Parasitic challenges

Experiments with abomasal parasites such as Teladorsagia (Ostertagia) circumcincta and Ostertagia ostertagi have shown that challenged animals grew slower than their pair fed controls (Sykes and Coop, Reference Sykes and Coop1977; Fox et al., Reference Fox, Gerrelli, Pitt, Jacobs, Gill and Gale1989). The average live weight gains of sheep challenged with Teladorsagia circumcincta were 0.79 of their pair fed controls (Sykes and Coop, Reference Sykes and Coop1977). Nitrogen balances performed at weeks 2 to 3, 7 to 8 and 12 to 13 post infection demonstrated time dependent effects on the nitrogen balance: live weight gains for the challenged sheep for weeks 2 to 8 were 0.66 of the pair-fed controls. Reductions in growth in naïve hosts are related to the time course of an infection, and may reflect the acquisition and expression of immunity (Coop and Kyriazakis, Reference Coop and Kyriazakis1999).

Mansour et al. (Reference Mansour, Rowan, Dixon and Carter1991 and Reference Mansour, Rowan, Dixon and Carter1992) challenged calves with either a single, or a trickle dose of Ostertagia ostertagi and found no difference in average growth rate between challenged calves and their pair-fed controls. The observation may have been due to the size of the challenge doses: the single dose was 609 larvae per kg body weight (BW) and the trickle dose was seven larvae per kg BW per day. Fox et al. (Reference Fox, Gerrelli, Pitt, Jacobs, Gill and Gale1989) using the same type of parasite, but a larger trickle dose (98 larvae per kg BW per day) found differences in growth rate between challenged calves and pair-fed controls. The effects of abomasal parasites on growth, beyond effects on VFI, depended on both the level of challenge and the stage of infection.

The immunological state of an animal, i.e. whether it is naïve to a pathogen or whether it has acquired immunity, needs to be accounted for when comparing rates of growth of challenged and healthy animals. Takhar and Farrell (Reference Takhar and Farrell1979b) found no differences in growth rate between immune chicks (data indicated increased VFI) and healthy pair-fed controls when re-challenged with Eimeria acervulina or Eimeria tenella. There were, however, clear differences in growth between pair-fed controls and immunologically naïve chicks. Immune animals may cope with larger challenge doses compared with immunologically naïve animals before effects on growth occur.

Kimambo et al. (Reference Kimambo, MacRae, Walker, Watt and Coop1988) challenged sheep with the intestinal parasite Trichostrongylus colubriformis and found the average dW/dt of the challenged sheep to be 0.11 and 0.83 of the pair-fed controls between weeks 6 to 13 and 13 to 20, respectively. As indicated by faecal egg counts and blood eosinophil counts the sheep were starting to express immunity somewhere during weeks 6 to 13. During weeks 13 to 20 the sheep were fully immune with faecal egg counts reduced to zero, but as this was a continuous, trickle challenge) blood eosinophil remained elevated. The reduction in growth during weeks 13 to 20 suggested a cost of expressing immunity. The findings of MacRae et al. (1979) are in quantitative agreement, and those of Symons and Jones (Reference Symons and Jones1975), Poppi et al. (Reference Poppi, MacRae, Brewer and Coop1986) and Datta et al. (Reference Datta, Nolan, Rowe and Gray1998) agree qualitatively.

Pair-feeding experiments with parasites other than those affecting the stomach and small intestine have also shown reductions in growth beyond those caused by reductions in food intake. The liver parasite Fasciola hepatica given at a low dose (three metacercariae per host) did not cause any significant differences in the rate of growth or body composition between parasitised sheep and their pair-fed controls (Sykes et al., Reference Sykes, Coop and Rushton1980). At higher doses (eight and 14 metacercariae), the challenge did have effects on growth compared with pair-fed controls. These findings are in support of those for abomasal parasites where reductions in growth, when compared with pair-fed controls, depended on the level of challenge.

