, quantitative trait loci; SCNT, somatic cell nuclear transfer; SRM, Selected Reaction Monitoring; UDPG, UDP-glucose pyrophosphorylase ⁎ Corresponding author. Department of Animal Health and Bioscience, Århus University, P.O. Box 50, DK-8830 Tjele, Denmark. Tel.: + 45 8999 1246; fax: + 45 8999 1500. E-mail address:
[email protected] (E. Bendixen). 1874-3919/$ – see front matter © 2010 Published by Elsevier B.V. doi:10.1016/j.jprot.2010.11.005
283
J O U RN A L OF P R O TE O MI CS 74 ( 20 1 1 ) 2 8 2–2 9 3
4.
Proteome markers in farm animal health . . . . . . . . . . 4.1. Mapping the serum and other body fluid proteomes 4.2. Mastitis in cattle . . . . . . . . . . . . . . . . . . . . 4.3. Gut health in pigs . . . . . . . . . . . . . . . . . . . 5. Farm animals — a new generation of model organisms . . 5.1. Digestive physiology . . . . . . . . . . . . . . . . . . 5.2. Neurodegenerative disorders . . . . . . . . . . . . . 5.3. Obesity and metabolism . . . . . . . . . . . . . . . . 6. Repositories of pig and cattle proteome data . . . . . . . . 7. Concluding remarks — what's next? . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.
Introduction
Farm animals include cattle, swine, poultry as well as small ruminants like sheep and goat. In recent years, also fish and prawn have been increasingly produced under farming conditions, and as such widened the concept of livestock and farm animal production. Animal farming is a large industrial sector, with global populations estimated to be 1 billion of pigs (www.thepigsite. com, and www.zoosavvy.com) and 1.3 billions of cattle (www. cattle-today.com). Farm animals are industrially bred and raised with the aim to produce food for human consumption, mainly meat and dairy products, which provide a large proportion of protein in food, at least in developed nations. Therefore farm animal sciences are essentially aimed at understanding biological mechanisms related to the production of food. Farm industries must minimize their costs of production in order to survive in a competitive market, while productivity must be optimized to meet the requirements of a growing human population. At the same time, the farmers must be responsible for monitoring health status and practicing good animal welfare. In particular, recognizing disease at an early stage is an enormous challenge for individual farmers, because of the increasingly large herds that are the current practice in the farm industry. In Western Europe it is not unusual to find as many as 200 milk cows in a single farmer's care. Balancing these needs may greatly benefit from detailed knowledge of the relevant biology of farm animals; accordingly, biomarkers that can help optimize a sustainable balance between productivity, product quality and animal welfare are in high demand. This is a major reason for the rapid growth of the interest in farm animal genomics and proteomics during the last decade, which has been dominated by studies related to understanding the biological traits that are important for production of meat and milk. Often the proteomics of cattle and pig has been narrowly targeted at enhancing product quality [1,2]. Recently, farm animal proteomics studies have also focused on how animal health and welfare can be monitored and enhanced through relevant biomarkers [3,4]. Many ongoing studies testify that further development of this field is truly important, and that proteomics has great potentials for contributing to solve some of the challenges of the farm and food industry [5,6].
. . . . . . . . . . of farm animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
. . . . . . . . . . .
286 286 286 287 288 288 288 288 289 289 289
Moreover, farm animals are becoming increasingly important model organisms for studying human disorders; in particular, this is true for pigs whose omnivorous feeding habits and metabolism as well as body size and life span are closer to humans than for rodents [3,7]. A brief overview on current distribution of proteomics studies for pig and cattle is given in Fig. 1. With the recent progress in genomics, the speed and ease by which farm animal proteomes can be studied will soon be comparable to that of classical model organisms like rodents, zebrafish and fruit fly; on the contrary, very little attention has so far been devoted to small ruminants, chicken and farmed fish. The aim of this review is to introduce some of the major themes and achievements in bovine and porcine proteome analyses in the past decade, and to present some of the views and considerations that influence the contemporary field of farm animal proteomics. In Sections 2 and 3, focus will be on the special biological traits that have major impact on production of meat and milk, and on how proteomics may help optimize production in food and farm industries. Section 4 is aimed at giving an overview of proteomics used in veterinary health, while understanding farm animals as model organisms for biomedical research will be discussed in detail in Section 5. An overview of current data repositories relevant to farm animal proteomics is presented in Section 6.
2.
Biology and production traits in farm animals
The biology of farm animals is widely influenced by domestication and selective breeding. Animals with biological traits that obviously allowed better and more efficient production of meat and milk were selected for breeding over the last 9000 years [8]. Cattle breeds were optimized either for milk yield, like the Holstein–Fresian breed, which produces 50 l of milk/day, or for meat production, like Belgian Blue, which can grow up to 4 kg of muscle tissue per day (www.ansi.okstate. edu/breeds). Pig breeds were primarily selected according to their feed conversion and growth phenotypes [9], including characteristics in muscle development and fat deposition [10,11]. Therefore, contemporary pig breeds show extreme biological variation in obesity and leanness traits. This is exemplified by the extreme growth phenotypes of Danish Landrace pigs that build 20 mm backfat versus the Mangalica breed that may deposit as much as 200 mm backfat, when fed
284
J O U RN A L OF P R O TE O MI CS 7 4 (2 0 1 1 ) 2 8 2–2 9 3
Fig. 1 – Distribution of farm animal proteomics literature. Number of published manuscripts within the keywords pig proteomics (A) and cow proteomics (B). Searches were processed on PubMed (January, 2010), and grouped according to the subtopics shown.
the same diet. For a review on growth properties of different pig breeds, see [11]. While intensive line-breeding has provided a tremendous increase in the efficiency of food production, it has also led to new issues that need to be faced, like accumulation of recessive genetic variants in animal populations [12, 13] and health problems caused by extreme biological phenotypes. Some important examples include the large frequency of mastitis and udder infections in dairy cattle, which is closely associated with extreme milk yields [14], and frequent problems with calving within meat breeds, caused by abnormal fetal growth resulting from the excessive growth of muscle tissues [15]. A means to overcome these problems is to characterise in more detail the complex biological mechanisms involved in these industrially important traits of farm animals. Within farm animal sciences, the term production trait is commonly used to refer to biological traits that are directly related to the yield and quality of the animal products [8]. The most important production traits include yield and quality of milk and meat, which are readily monitored at individual animal levels, but may also mean complex metabolic traits like muscle growth, fat deposition, and feed conversion, being multifactorial and not easy to measure unambiguously. The patterns of inheritance are usually complex (non-Mendelian) because many genes interact to control these traits. In animal genetics, they are often referred to as quantitative traits, and in the past decade much progress has been made to map quantitative trait loci (QTLs), as well as individual candidate genes, and polymorphisms that influence these production traits in cattle [16–18] and pig [19]. The extreme biological variation that is apparent in farm animals provides valuable and informative animal models to be studied, either for solving the challenges of farm industry, or for developing models for human biomedical research. In particular, these extreme phenotypes are important for characterising specific genetic influence on complex biological
traits. Likewise, for proteome analyses, animals with extreme phenotypic variation and well-characterised genetic linkage of traits are of great value, because knowledge about genetic variation provides means by which animals can be grouped informatively. This allows the reduction of the complexity in comparative and quantitative proteomics, which is particularly important when searching for biomarkers of complex traits.
3.
