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Relationships between genetic change and infectious disease in domestic livestock Pieter W. Knap1 and Stephen C. Bishop2 1 PIC Group, Roslin Institute (Edinburgh) and 2Roslin Institute (Edinburgh), Roslin, Midlothian EH25 9PS, United Kingdom Abstract The relations between genetic change in domestic livestock and infectious disease (including both its epidemiology and the animal's reaction to it) are examined. The overall picture is confusing because there are different, and seemingly unrelated, ways of considering the issue. An attempt is made to put these together into a more general framework. Four processes of particular interest are distinguished and discussed in more detail: (i) the way a population's genetic potential for immunocompetence can be changed by breeding, (ii) the way an animal's immunocompetence is influenced by that animal's production potential, in combination with the environmental resources that are available to it at a given time, (iii) the way the disease status of an animal (and a population of animals) is influenced by its immunocompetence, and (iv) the way the production level of an animal is influenced by activation of its immune system. Ultimately, all four processes influence the realised level of production. This comes down to four questions that need to be addressed: (i) can we use genetic variation in immunocompetence in animal breeding? (ii) does a higher production potential (today's direction of breeding) have a negative impact on immunocompetence? (iii) does improved immunocompetence result in improved health? and (iv) how large is the negative impact of disease on production? Introduction The second half of the 20th century has witnessed a strong intensification of domestic livestock production conditions, and an equally strong increase in animal densities. Perhaps as a direct result of these factors, the spread of infectious diseases has become the major factor to jeopardise the animal production sector in the western world. At the same time, livestock breeding programmes have mainly focused on creating genetic change in production traits. The contributions of McKay et al. (2000), Merks (2000), Preisinger (2000) and McGuirk (2000) in this volume show that they have been very successful in doing so in all important livestock species, and that we must expect a continuation, if not an acceleration, of genetic trends into the next few decades. The animal production sector has mainly relied on medication, vaccination and management techniques for the control of animal health, and as a consequence health-related traits have played a minor role in practical animal breeding. The classical illustration of this is the case of Marek's disease in chickens. Commercial poultry breeders spent considerable effort on selection for resistance against this viral disease after the genetic background of such resistance was established in the 1940's (Gavora, 1990), but when vaccines became available in the 1970's (Spencer et al., 1972), that selection effort was commonly perceived as unjustified, and it was widely abandoned. Since the advent of molecular genetic technology in the late 1990's, resistance to Marek's disease is the subject of scientific interest again, this time in search of QTL (Bumstead, 1998; Vallejo et al., 1998). This coincides with a growing concern in western society that the use of vaccines and medicines in animal production should be reduced. The relationships between genetic change (either in production traits or in health-related traits) and infectious disease (either its epidemiology or the animal's reaction to it) have received little serious attention until very recently. This paper examines those relationships. The overall picture is confusing because there are at least four very different, and seemingly unrelated, ways of considering the issue; we attempt to put these together into a more general framework. 65 Terminology Throughout this text terms are used that need proper definition to avoid ambiguity. A host organism is susceptible to a pathogen when the pathogen is able to replicate in the host. For example, humans are not susceptible to Marek's virus. Resistance to (infection with) a pathogen is then equivalent to absence of susceptibility: "the pathogen cannot establish infection even after exposure by injection" which can be "the result of the absence of receptors [to the pathogen in the host's tissue] or the presence of specific gene products interfering with [pathogen] replication" (Schat and Davies, 2000). On the other hand, when a host organism is resistant to disease, it is possible that "infection becomes established and the pathogen replicates, but this does not result in the development of disease". In that particular context, disease is equivalent to pathology, the situation characterised by fever and often by dysfunction and/or damage of host tissue. This generally causes "disruptions in metabolism and deviations in nutrient requirements" (Klasing et al., 1991). That the resistant host organism does not develop the disease is because it is able to launch an immune response against the pathogen. The term immunocompetence is used here in a broad sense to indicate the capability of the host to launch an immune response of sufficient specificity and magnitude, roughly indicating the effective quality of the host's immune system. This implies that animals tend to vary in their genetic potential for immunocompetence. The effects of pathology, and the resources required to launch an immune response, both lead to metabolic costs for the host organism. The term resilience refers to "the host's ability to maintain a reasonable level of productivity in the face of a [disease] challenge" (Albers et al. 1987; Coop and Kyriazakis, 1999), i.e. to maintain the situation where those metabolic costs do not lead to a significant reduction of production-related metabolic processes, which