Akinbamijo et al. (Reference Akinbamijo, Bennison, Romney, Wassink, Jaitner, Clifford and Dempfle1997) found that calves of two breeds (N'Dama and Gobra zebu bulls) challenged with a blood borne parasite (Trypanosoma congolense) grew slower than their pair-fed controls, but the two breeds were not affected to the same extent. Abbott et al. (Reference Abbott, Parkins and Holmes1985) also found effects of genotype (Finn Dorset v. Scottish Blackface sheep) and nutrition (high or low protein) on differences in growth rate between sheep challenged with Haemonchus contortus and their pair-fed controls. In the case of the effect of nutrition, the lower reduction in growth seen for the higher protein diet would have been associated with the extent of damage. The effect of host genotype is more difficult to explain. It may be the outcome of greater genetic resistance, an outcome of damage affecting a genotype to a lesser extent than others, or the outcome of genotypes used (growth potential and resistance) and the level of nutrition. A framework of growth during pathogen challenges would need to distinguish between these factors causing reductions in growth.

Bacterial challenges

Experiments with intravenously administered bacteria (Ruot et al., Reference Ruot, Breuille, Rambourdin, Bayle, Capitan and Obled2000; Papet et al., Reference Papet, Ruot, Breuille, Walrand, Farges, Vasson and Obled2002), lipopolysaccharide, LPS, (Steiger et al., Reference Steiger, Senn, Altreuther, Werling, Sutter, Kreuzer and Langhans1999), and local (nasal or oral) bacterial (Wannemacher et al., Reference Wannemacher, Powanda, Pekarek and Beisel1971; Powanda et al., 1972; Wannemacher et al., Reference Wannemacher, Powanda and Dinterman1974), or, bacterial antigen challenges (Klasing et al., Reference Klasing, Laurin, Peng and Fry1987; Arnold et al., Reference Arnold, Little and Rothwell1989) have shown reductions in growth that were not accounted for by reduced VFI. Challenges with LPS, while not a ‘true’ bacterial challenge, stimulate a similar kind of response, but of smaller and shorter magnitude, to that of bacterial challenges. As for macro-parasites these effects were dose dependent; Hunter and Grimble (Reference Hunter and Grimble1997) and Raina et al. (Reference Raina, Matsui and Jeejeebhoy2000) failed to demonstrate any reductions in growth that were not accounted for by reduced VFI in LPS challenged rats.

Klasing et al. (Reference Klasing, Laurin, Peng and Fry1987) challenged growing chicks with non-pathogenic antigens (sheep red blood cells or sephadex), bacterial antigens (Escherichia coli or Salmonella typhimurium LPS) or heat killed Staphylococcus aureus. Pair feeding was done four times per day to account for rapid effects of bacterial antigens on VFI. The relative growth rates (challenged growth rate/control growth rate) of chicks challenged with non-pathogenic antigens (0.90) and pathogenic antigens (0.83) were different. This may suggest that a host may respond in relation to the kind of pathogen challenging it (for a review see Baxter and Hodgkin (Reference Baxter and Hodgkin2002)). The more virulent pathogen may have stimulated a greater immune response, rather than causing more damage to the host and through a larger resource requirement of the greater immune responses had greater reductions in growth (see below).

Viral challenges

A few experiments were identified that considered the effects of viral challenges on growth in pair-fed animals compared with parasitic and bacterial models. Zijlstra et al. (Reference Zijlstra, Donovan, Odle, Gelberg, Petschow and Gaskins1997) found that pigs (starting weight ≈ 1.5 kg) infected with rotavirus grew slower than their pair-fed controls. The findings of Koyama et al. (Reference Koyama, Chen, Lee and Unger1997) agree as rats challenged with adenovirus also grew slower than their pair-fed controls. Roberts and Almond (Reference Roberts and Almond2003) found no difference between pigs challenged with porcine respiratory reproductive syndrome (PRRS) virus and Mycoplasma hyopneumoniae and their pair fed controls. This may have been due to the pathogen or dose used, or due to the nature in which the data was analysed (average growth rate calculated over several weeks). Several experiments have shown that reductions in growth beyond those caused by reduced VFI were variable over the time course of pathogen challenges.