Proteome markers for meat and milk quality
Meat and milk are major sources of proteins in the human diet. Both are raw materials of high value, but are economically fully exploited only through processing by the dairy and meat industries. The quality of the multiple end-products (e.g. cheese and cured meat) is highly dependent on the quality of the raw materials, hence directly associated with the structure and function of the constituent proteins [20]. Proteome-based markers for meat and milk quality are in great demand, because these markers allow scoring and ranking raw materials. Quality ranking allows optimal use of the entire product range by assessing the specific suitability of the individual products for further industrial processing [20]. Moreover, proteome markers are useful for monitoring and optimizing the industrial processes in the dairy and slaughter industry. Such technological markers are thus important tools for maximizing the gains of the food industry. In this chapter, we will discuss some important themes within meat and milk proteomics, which have been in focus during the last decade.
3.1.
Meat quality
Variations in meat quality characteristics, like tenderness, juiciness, flavour and odour, are closely related to the
J O U RN A L OF P R O TE O MI CS 74 ( 20 1 1 ) 2 8 2–2 9 3
biological traits and genetic variations of the live animals [21]. Hence a broad selection of studies including genetics, physiology, cell biology and biochemistry has been aiming at characterising biological traits and molecular mechanisms behind observable variations in meat quality [22–27]. In particular, the biology of muscle differentiation and growth, carcass composition, and fat deposition patterns have been characterised in detail at the proteome level [10,28,29]. While intramuscular fat deposition increases the taste and juiciness of meat, the subcutaneous fat deposition is a waste in meat production. Hence, optimizing pigs through breeding towards deposition of intramuscular rather than subcutaneous fat is a major goal [5]. Proteomics is an important tool for finding candidate genes that may influence this fat deposition. As these traits are complex, and multigenic in nature, molecular characterisation greatly benefits from correlated proteome and transcriptome analyses. Although many individual transcriptome [5] and proteome studies [1] have been made to understand muscle growth and development, so far only a few attempts have been made to integrate these technologies to explore at greater detail the molecular systems behind muscle growth and development in pig and cattle [10,30,31]. Tenderness in meat is a complex trait, affected by genetics as well as by pre- and post-slaughter handling and storage of animals and carcasses. Proteome studies have been aimed at characterising protein markers that can assess development of tenderness during post-mortem storage of the carcass [26,32]. In particular, post-mortem proteolysis in muscle has been closely described in beef [24,25,33] as well as in pork [29,32,34]. Hence, potential tenderness markers involved in several processes in vivo, including myofiber stability, muscle cell viability and protection against oxidative stress, have been suggested in addition to the well-known calpain/calpastatin system [26,35,36]. Genetic variation may give rise to variation in meat quality, as exemplified by the mutation in the PRKAG gene in pigs that leads to hyper-accumulation of glycogen in muscles, and to reduced meat quality [37,38]. Proteome studies have shown that the inferior quality of these meats seems to be related to post-mortem antioxidant and repair capacities, proteolysis and protein solubility [24]. A major meat quality challenge arises from the strong faeces-like smell that is commonly termed boar taint. This trait occurs in meat from approximately 10% of uncastrated male pigs; it is one of the major problems for the pork industry, and the reason for the currently common practice to castrate male pigs at birth. Boar taint is caused mainly by elevated levels of the male hormone androstenone and by the metabolite skatole [39,40]. Genetic screenings indicate a large but complex impact of genetic variation on the expression levels of these hormones and their association with boar taint [41– 43]. Extensive transcriptome studies have been made in order to find molecular markers that can help in recognizing animals that may develop this meat quality problem [44,45]. Dry-curing and processing of meats are large industrial sectors, thus minimizing biological variation in the raw materials that affect the quality of the end-products has a great economical impact. The level of post-mortem proteolysis in the muscle is determining texture, flavour and odour of
285
the final products. During the last few years, proteomics has been used to further understand these processes especially in products from pig meats such as dry-cured hams and Bayonne hams [46, 47]. Identification of naturally generated small peptides derived from myofibrillar proteins like myosin light chain I, titin and actin, expands the knowledge on the different proteinases that influence food properties. The aim of these meat proteomics studies is to characterise the quality and processing conditions of meat, in order to predict the quality of the end-product. Using a systems biology approach integrating both genetics and proteomics will be of great benefit also in this research field.
3.2.
Milk quality
Milk is the complex body fluid that is biologically designed to nurture all newborn mammals. For this purpose milk contains many secreted proteins; the major components are nutrients, while the low abundant proteins, which include anti-microbial factors, cytokines and chemokines, are believed to contribute to protect the neonate and its mother against pathogens and other post-partum environmental challenges [48,49]. For the dairy industry, bovine milk is a high value raw material, which is processed into cheese and other dairy products. While the major protein components of both human and bovine milk were biochemically characterised two decades ago [50], the analyses of the less abundant milk proteins have only just recently been reported for bovine [4,51–53] and porcine milk [54]. The relative secretion of proteins into milk fluctuates extensively as a result of genetic variations [55–57] as well as of epigenetic and environmental factors, as recently reviewed by [58]. Another source of complexity in milk proteins arises from the extensive variation in post-translational modifications, including glycosylation [59,60], phosphorylation [61,62], and proteolysis. The dairy industry has long known that the quality of dairy products depends both on the biological fluctuations in protein components [63] and on the modifications that milk proteins undergo during storage and processing [64,65]. Therefore, proteomics has become a very important tool for connecting the milk proteome to technically relevant properties of milk, by assessing protein yield and characterising protein polymorphism as well as protein modifications, like N- and O-glycosylation, or lysine lactosylation through heat treatment [64]. Specifically, the influence of glycosylation to stabilize the casein micelle and the overall issue of milk protein stability have been investigated in detail [50]. More recently, proteomics has also been used for quality control e.g. of cheeses [66]. For comparative and quantitative proteomics of milk samples, the problem of a wide dynamic range is a limiting factor. The caseins (including the αs1-, αs2-, ß-, and κ-forms) make up 80% of overall protein content of milk [67]. The less abundant proteins can only be detected after prefractionation, e.g. after the removal of caseins by precipitation at pH 4.6, which leaves in solution the whey proteins, mainly consisting of ß-lactoglobulin, α-lactalbumin, lactoferrin and lactoperoxidase [52,68,69]. Further depletion of medium- to high-abundance proteins is achieved in preparations of the milk fat globule membrane (MFGM) allowing the study of minor components. MFGM proteins belong to a
286
J O U RN A L OF P R O TE O MI CS 7 4 (2 0 1 1 ) 2 8 2–2 9 3
subcategory of milk protein (less than 5% of total milk protein) that is mainly hydrophobic and thus better separated and detected by LC–MS/MS methods [51,70–72]. Smolenski et al. provide an interesting comparison of LC–MS and 2-DE results on colostrum, milk and milk fractions [73]. A recent interest in proteomic milk studies has been the characterisation of the minor protein components, which have been recognized to have a wide range of bioactive roles, including control of inflammatory response and autoimmune diseases [74,75]. In particular, colostrum, the fluid that is the first milk secreted immediately after birth, has been analysed in detail [54,72,73,76,77]. Its high content of bioactive proteins is important in host defence as well as for promoting gastrointestinal growth and development of the newborn. Proteomic analyses of these minor protein components thus provide important knowledge about the role of milk-based nutrition for human health.
4.
Proteome markers in farm animal health
As in human pathology, proteome based biomarkers are important for early diagnostics in veterinary medicine. Diseases prevailing in farm animals, however, are very different from those that commonly challenge human health (like cancers and cardiovascular diseases). Thus, dedicated biomarker studies on farm animals are needed to monitor animal health and welfare, and to investigate type and state of disease, for ensuring animal welfare as well as for monitoring quality and safety of animal products. From an economical or productive point of view the most harmful diseases in pig production are PRRS (porcine respiratory and reproductive syndrome) and PMWS (post-weaning multisystemic wasting syndrome) which both cause high mortality rates in piglets and reduce growth and feed conversion in young pigs [78,79]. In cattle, major health issues include mastitis, which is an infection caused by a variety of pathogens invading the lactating mammary gland [80], whereas brucellosis, tuberculosis and salmonellosis are most relevant for their effects on meat production [81,82]. In this section, we will describe proteomic studies relevant for diagnosis of common veterinary diseases. Examples presented in detail include the progress in characterisation of body fluids from pig and cow, and selected cases of major importance for health and production of cattle and pig, namely bovine mastitis and gut health of pigs.