is of obvious relevance in animal production. The term environment is used here in its genetical context: it denotes everything that is not under control of the animal's genes. This includes nutrition and health care. Relationships between genetic change and infectious disease We distinguish four processes of particular interest: (i) the way a population's genetic potential for immunocompetence can be changed by breeding, (ii) the way an animal's immunocompetence is influenced by its production potential, in combination with the environmental resources that are available to it at a given time, (iii) the way the disease status of an animal (and a population of animals) is inP immunocompetence fluenced by its immunocompetence, and E resources (iv) the way the production level of an (iii) (ii) animal is influenced by activation of its immune system. These processes are indicated in Figure 1, which shows the main relationships among the animal's genetic potential (for immunocompetence and production traits), its environment (in terms of the available resources and the epidemiology of the prevalent infectious disease), and its phenotypic expression of genetic potential (in terms of immunocompetence and production traits). The factor that connects the four processes is the influence of environmental conditions on the relation between the genetic potential for production traits and the genetic potential for immunocompetence. The most striking effect of this is G production potential P production (iv) G immunocompetence potential (i) E epidemiology immune system activation disease status Figure 1 The main genetic (G), environmental (E) and phenotypic (P) entities that play a role in the relation between genetic change in domestic livestock and infectious disease. The processes (i) to (iv) are described in the text. 66 environmental sensitivity of genetic correlations. Ultimately, all four processes influence the realised level of production. A main task for the future is to fit the four together into a comprehensive framework that is accessible for animal breeders, nutritionists, epidemiologists and veterinarians. (i) Genetic change in immunocompetence 5 4 selection response (titer) Pinard et al. (1992) report on a selection experiment in which 37-day old chickens were immunised with sheep red blood cells, the selection criterion being the total antibody titer in the blood plasma five days after immunisation. Divergent (high and low titer) lines and an unselected control line were maintained for nine generations. The development of the selection criterion over generations is shown in Figure 2. These results make it clear that high or low immunocompetence can be selected for; the realised heritabilities of the selection traits in this experiment tended to increase over generations up to 0.40 and 0.44 in the high and low lines, respectively. The two lines differed in the frequencies of certain MHC haplotypes, and these were responsible for a significant part of the within-line variation of antibody production (Pinard et al., 1993a). The authors stress that "interactions between MHC genes and background genes have been extensively demonstrated" earlier, and conclude that their results "are in agreement with a polygenic control of the antibody response" to sheep red blood cells. 3 2 1 0 -1 -2 -3 -4 -5 0 5 10 15 cumulative selection differential (titer) 20 Figure 2 Direct phenotypic selection response of antibody titers in chickens after immunisation with sheep red blood cells, in relation to cumulative selection differentials over nine generations of selection. Data of the divergent selection lines are expressed as deviations from an unselected control line. Data from Pinard et al. (1992). When birds of the ninth generation of these lines were infected with Marek's virus at one day of age, the low titer line showed 70 % mortality up to 17 weeks of age compared to 43 % in the control line and 41 % in the high titer line (Pinard et al., 1993b). Mortality developed significantly faster in the order low > control > high line (38, 18 and 12 % mortality at 9 weeks, respectively). Although the heritability of resistance against Marek's virus in these lines was found to vary from 0.45 to 0.78 (and somewhat higher when adjusting for the effect of the MHC genotype), no clear genetic relationship between antibody production and resistance to Marek's virus could be established within lines. These findings provide a convenient illustration of the observations made by Nicholas (1996): "genetic variation in hosts for resistance to pathogens and parasites [… ] is present in most [… ] populations of hosts in relation to most [… ] parasites and pathogens that have been investigated. The genetic variation exists between and within breeds, and is usually multifactorial. Among the many documented examples are genetic variation for resistance to various bacteria, viruses and protozoa in chickens; [… ] to bacteria in turkeys; [… ] to bacteria, viruses and protozoa in fish; [...] to insects, bacteria, nematodes and various viruses in sheep; [… ] to bacteria in pigs; and [… ] to arthropods, nematodes, trypanosomes and bacteria in cattle [… ] The most obvious way to exploit this variation is to select for increased resistance. However, there is a major limitation to this approach, namely that the whole population has to be deliberately exposed to the pathogen or parasite". Rothschild (1991) evaluated the consequences of selection programmes that "require exposure to the disease" of interest, and implied that such selection for resistance to specific diseases by sibling challenge routines and 67 comparable systems is expensive, time-consuming, information-demanding and inconvenient. This would hold especially for intensive production systems (pigs, poultry), extensively farmed ruminants are often continuously exposed to the macroparasites that are their most important pathogens. Because of such limitations, much effort has