The experiments considered here included a variety of pathogens and hosts. Naïve and immune animals responded differently, with little or no reductions in growth in immune animals. The extent of unaccounted reductions in growth in immunologically naïve animals during pathogen challenges were, as expected, both pathogen (kind and dose) and time dependent. The literature data suggested, however, that over a certain range of doses, reductions in VFI fully explained reductions in growth. Effects on growth, beyond reduced VFI, depended on both host genotype and nutrition. To assign mechanisms for these effects on growth, marginal responses are now considered.

The digestibility of food during pathogen challenges

A pathogen challenge may affect the ability of a host to break down and absorb organic matter causing a reduction in available protein and energy (Turk, 1972). The literature is not consistent, however, on whether there are effects on true or apparent digestibility (Sykes and Greer, 2003). For this purpose it is important to consider the main site of infection of a particular pathogen. A pathogen that causes damage to the gastro-intestinal tract (GIT) could affect the ability of the host to digest organic matter components of a food. On the other hand, pathogens that do not affect the GIT, would not be expected to affect either digestion or absorption (Klasing and Barnes, Reference Klasing and Barnes1988).

The apparent digestibility of protein was reduced during pathogen challenges with parasitic worms of the stomach, small and large intestine in pigs (Hale, Reference Hale1986). The reduction in apparent digestibility was smaller for pathogens that affected mainly the stomach, compared with parasites of the small and large intestine (Hale, Reference Hale1986). The additional nitrogen excreted in the faeces, however, may have been associated with nitrogen leakage due to tissue damage and other endogenous nitrogen containing secretions e.g. mucin or plasma, and not due to a reduction of organic matter digestibility. Pathogen challenges affecting the small intestine, or parts further down the GIT, may result in damaged (or leaked) tissue not being digested and reabsorbed, while endogenous nitrogen leaving the stomach may be fully or partly re-absorbed in the small intestine. The observation that parasites of the small intestine (Trichostrongylus colubriformis: Kimambo et al., Reference Kimambo, MacRae, Walker, Watt and Coop1988) cause effects on growth beyond those associated with reduced VFI when compared with parasites of the stomach (Teladorsagia circumcincta: Sykes and Coop, Reference Sykes and Coop1977) is in support of this.

The true digestibility of organic matter during pathogen challenges has also been estimated. Symons and Jones (Reference Symons and Jones1970) estimated the true digestibility of protein using radioactively labelled protein in monogastrics (mouse and rat) and ruminants (sheep). They found no effect of large single doses with either one or three different gastro-intestinal nematodes (Nematospiroides dubius, Nippostrongylus brasiliensis and Trichostrongylus colubriformis) on the true digestibility of protein.Poppi et al. (Reference Poppi, MacRae, Brewer and Coop1986) using duodenal and ileal cannulas in sheep continuously infected with a sub-clinical trickle dose of Trichostrongylus colubriformis found a reduction in apparent digestibility, but no effect on the true digestibility of nitrogen. Sykes and Greer (Reference Sykes and Greer2003) considering further evidence for sheep are in agreement.

At low to moderate level challenges of the GIT, it appears that the true digestibility of food resources is not affected. It is likely that any effects on the ability of the host to digest organic matter will depend on the extent of damage caused by the pathogen. Damage incurred by a host increases with increasing levels (doses) of a challenge (Symons et al., Reference Symons, Steel and Jones1981) and the virulence (Turk, 1972) of a pathogen. It may be necessary to identify the doses and virulence at which true digestibility and absorption could be affected. This would allow the important distinction to be made between pathogen challenges that cause either only damage, or, damage and reductions in true organic matter digestibility.