4.1. Mapping the serum and other body fluid proteomes of farm animals For monitoring health and disease in farm animals, body fluids like serum, plasma and milk are important diagnostic samples, since their compositions reflect the overall health status of the individual animal [83,84]. Serum of different species has only roughly the same composition: 2-DE protein patterns seem comparable, but may differ in detail, i.e. physicochemical parameters and/or concentration of homologous proteins or appearance of species-specific proteins. Detailed serum/plasma protein 2-DE identification maps from healthy cattle [85,86], and pig [87] have been described.
The bovine 2-DE serum protein map was established at a time when the bovine genome was not completely sequenced, but immunoblotting with cross-reactive antibodies and mass spectrometric methods allowed identifying the position of 22– 26 high- to medium-abundance proteins [85,86]. The main species-specific features include the following: extremely low haptoglobin levels in healthy animals, a second, truncated transferrin chain, and noticeable levels of serum amino oxidase; the appearance of the latter may reflect that ruminants have a digestive system very different from monogastric mammals [85]. Bovine serum pattern changes were studied in animals with acute udder inflammation [85] and during pregnancy with or without complications. Pregnancy was observed to be associated with a marked change in acute phase proteins before calving, with a different pattern in animals that developed post-partum endometritis [88]. Fig. 2 gives details on some of these findings, showing time-dependent levels of orosomucoid, haptoglobin, and inter-α-trypsin inhibitor under both conditions. A truncated form of apolipoprotein A-I was found in plasma of veal calves after anabolic androgenic steroid administration [89]. Other proteomics studies have examined the epithelial lining fluid of bovine respiratory tract revealing that, under the influence of dexamethasone, an acute phase reaction occurs with induction of adipocyte-fatty acid binding protein and odorant binding protein [90]. Its composition was also altered in stress conditions, e.g. transport, suggesting altered respiratory disease susceptibility [91]. In CSF/spinal fluid of BSE-affected cows a more than 10-fold increase of apolipoprotein E was detected [92], forming a cluster of 5–6 spots in the gels. Lately, autoantibodies against brain glial fibrillary acidic protein were detected in sera of cattle with BSE [93]. In pigs, the identification of 27 high- to medium-abundance plasma proteins has been reported in the 2-DE protein pattern of healthy Landrace × Large White pigs, including some examples of infection/inflammation regulated proteins [87]. Twenty-seven proteins were shown differentially regulated in pigs with severe peritonitis-induced sepsis [94], among them some of the typical porcine acute phase proteins—major acute phase protein (pig-MAP= inter-α-trypsin inhibitor heavy chain H4), haptoglobin, hemopexin, α2-HSglycoprotein, albumin, and apolipoprotein A-I [83,95]. Recently, a preliminary 2-DE map of porcine saliva has been reported, based on samples from healthy animals [96]. The benefit of saliva as a specimen is that sample collection is not invasive, hence allowing frequent sampling.
4.2.
Mastitis in cattle
In dairy farming, the annual cost associated with treatment, culling and death of cows as well as loss in milk production due to mastitis is estimated to be £168 million in the UK alone [97]. Furthermore, mastitis severely compromises the welfare of cows. Early diagnosis and identification of the causal pathogen are crucial for initiating a successful antibiotic treatment, ensuring fast recovery and thereby minimizing the impact on animal health.
J O U RN A L OF P R O TE O MI CS 74 ( 20 1 1 ) 2 8 2–2 9 3
287
Fig. 2 – Change of cow serum protein pattern in course of pregnancy. A) 2-DE of cow serum at delivery (pool of 8 samples). Running conditions: 1d = IPG 4-10NL, 8 M urea, reducing conditions; 2d = 6–16% T polyacrylamide. Four protein chains that change most in course of pregnancy are marked. Concentrations of those four protein chains around time-point of delivery (day 0): B) of a healthy cow, and C) of a cow with post-partum endometritis; values determined from bands in SDS-PAGE. For more details see [88]. Full line: orosomucoid; broken line: dark — haptoglobin ß-chain; grey — haptoglobin α-chain; broken line: darker grey — inter-α-trypsin.
Proteomics methods have been applied to study the pathophysiology of bovine mastitis and to identify novel, early and sensitive biomarkers. Many studies have investigated proteome changes in mastitis milk [4,69,73,98] and plasma [99], with the aim of identifying diagnostic markers in easily accessible media. Other studies have focused on the composition of MFGM [51,53,71–73], which may be valuable for monitoring the pathological state of the mammary gland [51]. Pathogen-specific biomarkers, ideally detectable in milk using on-farm surveillance systems, would be highly valuable. If the distinction between Gram-positive and Gram-negative infections can be made already at early diagnostics of mastitis, the correct antibiotic treatment can be applied immediately, with great benefit to animal welfare, milk quality and farm economy. Recent progress has been made in both the technical and biological aspects towards identifying such markers by proteome analyses of experimentally induced mastitis, using the pathogens Staphylococcus aureus and Escherichia coli (which together account for more than 90% of all mastitis cases) [100]. S. aureus surface proteins and immunogenic proteins have been studied in clinical [101] as well as sub-clinical [102] mastitis. Differentially expressed proteins in milk were analysed after experimentally induced E. coli mastitis using a non-labeling approach [98], or LPS challenge using an iTRAQ tagged method [4], and both studies reported the quantitative response in the milk during the time-course of mastitis. Novel markers of host response were found, and animal-to-animal variation in these markers could clearly be correlated to the individual animal's physiological response to LPS challenge [4]. Specific markers that can be detected and quantified directly in bovine milk are useful for on-line surveillance, which combines on-farm equipment and routine assessment during daily milking. These systems are already commercially available but few biomarkers for early mastitis prediction have yet been validated for use in these
analytical systems, so that specific biomarkers, which can distinguish specific causal pathogens, remain to be characterised.
4.3.
Gut health in pigs
The gastrointestinal tract has a dual function: to efficiently acquire nutrients from digested food substances and to provide protection against potential pathogenic bacteria. These two contradicting needs — a large and thin intestinal surface for nutrient uptake and a strong barrier function against pathogens — have resulted in a trade off in structural and physiological adaptations in the gut to meet both requirements. The gut of newborn pigs is structurally and immunologically not fully developed, which makes early life a critical phase for pigs. Hence, gut health creates a major challenge to the pig industry, both in terms of animal welfare and economy [103]. A major issue in pig production is low birth weight, which results in high mortality rate among piglets during the first week of life [104]. Wang et al. described how intrauterine growth restriction affects the proteomes of small intestine, liver and skeletal muscle in newborn pigs and further showed continuous impairment of intestinal development in neonatal piglets with intrauterine growth restriction [105]. Avoiding diarrhoea is another major challenge in industrial production of pigs, particularly in neonatal and weaning piglets. The establishment of a beneficial microbiota is of key importance for protection against diarrhoea and for intestinal growth and development. The impact of individual gut bacteria on the intestinal proteome was studied in monoassociate germ-free piglets [106]. The proteome changes showed that non-pathogenic E. coli stimulated villus growth and cell turn-over, whereas this effect was not observed in piglets mono-associated with Lactobacillus fermentum. Adding
288
J O U RN A L OF P R O TE O MI CS 7 4 (2 0 1 1 ) 2 8 2–2 9 3
supplements such as zinc oxide to the feed is a commonly used strategy to minimize diarrhoea in weaning pigs. Proteomics studies revealed altered expression of proteins related to glutathione metabolism and apoptosis in the small intestine of pigs given zinc oxide [107]. Colostrum and its beneficial impact on gut development and pig health have also been studied by proteomics. A recent work aimed to characterise which proteins in colostrum can be traced within the intestinal tissue of neonatal piglets, thereby indicating that these proteins may have bioactive functions in the tissue [77]. Preterm piglets have been used as animal models to study the development of necrotizing enterocolitis (NEC), which is a major complication in prematurely born human neonates. These porcine models were also used to study the preventive roles of colostrum and milk formula [108,109].