been directed to find other ways to exploit genetic variation for disease resistance than by direct selection on the phenotype of actual interest. One of these ways is selection for "general immunocompetence", usually measured as the antibody production triggered by immunisation with an non-pathogenic substance such as sheep red blood cells, mollusc hemocyanin, chicken eggwhite lysozyme, bacterial lipopolysaccharide, etc. Another factor motivating the quest for improved general immunocompetence is that it is a desirable feature anyway, especially when the sector is not dominated by a small number of important diseases (so that the "phenotype of actual interest" cannot be clearly defined). In that respect, the above mentioned absence of a genetic relationship between the antibody production of Pinard's chickens and their resistance to a specific disease (i.e. Marek's) is at first sight disappointing. Pinard et al. (1993b) conclude that "indirect selection for resistance to Marek's disease by selecting for a unique immune parameter, which would not require exposure to the disease, does not seem feasible". This conclusion may be premature, as described below. Wilkie et al. (1998) discuss a similar selection experiment in pigs, in which the selection criterion was a BLUP-based selection index of four immunocompetence indicator traits (Mallard et al., 1992). When pigs of the fifth generation of these lines were infected with Mycoplasma hyorhinis, the high immunocompetence line developed significantly less severe peritonitis and pleuritis than the low line, but significantly more severe arthritis and similarly severe pericarditis. The authors note that their indicator traits were prone to strong fluctuations in between-animal variation, and that this "undoubtedly led to substantial variation in relative weighting on the four traits across generations" (Mallard et al., 1998), which makes it difficult to follow what has been exactly selected for in this experiment. Wilkie and Mallard (2000) conclude that "it should be possible to adjust the selection index to minimize negative effects and optimize breeding citeria". From that rather optimistic point of view, the abovementioned disappointing results of Pinard's attempts to create specific resistance by selecting for a general immunocompetence indicator trait, may simply suggest that she started accidentally with the wrong indicator trait for the disease she was ultimately interested in, creating an improvement in another component of the immune system than would have been relevant for that specific pathogen. It would be interesting to measure the resistance of Mallard's high and low selection lines to a serious bacterial or viral disease, and correlate the results to her immunocompetence indicator traits within lines. Rothschild (1991) suggested an alternative approach, "to identify individual marker genes that are associated with disease resistance and improved immune response [… ] Once specific MHC alleles and other genes can be identified, they can be used in marker-assisted selection or transferred to create transgenic animals with improved disease resistance and immune responsiveness". A decade later, the transgenic route is still posing problems. Schat and Davies (2000) write: "Although the development of transgenic chickens with increased resistance to avian leukosis virus certainly demonstrated the feasibility of this approach, the use of transgenic techniques for improved resistance to disease is a long way from becoming applicable in commercial lines [… ] The techniques for transgenesis are available, but it remains a costly and time-consuming approach. Moreover, the results are not always positive when biological costs are taken into account". Likewise, the application of marker-assisted selection to increase the resistance to specific diseases is now proving to be difficult because the resistance to many diseases is polygenic, and it is not easy to find the right genes. Hence there seems to be little hope for true "genetic eradication" of many livestock diseases, the obvious exceptions being resistance mechanisms that rely on a single gene, such as the ones that code for intestinal receptors for F18 or K88 Escherichia coli in pigs (Edfors-Lilja and Wallgren, 2000). It seems that for the approaches to genetic improvement that target a single disease, the drawback is that there are so many other diseases (and alternative pathotypes of the same pathogen) waiting to take over the first position on the animal production industry's priority list. For the more general approaches, the drawbacks are that the various mechanisms of host immunity may be not, or may even be unfavourably, intercor68 related (Rothschild, 1991; Mallard et al., 1998), and that an increase of "general" immunocompetence does not necessarily lead to an increase in resistance to a specific disease (for more evidence see Biozzi et al., 1982). Nevertheless, commercial breeding companies have nowadays a keen interest in breeding for disease resistance, simply because every successful attempt may be very advantageous in terms of competitive advantage when marketed properly. Of course, the recently re-developing interest in "breeding for disease resistance stems partly from awareness of the development of pathogen resistance to the therapeutic agent, which has led to calls for reduction in the use of antibiotics and other drugs throughout human and animal medicine" (Axford et al., 2000). This development of pathogen resistance is the simple result of artificial selection pressure placed on the pathogen by the exposure to the therapeutic, combined with the generally short generation intervals of pathogenic organisms. Such a reduction of drugs usage may require the development of (i) alternative therapeutic intervention methods, such as biological pest control, (ii) methods to reduce the need for therapy, such as modified animal management systems, and/or (iii) genetic