The ideal amino acid composition of food protein during pathogen challenges

Once reductions in VFI and any effects on the digestibility of food resources have been taken into account, the biological value is the final measure of protein (amino acid) supply. The biological value (denoted by v in equation [2]) is calculated from the ratio of the first limiting amino acid (AA) in the food protein to that in a reference protein e.g. pigs whole body protein. The aim of this section was to determine how immune proteins differed in their amino acid contents, AAC g AA per kg CP, to that of a ‘reference protein’ used for healthy animals, as v is an important measure of protein supply. The ratio of essential to non-essential AAs, becomes particularly relevant for low protein foods (Deschepper and de Groote, Reference Deschepper and de Groote1995), as the total amount of non-essential AAs may become limiting.

Animals may produce significant amounts of certain nitrogenous compounds such as cytokines, antibodies, acute phase proteins or specific immune cells during pathogen challenges. Beisel (Reference Beisel1977) and Wannemacher (1977), amongst others, have proposed that such immune responses lead to specific requirements for certain amino acids. Immune proteins and cells have been found to contain high proportions of certain AAs (Reeds et al., 1994). Immune cells may also have high requirements for certain essential e.g. sulphur AA (Grimble and Grimble, Reference Grimble and Grimble1998) and non-essential e.g. glutamine and arginine (Newsholme, Reference Newsholme2001; Field et al., Reference Field, Johnson and Schley2002) AAs. Table 1 summarises the AAC of acute phase proteins (Reeds et al., 1994), other immune proteins (Houdijk and Athanasiadou, Reference Houdijk, Athanasiadou, t'Mannetje, Ramirez-Aviles, Sandoval-Castro and Ku-vera2003), colostrum and milk (Csapo'-Kiss et al., Reference Csapo'-Kiss, Stefler, Martin, Makray and Csapo'1995) and a reference protein (whole body protein of pigs; Kyriazakis et al. (1993)).

Table 1 A summary of the amino acid composition of different proteins that are associated with the immune response, in relation to the reference protein that is normally used for calculating the biological value of food protein. The amino acid composition of colostrum (a source rich in immune proteins), milk and reference protein (whole body protein of pigs) are also shown for comparison

† Amino acid composition of whole body pig protein from Kyriazakis et al. (1993), acute phase proteins from Reeds et al. (Reference Reeds, Fjeld and Jahoor1994) and for IgA, IgE, sheep mast cell proteases (MCP) and mucin were taken from Houdijk and Athanasiadou (Reference Houdijk, Athanasiadou, t'Mannetje, Ramirez-Aviles, Sandoval-Castro and Ku-vera2003).

‡ ASX – Asparagine + aspartate. GLX – Glutamine + glutamate.

§ APP(X) represents six different acute phase proteins: (1) C-reactive protein, (2) fibrinogen, (3) alpha-1-glycoprotein, (4) alpha-1-antitrypsin, (5) haptoglobin and (6) amyloid A.

The comparison of colostrum and milk is included as colostrum contains a large proportion of maternal antibodies. There are some marked differences between the different kinds of proteins in terms of their AAC. The proteins considered in Table 1 included proteins part of the innate immune responses (acute phase proteins) and proteins normally associated with expression of acquired immunity (immunoglobulins).

The AAC of acute phase proteins, APPs, was calculated as the average of six acute phase proteins (C-reactive protein, fibrinogen, alpha-1-glycoprotein, alpha-1-antitrypsin, haptoglobin and amyloid A) and compared with the reference protein. Large differences were noted (AAC of APPs v. AAC reference protein) for phenylalanine, tyrosine, tryptophan and threonine. The largest differences between the average of the two immunoglobulins and the reference protein were tryptophan, valine, cysteine and threonine. Serine, a non-essential AA, was present at greater concentrations in both kinds of immune proteins, and especially in immunoglobulins, and MacRae (Reference MacRae1993) has discussed its potential importance during pathogen challenges.