5. Farm animals — a new generation of model organisms For understanding human diseases, the development of adequate animal models is of immediate importance. Although inbred and transgenic strains of classical model organisms like fruit fly, zebrafish and rodents have allowed characterising the functions of many genes and gene products, it has become increasingly clear that these animal models often fail to mimic the features of human diseases. Some obvious examples are seen in developing rodent models for Parkinson's disease [110,111] and for cystic fibrosis [112], which clearly do not reflect the pathology of these human diseases. Therefore, animal models that more closely resemble the genetics, anatomy, physiology and pathology of humans are in great demand. In this context, porcine models are particularly interesting, because pig is a non-primate mammal that is evolutionary closely related to man. Although pigs are not easily kept within laboratory facilities, they are nevertheless readily available for biomedical research through collaboration with the pig farming industry, whereby exceptionally large populations for experimental use can be available at reasonable costs. Moreover, various breeds of minipigs weighing less than 70 kg have been selected for use in pharmacological and toxicological research [113, 114] Much recent scientific progress has facilitated the development of porcine biomedical models. Most importantly, this includes the near completion of the porcine genome (http:// piggenome.org/) and the extensive characterisation of genetic variation in pigs [115,116]. Also, a wide range of proteome and transcriptome maps of porcine tissues and body fluids has been presented [117]. Moreover, the availability of cloned pigs through somatic cell nuclear transfer (SCNT) has greatly facilitated designing of relevant transgenic disease models [118,119]. As those animals have identical genomes, they are expected to be of great benefit to biological studies, in particular for characterising complex biological systems where the interplay of genetic and environmental factors are complexly intertwined. Relevant examples may be life-style related disorders like diabetes and metabolic syndromes, mental disorders and cardiovascular disorders [3,119]. In the present chapter we have selected a few specific areas of biomedical research where porcine models are either already
well developed or expected to be particularly interesting in the near future, also in context with proteomic studies and search for biomarkers.
5.1.
Digestive physiology
Unlike rodents, pigs are monogastric omnivores, with a gastrointestinal anatomy that is very similar to that of humans [120,121]. In addition, as discussed in chapter 4.3, the gastrointestinal biology of pigs has been characterised in great detail in the past decades and this knowledge is also expected to facilitate the understanding of gastrointestinal biology in humans [122]. We would highlight particularly the variety of studies on the impact of nutrition and food components [103], and specifically of fermented feeds and fermentable carbohydrates on gut health in pigs [123]. Both pathogenic and commensal microorganisms play important roles for porcine as well as for human health. However, investigating the molecular mechanisms taking place in hosts and microorganisms is a complex undertaking, which is greatly facilitated by experimental infections [124] and invasive sampling of gut tissues and gut contents. These are all possible in pigs, but hardly ever feasible in human intervention studies. The characterisation of many commensal microorganisms isolated from porcine gut has opened the possibility for in vitro studies and gnotobiotic pig models. Most of these microorganisms are likely to be important also for human health [125].
5.2.
Neurodegenerative disorders
Porcine models for characterising neurodegenerative disorders in man have been developed at a fast pace since SCNT-mediated cloning of pigs has become available. In this research field, porcine models are important because sampling of human brain biopsies is not possible, and rodent models have widely failed to mimic human neurodegenerative disorders. Current porcine models include pigs that carry mutant versions of genes known to cause human neuropathies, including amyloid-beta that causes Alzheimer's disease [126], α-synuclein that causes Parkinson's disease [127] and Huntington disease [128]. For a recent review on neuropathological pig models see [119]. Only very few proteome studies have been reported from these animals yet, but these models promise great opportunities for proteome studies in the near future.
5.3.
Obesity and metabolism
With obesity and metabolic syndromes becoming a major health threat, there is much current focus on understanding how complex interactions of genes and nutrition cause obesity and human obesity-associated pathologies. Porcine models can be very interesting because the main metabolism and insulin signalling of pigs closely resemble those of humans [3]. Indeed they have been applied to characterise mechanisms in obesity and energy metabolism [129], also including proteome studies [130]. Another important aspect is that, due to selective breeding, the domesticated pig breeds provide experimental models with extensive genetic and phenotypic variations in leanness
J O U RN A L OF P R O TE O MI CS 74 ( 20 1 1 ) 2 8 2–2 9 3
and obesity traits [131–133]. One example is the porcine mutation in the PRKAG gene coding for protein kinase AMPactivated γ subunit, which causes a glycogen storage disorder affecting muscle but not liver tissues. Muscle tissues from carriers of this mutation were characterised at transcript and protein levels [30] and this study clearly showed that in mutants many enzymes needed for glycogen synthesis, including UDP-glucose pyrophosphorylase (UDPG), are upregulated, while normal insulin and glucose-mediated regulation of cellular glucose transporters is bypassed [30]. A very recent proteome study was presented by [134] who analysed the serum proteome associated with diet-induced fatty liver in Ossabaw pigs. This study demonstrated that changes in the immunoregulatory, inflammatory and lipid metabolism pathways were closely related to those previously observed in serum from human patients with non-alcoholic fatty liver disease, further reflecting the likeness in metabolisms of pig and man.
6.
Repositories of pig and cattle proteome data
Detailed proteome maps are fundamental for biomarker studies and for defining which protein subsets are normally expressed in different tissues. A wide range of proteome maps, also from farm animal tissues, have been presented including both 2-DE gel maps [85,87,109,135,136] and LC–MS data [137,138]. However dedicated data repositories are still scarce for these species. Most information on bovine and porcine proteomes come from characterisation of muscles as well as of body fluids that have immediate diagnostic potentials, like serum and milk. On the other hand, tissues that may be very important for developing models of human disease (including cell types in the nervous system, relevant for neuropathologies) have not yet been well represented in the proteomic literature. Future mapping of a larger variety of porcine tissues will be of great benefit for building pig model studies. Large scale LC–MS/MS proteomics data are currently deposited in searchable repositories, like Pride (http://www. ebi.ac.uk/pride), Peptide Atlas (http://www.peptideatlas.org/ repository) and the NCBI-peptidome (http://www.ncbi.nlm. nih.gov/projects/peptidome). These repositories already extensively cover human proteomes and most classic model organisms, but so far only the Peptide Atlas includes the pig and cattle proteomes (http://www.peptideatlas.org). With regard to the pig proteome, the Peptide Atlas currently covers a very large collection of more than 2000 porcine proteins, represented by more than 13 000 peptides, based on analyses of 15 major tissues. The bovine Peptide Atlas covers more than 3000 proteins represented by more than 20 000 peptides, and is mainly based on data from milk and udder tissues. These data repositories are useful for selecting proteotypic peptides that allow targeted quantitative proteome analyses to be performed, through SRM (Selected Reaction Monitoring) [139– 141]. SRM-based proteome analyses promise to provide faster and more accurate identifications of individual proteins within complex proteomes, hence to allow extending the number of biological replicates in biomarker analyses, as well as providing measurements of absolute rather than relative
289
expression of specific proteins within complex proteome samples [142].
7.
Concluding remarks — what's next?