resistance. An important question in this respect is if selection for increased host resistance will be able to elude the pathogens' inevitable co-evolutionary attempts to resist such increased resistance. See also Bishop and Gettinby (2000). (ii) Production potential and immunocompetence The actual expression of immunocompetence will depend in the first place on the animal's genetic potential for immunocompetence, as discussed in the previous section. But there is growing evidence for environmentdependent effects of the genetic potential for production traits on the actual expression of immunocompetence. Genotypes with high production potential (suitably dubbed "metabolic athletes" by Elsasser et al., 1999), when placed in an environment that is inadequate in terms of metabolic resources to support their intrinsically high production levels and maintain homeostasis, tend to allocate resources primarily towards production-related processes (see Coop and Kyriazakis, 1999). This will leave other metabolic functions with insufficient resources. When that environment is at the same time challenging in terms of infectiousness, the immune system may become constrained that way, which will lead to inadequate immune response to infection (Emmans et al., 2000, in this volume; Luiting, 1999; Knap and Luiting, 1999; Thorp and Luiting, 2000). Rauw et al. (1998) reviewed "undesirable side effects of selection for high production efficiency" in domestic livestock, and write: "Selection for high body weight [in broiler chickens and turkeys] has resulted in a correlated negative immune performance. Broilers selected for high growth rate showed lower antibody responses when challenged with sheep erythrocytes than a low BW line (Miller et al., 1992) and a randombred control line (Qureshi and Havenstein, 1994). Little or no differences were found [… ] in the non-adaptive components of the immune system. Nestor et al. (1996ab) found a significantly higher mortality in turkeys selected for high BW compared to a randombred control line in a natural outbreak of erysipelas (11.8 and 1.6 % respectively), and when challenged with either Pasteurella multocida (72.1 and 43.6 %) or Newcastle disease virus (32.5 and 15.8 %)". Likewise, Nir (1998) refers to selection studies for high and low antibody response to sheep red blood cells in poultry (Van der Zijpp, 1983; Siegel and Gross, 1980; Martin et al., 1990; Kreukniet et al., 1994; Praharaj et al., 1995), which show a negative relationship between antibody production and growth rate. At present, the formal literature provides very little quantitative information on this issue in other livestock species, but tendencies towards unfavourable relations between immunocompetence and lean growth capacity in growing pigs have been reported by Stahly et al. (1994), Frank et al. (1997), McComb et al. (1997) and Schinckel et al. (1998). The results of these authors are difficult to interpret, not only because there is little information about standard errors in the reports, but also because the described trends are not always consistent between traits. The most striking result is perhaps the mortality of the pigs of Frank et al. (1997). Their "lean" and "fat" genotypes (with on average 57.1 and 50.7 % carcass lean, respectively) showed 3.6 and 2.8 % mortality, respectively, in a "low immunostimulation" environment (segregated early weaning, disinfected 69 finishing facilities, etc.) and 18.5 and 5.6 % mortality, respectively, in a "high immunostimulation" environment (conventional weaning, "continuous flow finisher", etc.). Sinclair et al. (1999) studied the immune response against bovine herpes virus vaccine in dairy cows of high versus average genetic merit for milk production, and provisionally concluded that the high merit cows, when given a diet low in concentrates, "redirect resources from the maintenance of an adequate immune system to milk production in order to maintain advantages in milk yield". The average merit cows did not show any dependence of immune response on diet. BW (kg) Environmental sensitivity may have profound 2.5 effects on the results of genetic parameter estimation, dependent on the environment in which the data are collected or the genotype on which they are measured. An example, admittedly making use of data on a non-infectious disease, 2.0 is the study of the genetic relation between growth rate and susceptibility to ascites in broiler chickens by De Greef et al. (2000). The result from this study that is most relevant to the present discussion is the genetic correlation 1.5 between growth rate and arterial pressure index (API, the ratio of the weight of the right heart ventricle to the weight of both ventricles plus their septum). API is used in this study as a direct measurement of ascites, it had a genetic 1.0 correlation with mortality due to ascites of +0.84. The genetic correlation between API 1.4 1.6 1.0 1.2 and growth rate was estimated as rG = –0.26 ± log API 0.09 in the entire chicken population; but when the "non-diseased" subpopulation with API Figure 3 Body weight (BW) at 35 days of age in relavalues below the population mode (about 40 % tion to log-transformed arterial pressure index (API) in of the total number) was analysed separately, broiler chickens. The data is divided into subsets below the estimate changed to rG = +0.29 ± 0.20. The (? ) and above (o) the mode of API. Solid lines: linear observed phenotypes are in Figure 3. The regressions through the subsets, broken line: through authors concluded that (i) genetically, the faster the whole data. Data from De Greef et al. (2000). growing animals have a stronger susceptibility to ascites; (ii) susceptibility to ascites is only expressed in a challenging environment; and (iii) growth rate becomes depressed when ascites actually occurs. As a result, their animals with the highest genetic potential for growth rate showed below-average growth due to ascites; genetic correlation estimates