There were very large differences for the amino acid contents of immune proteins to that taken to be the normal reference amino acid content. The important issue, however, is whether the total requirement for ‘ideal protein’ for potential growth, P R _max/e _p, and that for making immune proteins, I M P/e _p g/day, and their amino contents lead to a continuously changing reference protein in challenged animals. It is assumed that P R _max is the animal's genetic maximum for protein retention and that IMP is the amount of immune protein produced at a particular point in time. It is also assumed that e _p is the same for both challenged and healthy animals. During health, v is taken as a constant for a given kind of food and reference protein. As the value of IMP changes over the time course of an infection, it may be necessary to calculate v by weighting the requirements and AAC of the protein requirements for growth and immune responses:

([3])

where AAC _growth is the normal reference protein, AAC _im is the AAC of the immune proteins and the content of the food protein is AAC _food. The potential protein retention (PR _max) is used in equation [3] as it is necessary to estimate v from potential protein requirements before predicting the partitioning of ideal protein towards actual PR. As PR _max (due to host state) and IMP (due to stage of infection and level of challenge) change over time it would be necessary to perform the above calculation on a time step basis. Modelling simulations would provide a more robust test of whether the ideal protein concept needs replacing (or revising) during pathogen challenges by taking into account changes in requirements for individual amino acids for both growth and immune functions.

Effects of pathogen challenges on the upper limit for growth

It is reasonable to assume that a given animal in a given state has an upper limit for growth e.g. Emmans and Fisher (Reference Emmans, Fisher, Fisher and Boorman1986). In marginal response experiments, it is assumed that this limit is achieved when no further response is observed to additional levels of resource intake: the linear-plateau concept. During pathogen challenges it would appear that challenged animals have not achieved the same upper limit for growth as their healthy controls (Willis and Baker, Reference Willis and Baker1981a and Reference Willis and Bakerb; Williams et al., Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc; Webel et al., Reference Webel, Johnson and Baker1998a). The authors' conclusion was that the ‘potential upper limit’ for growth was reduced.

Webel et al. (Reference Webel, Johnson and Baker1998a) found a lower plateau (maximum) for PR for LPS challenged chicks than their healthy controls, while Willis and Baker (Reference Willis and Baker1981a and Reference Willis and Bakerb) found that chicks challenged with Eimeria acervulina had a lower plateau for average daily body weight gain. The reduction in PR _max was estimated roughly to be 25% from Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) for pigs in a high immune stimulation environment, while Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) found a reduction of 11% for the response to lysine, but not for threonine or arginine in chicks. A plateau, however, may also arise due to insufficient amounts of energy in relation to protein, or due to exposure to a non-infectious stressor (Kyriazakis and Emmans, Reference Kyriazakis and Emmans1992b; Wellock et al., Reference Wellock, Emmans and Kyriazakis2003b).

Kyriazakis and Emmans (Reference Kyriazakis and Emmans1992b) showed that a reduction in the marginal efficiency for growth produces a plateau response, and that the marginal efficiency depended on the energy to protein ratio of the food. Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) used foods with different energy to protein ratios, and the highest level of protein, which was the only level that yielded a plateau response, had an energy to protein ratio close to the critical value for a reduction in efficiency to occur (Sandberg et al., Reference Sandberg, Emmans and Kyriazakis2005b). The findings of Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) and Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) do not, as some authors have assumed e.g. Escobar et al. (Reference Escobar, Van Alstine, Baker and Johnson2004), necessarily show that an animal's upper limit for growth is reduced during pathogen challenges.

A reduction in appetite (anorexia) could also result in the kind of responses that have been observed for marginal responses. The experiments of Willis and Baker (Reference Willis and Baker1981a and Reference Willis and Bakerb), Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) and Webel et al. (Reference Webel, Johnson and Baker1998a) were performed over a defined time, rather than weight range. Effects of pathogen challenges on growth are often transient, with an initial drop in growth rate, after which the animals recover as shown in the example from Hein (Reference Hein1968) in Figure 1.

Figure 1 The effect of different single challenge doses of the protozoan Eimeria acervulina on the live weight, LW g, of chicks over the time course of an experiment, which included the acute- and post-infection phase (Hein, Reference Hein1968).