As discussed in this paper, farm animal proteomics has been implemented and successfully applied to optimize welfare and productivity in the farm and food industry. It has also become increasingly important for developing and describing animal models used in biomedical sciences. In particular, porcine biomedical models are to be expected to increase in importance and with them the need for pig proteome research. In methodology, future progress in farm animal proteomics depends on the same factors as human proteomics: both will benefit from increasing the throughput, which will allow analyses of much larger populations than currently feasible. This will greatly enhance our understanding of individual variation within specific biological mechanisms. Together with increased sensitivity (to reliably analyse also minor proteome components) and the implementation of more specific and robust quantification methods, these developments will be of great impact on our search for biomarkers.
REFERENCES [1] Picard B, Berri C, Lefaucheur L, Molette C, Sayd T, Terlouw C. Skeletal muscle proteomics in livestock production. Brief Funct Genomics 2010;9:259–78. [2] Guy PA, Fenaille F. Contribution of mass spectrometry to assess quality of milk-based products. Mass Spectrom Rev 2006;25:290–326. [3] Bendixen E, Danielsen M, Larsen K, Bendixen C. Advances in porcine genomics and proteomics — a toolbox for developing the pig as a model organism for molecular biomedical research. Brief Funct Genomics 2010;9:208–19. [4] Danielsen M, Codrea MC, Ingvartsen KL, Friggens NC, Bendixen E, Rontved CM. Quantitative milk proteomics — host responses to lipopolysaccharide-mediated inflammation of bovine mammary gland. Proteomics 2010;10:2240–9. [5] Wimmers K, Murani E, Ponsuksili S. Functional genomics and genetical genomics approaches towards elucidating networks of genes affecting meat performance in pigs. Brief Funct Genomics 2010;9:251–8. [6] Hiendleder S, Bauersachs S, Boulesteix A, Blum H, Arnold GJ, Frohlich T, et al. Functional genomics: tools for improving farm animal health and welfare. Rev Sci Tech 2005;24:355–77. [7] Lunney JK. Advances in swine biomedical model genomics. Int J Biol Sci 2007;3:179–84. [8] Andersson L, Georges M. Domestic-animal genomics: deciphering the genetics of complex traits. Nat Rev Genet 2004;5:202–12. [9] Giuffra E, Kijas JM, Amarger V, Carlborg O, Jeon JT, Andersson L. The origin of the domestic pig: independent domestication and subsequent introgression. Genetics 2000;154:1785–91. [10] Liu J, Damon M, Guitton N, Guisle I, Ecolan P, Vincent A, et al. Differentially-expressed genes in pig longissimus muscles with contrasting levels of fat, as identified by combined transcriptomic, reverse transcription PCR, and proteomic analyses. J Agric Food Chem 2009;57: 3808–17.
290
J O U RN A L OF P R O TE O MI CS 7 4 (2 0 1 1 ) 2 8 2–2 9 3
[11] Switonski M, Stachowiak M, Cieslak J, Bartz M, Grzes M. Genetics of fat tissue accumulation in pigs: a comparative approach. J Appl Genet 2010;51:153–68. [12] Thomsen B, Horn P, Panitz F, Bendixen E, Petersen AH, Holm LE, et al. A missense mutation in the bovine SLC35A3 gene, encoding a UDP-N-acetylglucosamine transporter, causes complex vertebral malformation. Genome Res 2006;16:97–105. [13] Ibeagha-Awemu EM, Kgwatalala P, Ibeagha AE, Zhao X. A critical analysis of disease-associated DNA polymorphisms in the genes of cattle, goat, sheep, and pig. Mamm Genome 2008;19:226–45. [14] Lund MS, Guldbrandtsen B, Buitenhuis AJ, Thomsen B, Bendixen C. Detection of quantitative trait loci in Danish Holstein cattle affecting clinical mastitis, somatic cell score, udder conformation traits, and assessment of associated effects on milk yield. J Dairy Sci 2008;91:4028–36. [15] Wibowo TA, Gaskins CT, Newberry RC, Thorgaard GH, Michal JJ, Jiang Z. Genome assembly anchored QTL map of bovine chromosome 14. Int J Biol Sci 2008;4:406–14. [16] Marques E, Nkrumah JD, Sherman EL, Moore SS. Polymorphisms in positional candidate genes on BTA14 and BTA26 affect carcass quality in beef cattle. J Anim Sci 2009;87: 2475–84. [17] Nilsen H, Olsen HG, Hayes B, Nome T, Sehested E, Svendsen M, et al. Characterization of a QTL region affecting clinical mastitis and protein yield on BTA6. Anim Genet 2009;40: 701–12. [18] Ogorevc J, Kunej T, Razpet A, Dovc P. Database of cattle candidate genes and genetic markers for milk production and mastitis. Anim Genet 2009;40:832–51. [19] Rothschild MF, Hu ZL, Jiang Z. Advances in QTL mapping in pigs. Int J Biol Sci 2007;3:192–7. [20] Candek-Potokar M, Monin G, Zlender B. Pork quality, processing, and sensory characteristics of dry-cured hams as influenced by Duroc crossing and sex. J Anim Sci 2002;80: 988–96. [21] Boleman SJ, Boleman SL, Miller RK, Taylor JF, Cross HR, Wheeler TL, et al. Consumer evaluation of beef of known categories of tenderness. J Anim Sci 1997;75:1521–4. [22] Xu YJ, Jin ML, Wang LJ, Zhang AD, Zuo B, Xu DQ, et al. Differential proteome analysis of porcine skeletal muscles between Meishan and Large White. J Anim Sci 2009;87: 2519–27. [23] Laville E, Sayd T, Terlouw C, Blinet S, Pinguet J, Fillaut M, et al. Differences in pig muscle proteome according to HAL genotype: implications for meat quality defects. J Agric Food Chem 2009;57:4913–23. [24] Laville E, Sayd T, Morzel M, Blinet S, Chambon C, Lepetit J, et al. Proteome changes during meat aging in tough and tender beef suggest the importance of apoptosis and protein solubility for beef aging and tenderization. J Agric Food Chem 2009;57:10755–64. [25] Jia X, Ekman M, Grove H, Faergestad EM, Aass L, Hildrum KI, et al. Proteome changes in bovine longissimus thoracis muscle during the early postmortem storage period. J Proteome Res 2007;6:2720–31. [26] Jia X, Veiseth-Kent E, Grove H, Kuziora P, Aass L, Hildrum KI, et al. Peroxiredoxin-6 — a potential protein marker for meat tenderness in bovine longissimus thoracis muscle. J Anim Sci 2009;87:2391–9. [27] Bouley J, Meunier B, Chambon C, De SS, Hocquette JF, Picard B. Proteomic analysis of bovine skeletal muscle hypertrophy. Proteomics 2005;5:490–500. [28] Chaze T, Meunier B, Chambon C, Jurie C, Picard B. In vivo proteome dynamics during early bovine myogenesis. Proteomics 2008;8:4236–48. [29] Lametsch R, Bendixen E. Proteome analysis applied to meat science: characterizing postmortem changes in porcine muscle. J Agric Food Chem 2001;49:4531–7.