will then depend on environmental conditions, and breeders will have to take this into account. Studies of resistance against intestinal nematodes in growing sheep have revealed a considerable variation in genetic correlation estimates between resistance and growth rate. Published estimates range from strongly favourable (Bishop et al., 1996), through moderately favourable or neutral (Albers et al., 1987; Bisset et al., 1992; Douch et al. 1995; Eady, 1998) to moderately unfavourable (McEwan et al., 1992, 1995). A part of these differences can be explained by differences in nutritional conditions, but a more important factor seems to be the parasite species: paradoxically, the more tissue-damaging and resource-demanding nematode species turn out to evoke less of an immune response in the host. Environmental sensitivity of high-production genotypes is, of course, a key issue in "The challenge of genetic change in animal production". At the same time, the current experimental evidence is rudimentary and sometimes anecdotal. Apart from the nematodiasis and ascites studies, all the above sources derived their genetic 70 contrasts from comparisons of different breeds or, at best, selection lines that were developed from the same base population. For the animal breeding industry to take relevant action on this issue would require information on the within-population variance and covariance of these traits (Knap and Luiting, 1999). Without any doubt, environmental sensitivity will be a major topic for fundamental research and simulation modelling during the coming decade. Given the current lack of quantitative data, research emphasis should be on the quantification of genetic variation and covariation within genotypes, rather than on revealing contrasts between breeds. (iii) Immunocompetence and disease status As we discussed in the section on Genetic change in immunocompetence above, breeding for improved immunocompetence in domestic livestock is a costly exercise. Therefore its implementation should be preceded by a proper cost-benefit analysis. The benefits of enhancing disease resistance in a breeding population can be studied by means of genetic-epidemiological models, which are being developed recently. These models translate (i) the infectiousness of a pathogen and (ii) the level of genetic resistance against it in a host population, into the disease status of that population. The latter can be described in terms of the minimum proportion of animals that must be resistant to protect the population from an epidemic, or the decline of the probability (and severity) of an epidemic as time (and selection) progresses, etc. The form of the genetic-epidemiological model will differ according to the type of pathogen and method of disease transmission, as well as the host species. For example, simple compartmental models describing the status of the host as susceptible, latent, infected or recovered are generally sufficient for bacterial and viral infections, whereas for more complex macroparasitic (such as nematode) infections, each stage of the parasitic lifecycle must be described – possibly for every host animal. Additionally, whether the infection is transmitted by direct animal contact or from an environmental reservoir of infection must also be modelled. For environmental sources of infection, scenarios exist where the animals' disease status feeds back to the environmental contamination causing a direct interaction between host resistance and disease epidemiology, as in nematode infections, or where the environmental source of infection is essentially infinite, as for trypanosomiasis. Published examples of genetic-epidemiological models for livestock include models of nematode infections in sheep (Bishop and Stear, 1997), scrapie in sheep (Stringer et al., 1998) and viral infections in pigs (MacKenzie and Bishop, 1999). 71 1.0 2.5 R0 2.0 no epidemic 0.8 0.6 R0 1.5 0.4 1.0 minor 0.2 0.5 major 0.0 0.0 0 30 10 20 time since start of selection (years) Figure 4 Simulated consequences of selection for resistance on R0 and on the probabilities of a major, minor or no epidemic. probability The model of the latter authors summarises the type of information obtainable from genetic-epidemiological models. For direct contact diseases, a useful parameter summarising the transmission dynamics is R0, the basic reproductive ratio, which describes the number of secondary infections expected from a primary infected individual. If R0 is greater than 1, an epidemic is expected. If the epidemic gains a foothold in the population and persists longer than a defined period it may be termed major, if it dies out naturally it may be defined minor. Using a stochastic model which models the disease transmission as a series of random events in time, MacKenzie (1999) was able to describe the dynamics of transmissible gastroenteritis epidemics in a structured pig farm. Assuming that selection for resistance to this disease is possible, and assuming an arbitrary rate of genetic progress, MacKenzie demonstrated how R0 and the probabilities of major/minor/no epidemics change as selection pro- gresses, should the infectious agent be present. This is shown in Figure 4. Additionally, she also showed how the duration and severity of the major, but not necessarily the minor, epidemics decreased with selection. The value of these models, in addition to the insight they provide, is as a tool to enable breeders to make rational decisions about breeding for disease resistance. The fundamental concepts to consider when making these decisions are the costs incurred in implementing a selection strategy (which may be large) compared to the beneficial consequences, as evaluated from the model. It must be demonstrated that breeding for resistance will be truly beneficial and serve to produce populations that, in a reasonable timescale, will no longer be susceptible to the disease. 