The transient drop (2 to 3 days long) in growth rate shown in Figure 1 dictated the extent to which the healthy and challenged animals differed in size after they had overcome the infection (post infection). The extent of the difference depended on the dose of oocysts of Eimeria acervulina. As the challenged animals were smaller post infection they would also have a smaller upper limit for growth at a time, compared with healthy controls. In the marginal response experiments that were mentioned earlier, calculations of average growth rates included post-infection growth rates. This would bias the average growth rate of challenged animals downwards, but the difference would be detectable at only higher protein intakes.

Bown et al. (1991) found that sheep given abomasal infusions with nitrogen, when challenged with a gastro-intestinal parasite achieved the same level of nitrogen retention as their healthy controls; thus their data do not support a reduced upper limit for growth. It is difficult to design an experiment that would allow the two mechanisms (reduced potential versus reduced appetite) to be distinguished. A possible solution could be to combine the experimental methodologies of pair feeding and marginal response experiments where challenged animals and healthy (pair-fed) controls are given foods with different protein contents. On the other hand, it could be possible to use an infusion approach with different levels of nutrient infusion and where nitrogen balance is measured over the entire period of an infection. Future modelling attempts, using either mechanism, may contribute towards our understanding of how growth is reduced during pathogen challenges.

Effects of pathogen challenges on maintenance

Marginal response experiments were identified for five different kinds of ‘pathogen challenges’: a ‘high immune system activation’ treatment of pigs (Williams et al., Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc), a bacterial antigen challenge of chicks (LPS, Webel et al., 1998a and b), challenges with gastro-intestinal parasites of chicks (Eimeria acervulina, Willis and Baker Reference Willis and Baker1981a and Reference Willis and Bakerb) and sheep (Trichostrongylus colubriformis, Poppi et al., Reference Poppi, MacRae, Brewer and Coop1986, Datta et al., Reference Datta, Nolan, Rowe and Gray1998) and a challenge of pigs with blood borne parasites (Trypanosoma vivax, Fagbemi et al., 1990; Otesile et al., 1991). Data were analysed using either linear or continuous linear plateau regressions.

The experiments of Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) and Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) were the only experiments found in the literature, which measured PR in relation to different levels of protein or amino acid intake during a ‘challenge’. In the experiments of Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc), foods with different crude protein contents were used (designed with lysine as the first limiting amino acid). Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) formulated diets to provide different levels of lysine, threonine or arginine. The response in PR to lysine intake is shown in Figure 2, for chicks challenged with LPS, with a continuous linear plateau model (described in Sandberg et al. (Reference Sandberg, Emmans and Kyriazakis2005b)) fitted to the data.

Figure 2 The response in protein retention (g/day) to digestible lysine intake (g/day) of chicks challenged with LPS on every 2nd day over an 11-day period, in relation to their unchallenged controls (data from Webel et al. (Reference Webel, Johnson and Baker1998a)). A continuous-linear-plateau model (described in Sandberg et al. (Reference Sandberg, Emmans and Kyriazakis2005b)) was fitted to the data.

The marginal response in Figure 2 was not different, while maintenance increased slightly, and the challenged animals did not achieve the same maximum rate of PR, as discussed earlier. The response varied with different amino acids. Maintenance in the challenged animals varied from 1 to 1.3 times that in the healthy. Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) found that chicks supplemented with lysine had an increased maintenance, while those with threonine tended towards an increase, with no effect for arginine. Different effects on maintenance may suggest that amino acid requirements were affected to different extents. LPS stimulates an acute phase response, including the production of acute phase proteins, which have different amino acid contents from body protein retained in the healthy animal (see Table 1). The experiments of Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) also suggest that it may be necessary to consider the requirements of individual amino acids to fully account for reductions in growth during pathogen challenges.