[30] Hedegaard J, Horn P, Lametsch R, Sondergaard MH, Roepstorff P, Bendixen C, et al. UDP-glucose pyrophosphorylase is upregulated in carriers of the porcine RN− mutation in the AMP-activated protein kinase. Proteomics 2004;4:2448–54. [31] Hornshoj H, Bendixen E, Conley LN, Andersen PK, Hedegaard J, Panitz F, et al. Transcriptomic and proteomic profiling of two porcine tissues using high-throughput technologies. BMC Genomics 2009;10:30. [32] Lametsch R, Karlsson A, Rosenvold K, Andersen HJ, Roepstorff P, Bendixen E. Postmortem proteome changes of porcine muscle related to tenderness. J Agric Food Chem 2003;51:6992–7. [33] Bjarnadottir SG, Hollung K, Faergestad EM, Veiseth-Kent E. Proteome changes in bovine longissimus thoracis muscle during the first 48 h postmortem: shifts in energy status and myofibrillar stability. J Agric Food Chem 2010;58:7408–14. [34] Lametsch R, Roepstorff P, Bendixen E. Identification of protein degradation during post-mortem storage of pig meat. J Agric Food Chem 2002;50:5508–12. [35] Houbak MB, Ertbjerg P, Therkildsen M. In vitro study to evaluate the degradation of bovine muscle proteins post-mortem by proteasome and mu-calpain. Meat Sci 2008;79:77–85. [36] Morzel M, Terlouw C, Chambon C, Micol D, Picard B. Muscle proteome and meat eating qualities of Longissimus thoracis of “Blonde d'Aquitaine” young bulls: a central role of HSP27 isoforms. Meat Sci 2008;78:297–304. [37] Milan D, Woloszyn N, Yerle M, Le RP, Bonnet M, Riquet J, et al. Accurate mapping of the “acid meat” RN gene on genetic and physical maps of pig chromosome 15. Mamm Genome 1996;7:47–51. [38] Le RP, Naveau J, Elsen JM, Sellier P. Evidence for a new major gene influencing meat quality in pigs. Genet Res 1990;55: 33–40. [39] Bonneau M, Lebret B. Production systems and influence on eating quality of pork. Meat Sci 2010;84:293–300. [40] Robic A, Larzul C, Bonneau M. Genetic and metabolic aspects of androstenone and skatole deposition in pig adipose tissue: a review. Genet Sel Evol 2008;40:129–43. [41] Skinner TM, Doran E, McGivan JD, Haley CS, Archibald AL. Cloning and mapping of the porcine cytochrome-p450 2E1 gene and its association with skatole levels in the domestic pig. Anim Genet 2005;36:417–22. [42] Lee GJ, Archibald AL, Law AS, Lloyd S, Wood J, Haley CS. Detection of quantitative trait loci for androstenone, skatole and boar taint in a cross between Large White and Meishan pigs. Anim Genet 2005;36:14–22. [43] Duijvesteijn N, Knol EF, Merks JW, Crooijmans RP, Groenen MA, Bovenhuis H, et al. A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6. BMC Genet 2010;11:42. [44] Grindflek E, Berget I, Moe M, Oeth P, Lien S. Transcript profiling of candidate genes in testis of pigs exhibiting large differences in androstenone levels. BMC Genet 2010;11:4. [45] Moe M, Lien S, Bendixen C, Hedegaard J, Hornshoj H, Berget I, et al. Gene expression profiles in liver of pigs with extreme high and low levels of androstenone. BMC Vet Res 2008;4:29. [46] Sentandreu MA, Armenteros M, Calvete JJ, Ouali A, Aristoy MC, Toldra F. Proteomic identification of actin-derived oligopeptides in dry-cured ham. J Agric Food Chem 2007;55: 3613–9. [47] Theron L, Chevarin L, Robert N, Dutertre C, Sante-Lhoutellier V. Time course of peptide fingerprints in semimembranosus and biceps femoris muscles during Bayonne ham processing. Meat Sci 2009;82:272–7. [48] D'Alessandro A, Scaloni A, Zolla L. Human milk proteins: an interactomics and updated functional overview. J Proteome Res 2010;9:3339–73.
J O U RN A L OF P R O TE O MI CS 74 ( 20 1 1 ) 2 8 2–2 9 3
[49] Lonnerdal B. Bioactive proteins in human milk: mechanisms of action. J Pediatr 2010;156:S26–30. [50] O'Donnell R, Holland JW, Deeth HC, Alewood P. Milk proteomics. Int Dairy J 2004;14:1013–23. [51] Bianchi L, Puglia M, Landi C, Matteoni S, Perini D, Armini A, et al. Solubilization methods and reference 2-DE map of cow milk fat globules. J Proteomics 2009;72:853–64. [52] D'Amato A, Bachi A, Fasoli E, Boschetti E, Peltre G, Senechal H, et al. In-depth exploration of cow's whey proteome via combinatorial peptide ligand libraries. J Proteome Res 2009;8: 3925–36. [53] Reinhardt TA, Lippolis JD. Bovine milk fat globule membrane proteome. J Dairy Res 2006;73:406–16. [54] Wu WZ, Wang XQ, Wu GY, Kim SW, Chen F, Wang JJ. Differential composition of proteomes in sow colostrum and milk from anterior and posterior mammary glands. J Anim Sci 2010;88:2657–64. [55] Caroli A, Rizzi R, Luhken G, Erhardt G. Short communication: milk protein genetic variation and casein haplotype structure in the Original Pinzgauer cattle. J Dairy Sci 2010;93: 1260–5. [56] Chamberlain AJ, McPartlan HC, Goddard ME. The number of loci that affect milk production traits in dairy cattle. Genetics 2007;177:1117–23. [57] Ganai NA, Bovenhuis H, van Arendonk JA, Visker MH. Novel polymorphisms in the bovine beta-lactoglobulin gene and their effects on beta-lactoglobulin protein concentration in milk. Anim Genet 2009;40:127–33. [58] Singh K, Erdman RA, Swanson KM, Molenaar AJ, Maqbool NJ, Wheeler TT, et al. Epigenetic regulation of milk production in dairy cows. J Mammary Gland Biol Neoplasia 2010;15:101–12. [59] Picariello G, Ferranti P, Mamone G, Roepstorff P, Addeo F. Identification of N-linked glycoproteins in human milk by hydrophilic interaction liquid chromatography and mass spectrometry. Proteomics 2008;8:3833–47. [60] Wilson NL, Robinson LJ, Donnet A, Bovetto L, Packer NH, Karlsson NG. Glycoproteomics of milk: differences in sugar epitopes on human and bovine milk fat globule membranes. J Proteome Res 2008;7:3687–96. [61] Holland JW, Deeth HC, Alewood PF. Proteomic analysis of kappa-casein micro-heterogeneity. Proteomics 2004;4: 743–52. [62] Kjeldsen F, Savitski MM, Nielsen ML, Shi L, Zubarev RA. On studying protein phosphorylation patterns using bottom-up LC–MS/MS: the case of human alpha-casein. Analyst 2007;132:768–76. [63] Wedholm A, Moller HS, Stensballe A, Lindmark-Mansson H, Karlsson AH, Andersson R, et al. Effect of minor milk proteins in chymosin separated whey and casein fractions on cheese yield as determined by proteomics and multivariate data analysis. J Dairy Sci 2008;91:3787–97. [64] D'Ambrosio C, Arena S, Salzano AM, Renzone G, Ledda L, Scaloni A. A proteomic characterization of water buffalo milk fractions describing PTM of major species and the identification of minor components involved in nutrient delivery and defense against pathogens. Proteomics 2008;8: 3657–66. [65] Johnson ME, Lucey JA. Major technological advances and trends in cheese. J Dairy Sci 2006;89:1174–8. [66] Guarino C, Fuselli F, La MA, Longo L, Faberi A, Marianella RM. Peptidomic approach, based on liquid chromatography/electrospray ionization tandem mass spectrometry, for detecting sheep's milk in goat's and cow's cheeses. Rapid Commun Mass Spectrom 2010;24: 705–13. [67] Ginger MR, Grigor MR. Comparative aspects of milk caseins. Comp Biochem Physiol B Biochem Mol Biol 1999;124:133–45. [68] Panchaud A, Kussmann M, Affolter M. Rapid enrichment of bioactive milk proteins and iterative, consolidated protein
[69]
[70]
[71]
[72]
[73]
[74] [75]
[76]
[77]
[78]
[79]
[80]