1.0 resistant proportion In the case of single genes conferring resistance to a disease, genetic-epidemiological models can also be used to answer the important question of what proportion of the population must be resistant to protect the population as a whole against an epidemic. This problem was solved by MacKenzie and Bishop (1999) and is shown in Figure 5. The key result is that only for the most virulent diseases is it necessary for the whole population to be resistant. For many potential diseases the population can carry sizeable proportions of susceptible individuals without apparent detriment. This is analogous to vaccination strategies in human populations. 0.8 0.6 0.4 0.2 0.0 1 3 5 7 9 basic reproductive ratio (R0) Figure 5 The proportion of a population that must be resistant, in the case of single genes for resistance, to protect the population as a whole against an epidemic. (iv) Immune system activation and production The immune system can actively regulate the various components of nutrient metabolism in three ways (Klasing et al., 1991): (i) by direct neural connections to the central nervous system, which may trigger behavioural adaptations and/or release of hypothalamic and pituitary hormones; (ii) by release of hormones such as ACTH and thyrotropin by immune cells; (iii) most importantly, through leucocytic cytokines which trigger not only anorexia and fever but also processes like the upregulation of gluconeogenesis from glycogen, fatty acids and amino acids, accompanied by the downregulation of muscle protein deposition (and/or muscle proteolysis) to support this gluconeogenesis and to support acute-phase glycoprotein synthesis (Jepson et al., 1986). As a result, infection and the associated activation of the immune system will lead to a cascade of resourcereallocating processes in the host, most notably the following: (i) the production of acute-phase glycoproteins, immune cells and immunoglobulins, which requires extra protein synthesis; (ii) repair of damaged tissue, which may cause a strong increase in protein turnover rates and hence increase metabolism considerably (see Carli et al., 1989, for an example in post-surgery humans); (iii) fever, with the same metabolic effect as subcritical ambient temperature; (iv) depression of voluntary feed intake (anorexia), the process with the most dramatic effects on energy metabolism. Apart from fever (see below), the quantitative impact of these processes on nutrient metabolism is poorly documented. Demas et al. (1997) immunised mice with a non-pathogenic mollusc hemocyanin. Compared to non-immunised controls, immunised mice showed significantly increased specific antibody serum levels. On days 10 and 15 after immunisation they had developed a mild fever: their rectal temperatures were increased by 1.6 and 1.0 °C, and their metabolic rate (O2 consumption) by 30 %. This increase in body temperature would by itself cause a 7 to 20 % increase in metabolic rate (see below); the remaining 10 to 23 % increase in metabolic rate would then be due to immune system activation per se (mainly the protein synthesis processes (i) above). Fever constitutes an important "metabolic cost": a raise of body temperature by 1 °C causes an increase of metabolic rate by 5 to 13 % (Baracos et al., 1987; Van Dam et al., 1996ab; Demas et al., 1997). Further72 more, fever is usually accompanied by body protein catabolism. "Only after fever begins does the nitrogen balance become abruptly negative [… ] Daily losses [in humans] may then range from 2 to 23 g per day, depending on the presence of anorexia [… ] and the magnitude of hypermetabolism [… ] Early losses of nitrogen come chiefly from the so-called labile nitrogen pool, [… ] primarily the protein in skeletal muscle and other somatic tissues. If an infection enters a subacute or chronic phase, and if the body supplies of labile nitrogen become exhausted, daily losses of nitrogen begin to lessen, and the body enters a new relatively steady state [… ] Rapidly growing normal children typically exhibit a strongly positive nitrogen balance. If they become ill with an infection of mild to moderate severity, they may not revert to a negative nitrogen balance, but show a less positive balance instead" (Beisel, 1985). But overall energy partitioning may be only little affected in the absence of fever. Schrama et al. (1997) reviewed trials in which the energy metabolism of growing pigs was studied in relation to immunisation with Pasteurella multocida toxin and various non-pathogenic substances, none of which resulted in fever. The authors conclude: "In general, the presented data [… ] suggest that mild stress and/or disease first result in the reallocation of energy between different maintenance processes [i.e. the immunity-related maintenance processes are increased, and activity is reduced by a similar amount]. More severe stress and/or disease will result in a decreased feed intake, which often coincides with an increase in the total energy required for maintenance processes [… ] The reallocation induced by exposure to stressors can conflict with the reallocation of nutrients required for the animal to maintain its health status". Anorexia during immune system activation seems counterproductive, as the body would need more resources to deal with its elevated metabolism and with the nutrient requirements of the pathogen. Indeed, studies have been directed to the required "upgrade" of diet composition to keep production at the economically desired levels (Klasing et al., 1991; Stahly, 1996; Van Houtert, 1997), and attempts have been made to reduce the anorexia response by genetic selection (i.e. selection for increased resilience; Albers et al., 1987; Albers and Gray, 1989; Bisset et al., 1994, 1996). Kyriazakis et al. (1998) attempted to explain the paradox, which is especially significant from a production point of view, by suggesting that anorexia promotes an effective immune response in the host by removing possible immunotoxic effects of micronutrient