The experiments of Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) agree with those of Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb), but with greater maintenance levels in two cases (2.9 and 1.6 times their controls). In both sets of experiments, however, the challenges used would be expected to have a small to moderate effect on the host, compared with actual bacterial, viral or parasitic challenges. To describe the effects of a particular pathogen on requirements for maintenance it is necessary to perform experiments that account for the entire time course of an infection. Marginal responses that are measured over the entire time course of defined pathogen challenges, and in particular during the acute phase (as shown in Figure 1) of an infection, at different doses would allow this.

Marginal response experiments with limiting energy have shown increased maintenance requirements as determined by regressions of live weight gain against energy intake, Figure 3. West African Dwarf goats challenged by Trypanosoma vivax (Van Dam, Reference Van Dam1996) had approximately maintenance requirements 1.2 times as great as their healthy counterparts.

Figure 3 The marginal response in energy retention (ER) to metabolisable energy intake (ME) for West African Dwarf goats challenged with Trypanosoma vivax; figure reproduced from Van Dam (Reference Van Dam1996) with permission. Regressions fitted with a common slope; open symbols are the non-infected, and closed symbols are the infected goats: the circles and triangles make the distinction between two different quality (low and high energy content) foods.

Experiments with pigs are in further support of an increase in maintenance requirements for energy (Fagbemi et al., 1990; Otesile et al., 1991). In these experiments the pigs were challenged with the same dose of Trypanosoma brucei at two different live weights. The 10 kg pigs (Fagbemi et al., 1990) had a greater increase in energy maintenance requirements (2.2 times healthy controls) than 100 kg pigs (Otesile et al., 1991), which had maintenance requirements 1.7 times of healthy controls. This suggests that animals of different sizes may have different changes in requirements at a challenge dose, but the issue that remains is how to relate pathogen challenges to size.

The effects of different challenge doses on hosts of different sizes need to be accounted for in both predictive frameworks and in the design of experiments. The issue is in two parts: (1) what is the relevant measure of animal size when quantifying the effects of pathogen challenges; (2) is it necessary to take into account, not only animal size, but also an animal's degree of maturity (or age) through some scaling rule? No clear data exist which allows for a choice to be made between body weight, protein weight and protein weight combined with a measure of fatness, as a measure of size. The second issue is even more difficult, and experiments that provide sufficient data to test it are needed. Satisfactory quantification of a pathogen challenge becomes crucial when considering interactions between dose, genotype and nutrition on the outcome of pathogen challenges. This has not been considered in a large number of experiments that address these interactions.

Effects of pathogen challenges on the marginal response

Pathogen challenges have been found to have different effects on the marginal response in growth for different pathogens.Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) and Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) found no effect of high immune system activation and LPS, respectively, on the marginal response in PR to either protein or amino acid intake. Willis and Baker (Reference Willis and Baker1981a and Reference Willis and Bakerb), using a specific pathogen challenge (Eimeria acervulina) at different doses on chicks found a slight effect on the marginal responses in live-weight gain to amino acid intake at a larger dose. The slope for chicks challenged with 2 × 10⁵ oocytes was reduced numerically by 10%, while with a challenge of 1 × 10⁶ oocytes the slope was reduced significantly by 22%. Taken together, however, marginal response experiments in monogastrics may suggest little effect on the marginal material efficiency of growth.

Experiments with sheep where either live weight gain or nitrogen retention were regressed on FI suggested an effect on the marginal response (Poppi et al., Reference Poppi, MacRae, Brewer and Coop1986; Datta et al., Reference Datta, Nolan, Rowe and Gray1998). The marginal response of the sheep was not affected during the initial weeks of a continuous challenge. Around the time when the sheep were likely to have had the greatest worm burdens and just prior to starting to express immunity, the slope was reduced and the intercept displaced, indicating a reduced marginal response and an increase in maintenance. The marginal response was no different from that of their controls once the animals had acquired immunity, and, overcame the challenge (Poppi et al., Reference Poppi, MacRae, Brewer and Coop1986; Datta et al., Reference Datta, Nolan, Rowe and Gray1998).