[81]
[82] [83]
[84] [85] [86]
[87]
291
identification by multidimensional protein identification technology. Proteomics 2005;5:3836–46. Hogarth CJ, Fitzpatrick JL, Nolan AM, Young FJ, Pitt A, Eckersall PD. Differential protein composition of bovine whey: a comparison of whey from healthy animals and from those with clinical mastitis. Proteomics 2004;4: 2094–100. Affolter M, Grass L, Vanrobaeys F, Casado B, Kussmann M. Qualitative and quantitative profiling of the bovine milk fat globule membrane proteome. J Proteomics 2010;73:1079–88. Cavaletto M, Giuffrida MG, Conti A. Milk fat globule membrane components — a proteomic approach. Adv Exp Med Biol 2008;606:129–41. Reinhardt TA, Lippolis JD. Developmental changes in the milk fat globule membrane proteome during the transition from colostrum to milk. J Dairy Sci 2008;91:2307–18. Smolenski G, Haines S, Kwan FY, Bond J, Farr V, Davis SR, et al. Characterisation of host defence proteins in milk using a proteomic approach. J Proteome Res 2007;6:207–15. Kilara A, Panyam D. Peptides from milk proteins and their properties. Crit Rev Food Sci Nutr 2003;43:607–33. Madureira AR, Tavares T, Gomes AMP, Pintado ME, Malcata FX. Invited review: physiological properties of bioactive peptides obtained from whey proteins. J Dairy Sci 2010;93: 437–55. Stelwagen K, Carpenter E, Haigh B, Hodgkinson A, Wheeler TT. Immune components of bovine colostrum and milk. J Anim Sci 2009;87:3–9. Danielsen M, Pedersen LJ, Bendixen E. An in vivo characterization of colostrum protein uptake in porcine gut during early lactation. J Proteomics in press, doi:10.1016/j. jprot.2010.08.011. Kennedy S, Moffett D, McNeilly F, Meehan B, Ellis J, Krakowka S, et al. Reproduction of lesions of postweaning multisystemic wasting syndrome by infection of conventional pigs with porcine circovirus type 2 alone or in combination with porcine parvovirus. J Comp Pathol 2000;122:9–24. Opriessnig T, Patterson AR, Elsener J, Meng XJ, Halbur PG. Influence of maternal antibodies on efficacy of porcine circovirus type 2 (PCV2) vaccination to protect pigs from experimental infection with PCV2. Clin Vaccine Immunol 2008;15:397–401. Viguier C, Arora S, Gilmartin N, Welbeck K, O'Kennedy R. Mastitis detection: current trends and future perspectives. Trends Biotechnol 2009;27:486–93. Rua-Domenech R, Goodchild AT, Vordermeier HM, Hewinson RG, Christiansen KH, Clifton-Hadley RS. Ante mortem diagnosis of tuberculosis in cattle: a review of the tuberculin tests, gamma-interferon assay and other ancillary diagnostic techniques. Res Vet Sci 2006;81:190–210. Corry JEL, Hinton MH. Zoonoses in the meat industry: a review. Acta Vet Hung 1997;45:457–79. Eckersall PD, Saini PK, McComb C. The acute phase response of acid soluble glycoprotein, alpha(1)-acid glycoprotein, ceruloplasmin, haptoglobin and C-reactive protein, in the pig. Vet Immunol Immunopathol 1996;51:377–85. Eckersall PD. Acute phase protein: biomarkers of disease in cattle and sheep. Cattle Practice 2007;15:240–3. Wait R, Miller I, Eberini I, Cairoli F, Veronesi C, Battocchio M, et al. serum. Electrophoresis 2002;23:3418–27. Talamo F, D'Ambrosio C, Arena S, Del Vecchio P, Ledda L, Zehender G, et al. Proteins from bovine tissues and biological fluids: defining a reference electrophoresis map for liver, kidney, muscle, plasma and red blood cells. Proteomics 2003;3:440–60. Miller I, Wait R, Sipos W, Gemeiner M. A proteomic reference map for pig serum proteins as a prerequisite for diagnostic applications. Res Vet Sci 2009;86:362–7.
292
J O U RN A L OF P R O TE O MI CS 7 4 (2 0 1 1 ) 2 8 2–2 9 3
[88] Cairoli F, Battocchio M, Veronesi MC, Brambilla D, Conserva F, Eberini I, et al. Serum protein pattern during cow pregnancy: acute-phase proteins increase in the peripartum period. Electrophoresis 2006;27:1617–25. [89] Draisci R, Montesissa C, Santarnaria B, DAmbrosio C, Ferretti G, Merlanti R, et al. Integrated analytical approach in veal calves administered the anabolic androgenic steroids boldenone and boldione: urine and plasma kinetic profile and changes in plasma protein expression. Proteomics 2007;7:3184–93. [90] Mitchell GB, Clark ME, Caswell JL. Alterations in the bovine bronchoalveolar lavage proteome induced by dexamethasone. Vet Immunol Immunopathol 2007;118:283–93. [91] Mitchell GB, Clark ME, Siwicky M, Caswell JL. Stress alters the cellular and proteomic compartments of bovine bronchoalveolar lavage fluid. Vet Immunol Immunopathol 2008;125:111–25. [92] Hochstrasser DF, Frutiger S, Wilkins MR, Hughes G, Sanchez JC. Elevation of apolipoprotein E in the CSF of cattle affected by BSE. FEBS Lett 1997;416:161–3. [93] Nomura S, Miyasho T, Maeda N, Doh-ura K, Yokota H. Autoantibody to glial fibrillary acidic protein in the sera of cattle with bovine spongiform encephalopathy. Proteomics 2009;9:4029–35. [94] Thongboonkerd V, Chiangjong W, Mares J, Moravec J, Tuma Z, Karvunidis T, et al. Altered plasma proteome during an early phase of peritonitis-induced sepsis. Clin Sci (Lond) 2009;116:721–30. [95] Heegaard PM, Klausen J, Nielsen JP, Gonzalez-Ramon N, Pineiro M, Lampreave F, et al. Haptoglobin, C-reactive protein, major acute phase protein and serum amyloid A protein are sensitive indicators of infection. Comp Biochem Physiol B Biochem Mol Biol 1998;119:365–73. [96] Gutierrez AM, Miller I, Hummel K, Nobauer K, Martinez-Subiela S, Razzazi-Fazeli E, et al. Proteomic analysis of porcine saliva. Vet J in press, doi:10.1016/j. tvjl.2009.12.020. [97] Bradley AJ, Leach KA, Breen JE, Green LE, Green MJ. Survey of the incidence and aetiology of mastitis on dairy farms in England and Wales. Vet Rec 2007;160:253–7. [98] Boehmer JL, Bannerman DD, Shefcheck K, Ward JL. mastitis. J Dairy Sci 2008;91:4206–18. [99] Yang YX, Zhao XX, Zhang Y. Proteomic analysis of mammary tissues from healthy cows and clinical mastitic cows for identification of disease-related proteins. Vet Res Commun 2009;33:295–303. [100] Green MJ, Leach KA, Breen JE, Green LE, Bradley AJ. National intervention study of mastitis control in dairy herds in England and Wales. Vet Rec 2007;160:287–93. [101] Taverna F, Negri A, Piccinini R, Zecconi A, Nonnis S, Ronchi S, et al. isolated from bovine mastitis case by a proteomic approach. Vet Microbiol 2007;119:240–7. [102] Tedeschi G, Taverna F, Negri A, Piccinini R, Nonnis S, Ronchi S, et al. isolated from sub-clinical mastitis. Vet Microbiol 2009;134:388–91. [103] Lalles JP, Bosi P, Smidt H, Stokes CR. Nutritional management of gut health in pigs around weaning. Proc Nutr Soc 2007;66:260–8. [104] Milligan BN, Fraser D, Kramer DL. Within-litter birth weight variation in the domestic pig and its relation to pre-weaning survival, weight gain, and variation in weaning weights. Livest Prod Sci 2002;76:181–91. [105] Wang X, Wu W, Lin G, Li D, Wu G, Wang J. Temporal proteomic analysis reveals continuous impairment of intestinal development in neonatal piglets with intrauterine growth restriction. J Proteome Res 2010;9:924–35. [106] Danielsen M, Hornshoj H, Siggers RH, Jensen BB, van Kessel AG, Bendixen E. Effects of bacterial colonization on the porcine intestinal proteome. J Proteome Res 2007;6:2596–604.