excesses. All the above was summarised in terms of production traits by Stahly (1996), who discussed trials with young growing pigs subjected to high and low immune system activation (achieved by conventional weaning and medicated early weaning, respectively; Williams et al., 1997ab). He concludes that "minimizing the activation of the pig's immune system" in those trials resulted in higher ad libitum feed intake, growth rate, muscle development, and feed efficiency. Livestock nutrient metabolism has been modeled extensively (see Baldwin and Sainz, 1995; Black et al., 1995). Typically these simulation models deal with the resource requirements of the animal, given its genetic potential, in addition to the resource demands of various environmental "load" factors (most notably, nutrition and climate). Immunological costs have not been included in any of these models yet. Modeling the resource demands of an immune system activation in a similar manner would then require a quantitative description of the ways the immune system interacts with nutrient metabolism, in terms of the four resourcereallocating processes listed above (protein synthesis, tissue repair, fever, anorexia). This interaction is likely to depend on environmental factors (type of pathogen, other load factors, pathogen density in the case of macroparasites) and on animal-intrinsic factors (immunocompetence potential, nutritional status). Modeling these processes is of interest in an animal breeding context because the genetic evaluation of breeding animals is based evermore often on performance data that have been created in various environmental settings. The system to be described in such evaluations (e.g. a lactating cow, a growing pig, a laying hen) is non-linear in terms of its metabolic processes and their genetic background. When health-related factors are included in the description, the system also becomes strongly environment-variant. This makes the use of linear additive models to describe such systems questionable, and calls for the development of stochastic, dynamic, mechanistic simulation models. Similar arguments hold for the prediction of performance of a particular genotype in novel conditions. Ultimately, what needs to be modeled is genotype by health environment interaction. 73 In modeling, the distinction between clinical disease (with its extreme but usually short-lasting consequences) and subclinical disease (with its less extreme but chronic consequences) is important. Severe fever is a common aspect of clinical disease, and possibly the most important one in terms of resource demands. Because fever is usually not severe in subclinical disease conditions, the metabolically most important consequence of subclinical disease is likely to be anorexia. An interesting question is if there are interactive effects around resource reallocation in case of infection: do specific production-related processes become suppressed selectively? If so, the environment-variant aspects of the infectious scenario become even more relevant. Beisel (1985) writes: "… the function of [… ] host defensive mechanisms [… ] requires an ongoing capacity of body cells to synthesize new proteins. For this reason, any nutritional deficit or imbalance that influences protein synthetic functions can lead in turn to [… ] a weakening of host resistance", and Coop and Kyriazakis (1999) propose that resources are preferentially (not absolutely) allocated towards (i) maintenance of body protein, including repair, replacement and reaction to tissue damaged or lost due to infection; (ii) the processes of acquisition of immunity that precede the actual immune response; and (iii) growth and reproduction, in that order of preference. They conclude that "the ability to maintain relatively undepressed functions of growth and reproduction" (i.e. resilience) will depend on the extent of tissue damage due to infection, and also that this implies a strong influence of nutrition on the expression of immunocompetence. The tendency of modern highly productive genotypes to allocate resources preferentially towards production-related processes (see the section on Production potential and immunocompetence above) would not accord with Coop and Kyriazakis's (1999) order of preference. There may be a genotype-dependent trend here, which would make mechanistic modelling all the more attractive. Modeling can be done empirically or mechanistically: empirical models are much easier to develop, whereas mechanistic models are more generally applicable and provide more insight in the processes between input and output. Empirical modeling of the resource demands of an immune system activation would require measurement of the degree of immune system activation in animals in some infectious setting, and of the associated nutrient intakes and/or production performance. The latter measurements can then be regressed on the former ones, and the resulting prediction equation can be used to predict resource requirements in future conditions. Such analyses have been performed by Leathwick et al. (1992) and Bishop and Stear (1999) for nematode infections in sheep. The former authors developed a model to simulate the epidemiology of nematodiasis that distinguishes between gut tissue damage (in terms of "worm burden", the number of established parasites in the gut) and immune response (in terms of the number of infective larvae ingested daily) as the components responsible for the parasite's effect on the host's growth performance. The relevant equation in this model empirically predicts host weight loss (WL) as a function of cumulative daily worm burden (WB) and cumulative daily larvae intake (LI) as WL = a × WB + b × LI + c × WB × LI. The constants a, b and c are derived from regression analysis of field data. By contrast, the mechanistic approach would attempt to describe the host genotype in terms of (i) its resource requirements for acquisition of immunity to