[107] Wang X, Ou D, Yin J, Wu G, Wang J. Proteomic analysis reveals altered expression of proteins related to glutathione metabolism and apoptosis in the small intestine of zinc oxide-supplemented piglets. Amino Acids 2009;37: 209–18. [108] Jiang P, Siggers JL, Ngai HH, Sit WH, Sangild PT, Wan JM. The small intestine proteome is changed in preterm pigs developing necrotizing enterocolitis in response to formula feeding. J Nutr 2008;138:1895–901. [109] Danielsen M, Thymann T, Jensen BB, Jensen ON, Sangild PT, Bendixen E. Proteome profiles of mucosal immunoglobulin uptake in inflamed porcine gut. Proteomics 2006;6:6588–96. [110] Perez RG, Hastings TG. Could a loss of alpha-synuclein function put dopaminergic neurons at risk? J Neurochem 2004;89:1318–24. [111] Yamada M, Iwatsubo T, Mizuno Y, Mochizuki H. Overexpression of alpha-synuclein in rat substantia nigra results in loss of dopaminergic neurons, phosphorylation of alpha-synuclein and activation of caspase-9: resemblance to pathogenetic changes in Parkinson's disease. J Neurochem 2004;91:451–61. [112] Guilbault C, Saeed Z, Downey GP, Radzioch D. Cystic fibrosis mouse models. Am J Respir Cell Mol Biol 2007;36:1–7. [113] Phillips RW, Panepinto LM, Spangler R, Westmoreland N. Yucatan miniature swine as a model for the study of human diabetes mellitus. Diabetes 1982;31:30–6. [114] Larsen MO, Rolin B. Use of the Gottingen minipig as a model of diabetes, with special focus on type 1 diabetes research. ILAR J 2004;45:303–13. [115] Fadista J, Nygaard M, Holm LE, Thomsen B, Bendixen C. A snapshot of CNVs in the pig genome. PLoS ONE 2008;3. [116] Mote BE, Rothschild MF. Cracking the genomic piggy bank: identifying secrets of the pig genome. Genome Dyn 2006;2: 86–96. [117] Hornshoj H, Conley LN, Hedegaard J, Sorensen P, Panitz F, Bendixen C. Microarray expression profiles of 20000 genes across 23 healthy porcine tissues. PLoS ONE 2007:2. [118] Vajta G, Zhang YH, Machaty Z. Somatic cell nuclear transfer in pigs: recent achievements and future possibilities. Reprod Fertil Dev 2007;19:403–23. [119] Aigner B, Renner S, Kessler B, Klymiuk N, Kurome M, Wunsch A, et al. Transgenic pigs as models for translational biomedical research. J Mol Med 2010;88:653–64. [120] Patterson JK, Lei XG, Miller DD. The pig as an experimental model for elucidating the mechanisms governing dietary influence on mineral absorption. Exp Biol Med 2008;233:651–64. [121] Rothkotter HJ. Anatomical particularities of the porcine immune system — a physician's view. Dev Comp Immunol 2009;33:267–72. [122] Domeneghini C, Di Giancamillo A, Arrighi S, Bosi G. Trophic feed additives and their effects upon the gut structure and intestinal metabolism State of the art in the pig, and perspectives towards humans. Histol Histopathol 2006;21: 273–83. [123] Niba AT, Beal JD, Kudi AC, Brooks PH. Bacterial fermentation in the gastrointestinal tract of non-ruminants: influence of fermented feeds and fermentable carbohydrates. Trop Anim Health Prod 2009;41:1393–407. [124] Butler JE, Lager KM, Splichal I, Francis D, Kacskovics I, Sinkora M, et al. The piglet as a model for B cell and immune system development. Vet Immunol Immunopathol 2009;128:147–70. [125] Pang XY, Hua XG, Yang Q, Ding DH, Che CY, Cui L, et al. Inter-species transplantation of gut microbiota from human to pigs. ISME J 2007;1:156–62. [126] Kragh CL, Lund LB, Febbraro F, Hansen HD, Gai WP, Richter-Landsberg C, et al. Mechanisms in synucleinopathies — intra and intercellular signaling. J Neurochem 2009;110:62.
J O U RN A L OF P R O TE O MI CS 74 ( 20 1 1 ) 2 8 2–2 9 3
[127] Frandsen PM, Madsen LB, Bendixen C, Larsen K. Porcine gamma-synuclein: molecular cloning, expression analysis, chromosomal localization and functional expression. Mol Biol Rep 2009;36:971–9. [128] Matsuyama N, Hadano S, Onoe K, Osuga H, Showguchi-Miyata J, Gondo Y, et al. Identification and characterization of the miniature pig Huntington's disease gene homolog: evidence for conservation and polymorphism in the CAG triplet repeat. Genomics 2000;69: 72–85. [129] Spurlock ME, Gabler NK. The development of porcine models of obesity and the metabolic syndrome. J Nutr 2008;138: 397–402. [130] Barcelo-Batllori S, Gomis R. Proteomics in obesity research. Proteomics: Clin Appl 2009;3:263–78. [131] Nowacka-Woszuk J, Szczerbal I, Fijak-Nowak H, Switonski M. Chromosomal localization of 13 candidate genes for human obesity in the pig genome. J Appl Genet 2008;49:373–7. [132] Grapes L, Rothschild MF. Investigation of a QTL region for loin eye area and fatness on pig chromosome 1. Mamm Genome 2006;17:657–68. [133] Wimmers K, Fiedler I, Hardge T, Murani E, Schellander K, Ponsuksili S. QTL for microstructural and biophysical muscle properties and body composition in pigs. BMC Genet 2006;7:15. [134] Bell LN, Lee L, Saxena R, Bemis KG, Wang M, Theodorakis JL, et al. Serum proteomic analysis of diet-induced steatohepatitis and metabolic syndrome in the Ossabaw
[135]
[136]
[137]
[138]
[139]
[140] [141]
[142]
293
miniature swine. Am J Physiol Gastrointest Liver Physiol 2010;298:G746–54. Caperna TJ, Shannon AE, Garrett WM. A gel-based reference map of the porcine hepatocyte proteome. Domest Anim Endocrinol 2008;35:142–56. Lametsch R, Bendixen E. Proteome analysis applied to meat science: characterizing post mortem changes in porcine muscle. J Agric Food Chem 2001;49:4531–7. Jalili PR, Dass C. Proteome analysis in the bovine liquid adrenal medulla using chromatography with tandem mass spectrometry. Rapid Commun Mass Spectrom 2004;18:1877–84. Tsujita T, Kang D, Moon MH, Ohno N, Inoue T, Matsumoto M, et al. Large-scale identification by shotgun proteomics of proteins expressed in porcine liver and salivary gland. Zool Sci 2008;25:129–38. Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, Domon B, et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat Meth 2010;7:43–6. Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. by targeted proteomics. Cell 2009;138:795–806. Deutsch EW, Lam H, Aebersold R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep 2008;9:429–34. Huttenhain R, Malmstrom J, Picotti P, Aebersold R. Perspectives of targeted mass spectrometry for protein biomarker verification. Curr Opin Chem Biol 2009;13:518–25.