infection; (ii) its resource requirements for the actual expression of the immune response, including the effects of fever and anorexia; (iii) its increased protein turnover rates due to infection, all in relation to infection with a specific pathogen. For a particular host genotype in a particular environmental setting (nutrition, climate) and infected with a particular pathogen, a mechanistic model would then predict the extra resource requirements to maintain full expression of its production potential. Failing the fulfilment of these resource requirements, it would predict the realised sub-optimal production level. Such an approach would require the description of the infectious environment in terms of a few parameters that make it possible to quantify its metabolic load. It would also require much more quantitative information about the resource demands of the various components of immune system activation than is currently available. 74 Conclusions We can now return to Figure 1, and look again at the processes summarised there. The previous sections have shown that (i) there is, in principle, ample opportunity for changing a population's genetic potential for immunocompetence by breeding, because many of the related characteristics show substantial genetic variation between animals. However, direct selection on the target (resistant) phenotype is inconvenient in the intensive production systems because it requires challenge routines, and this makes it difficult to create genetic change in practice. Indirect selection on more general expressions of immunocompetence may provide an alternative, but requires detailed knowledge of the specific components to be targeted. Molecular genetic technology seems promising, especially for resistance mechanisms controlled by a single gene, but needs much more development for the many diseases that are dealt with by polygenic resistance mechanisms. Furthermore, (ii) information about the way an animal's immunocompetence is influenced by its production potential (in combination with the environmental resources that are available to it at a given time) is gradually emerging, although part of this information is still confusing. Although there are no signs, as yet, for immediate immunological disaster as the sole result of extreme selection for production traits, it seems clear that genotype by health environmental interactions should receive more specific attention. The environmental sensitivity of genetic (co)variances is particularly worrying, since it makes breeding value estimation much more complicated. Taken together, points (i) and (ii) suggest that animal breeders should extend their breeding objectives (conventionally emphasising production traits) with immunocompetence characteristics. The challenge here is to do so in a structured way rather than on an ad hoc basis, and this will require information on withinpopulation (co)variation. The way (iii) the disease status of an animal population is influenced by its immunocompetence profile can be modeled now, which has created a functional link between epidemiology and animal breeding. This is important because the eventual genetic outcome of points (i) and (ii) will have to be evaluated in its economic context, and that context is largely governed by epidemiology. As with most fitness-related traits (see Knap and Luiting, 1999), a successful incorporation of health-related traits into animal breeding programmes will demand a multidisciplinary approach. Genetic-epidemiological models provide a convenient vehicle for this approach. Ultimately, the economic values that will be required to set up the breeding objectives mentioned in the previous paragraph will have to be estimated by using such models. Finally, (iv) activation of an animal's immune system is likely to result in increasingly non-linear and environment-variant components of the metabolic processes that govern its production performance. Because breeding value estimation is increasingly based on production performance data from various environmental conditions, it becomes more and more important to estimate that production potential using models that can represent this non-linear and environment-variant system. The challenge here is to develop a model that can predict the above mentioned environmentally sensitive (co)variances, as opposed to treating them as a statistical nuisance factor. Such models would also make it possible to specify the environmental conditions (nutrition, climate, health) that would allow a high-performance genotype to express its full potential. The concerns expressed by Nir (1998) and Tamminga (2000, in this volume) about the possibilities of feeding and managing modern poultry and future dairy cattle genotypes in order to achieve just that, illustrate that we may expect a strong demand for such tools, perhaps in the first place to decide which genotypes are suitable for a given market. This review has indicated some strong needs for further information, as related to the processes in Figure 1: (i) quantification of the genetic relationships between immunocompetence and resistance to specific pathogens within lines; quantification of the co-evolutionary response of pathogen virulence to selection for host resistance; (ii) quantification of genetic variation and covariation between immunocompetence and production traits within lines; (iii) quantification at the population level of the infectiousness (i.e. R0) and impact on productivity of various diseases; use of this information to predict the consequences of altering disease resistance in terms of the probability and severity of infection, and how this translates into economic benefits; (iv) quantification of the resource demands of the various components of immune system activation; description 75 of the infectious environment in terms of a few parameters that make it possible to quantify its metabolic load. 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