The time dependent effect on the slope as suggested by Poppi et al. (Reference Poppi, MacRae, Brewer and Coop1986) appears to coincide with the accumulation of the worm burden for that pathogen e.g. Van Houtert et al. (Reference Van Houtert, Barger, Steel, Windon and Emery1995). These time (pathogen load) dependent effects on the marginal response may explain why Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) and Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) did not find any effects on the slope. LPS challenges have short-term effects, and Williams et al. (Reference Williams, Stahly and Zimmerman1997a, Reference Williams, Stahly and Zimmermanb and Reference Williams, Stahly and Zimmermanc) used an undefined ‘high immune system activation’. To predict the effects of pathogen challenges on marginal responses in growth to resource intake it may be necessary to account for the kind and dose of pathogen together with the development of a challenge over time. The effect of pathogen challenges on the marginal response in growth, however, would appear to be small in magnitude.

Non-pathogenic antigen challenges

It is difficult to estimate the energy cost that is associated with the production of only immune proteins. The model used by several authors e.g. Demas et al. (Reference Demas, Chefer, Talan and Nelson1997), Mashaly et al. (Reference Mashaly, Heetkamp, Parmentier and Schrama2000), Pilorz et al. (Reference Pilorz, Jackel, Knudsen and Trillmich2005) is challenges with non-pathogenic antigens such as sheep red blood cells or keyhole limpet hemocyanin (an immune stimulant glucoprotein). In principle, any effects on energy metabolism would be the consequence of the expression of immune proteins and not due to repair of damage or fever.

Several experiments were identified in which total energy expenditure was measured in-directly through measurements of oxygen consumption during challenges with non-pathogenic antigens. Any difference in energy expenditure is then estimated roughly from the rate of oxygen consumption (20.1 kJ/l O₂; Eraud et al. (Reference Eraud, Duriez, Chastel and Faivre2005)) between challenged and non-challenged animals, which are shown in Table 2.

Table 2 The relative change in energy expenditure (RCE, challenged/controls) and energetic cost (E_antibody, challenged energy expenditure – control energy expenditure) due to antibody production, as measured by changes in oxygen consumption of animals and birds challenged with non-pathogenic antigens

† The antigen used was keyhole limpet hemocyanin (an immune stimulant glucoprotein) in all other cases it was sheep red blood cells.

‡ Estimated from age.

The data in Table 2 show that a significant energy requirement is associated with the production of immune proteins, given that any changes in energy expenditure are due to antibody production. The relative changes in energy expenditure appear to be within a similar range, except of that of Demas et al. (Reference Demas, Chefer, Talan and Nelson1997): this may be explained by the use of a different antigen, which induced an increase (>1°C) in colonic temperature. Demas et al. (Reference Demas, Chefer, Talan and Nelson1997), Svensson et al. (Reference Svensson, Raberg, Koch and Hasselquist1998) and Eraud et al. (Reference Eraud, Duriez, Chastel and Faivre2005) found that the largest increase in energy expenditure coincided with peak antibody production. It would appear, therefore, that the energy cost of mounting an immune response is strongly related to the level of the immune response. This in turn may explain why some authors have not found any effects on overall energy expenditure or growth (Henken and Brandsma, Reference Henken and Brandsma1982; Pilorz et al., Reference Pilorz, Jackel, Knudsen and Trillmich2005).

Difficulties may also arise from other components of an animal's energy budget being affected. Even a non-pathogenic antigen challenge (such as sheep red blood cells) may reduce the energy expenditure associated with activity (Henken and Brandsma, Reference Henken and Brandsma1982) as has been observed for pathogenic challenges (Van Diemen et al., Reference Van Diemen, Henken, Schrama, Brandsma and Verstegen1995; Van Dam, Reference Van Dam1996). Energy requirements due to production of immune proteins may therefore be partially ‘hidden’ within the overall energy budget of the animal. Experiments that have tried to consider the individual components of an animal's energy budget are now considered to further characterise the effects of pathogen challenges on energy requirements.

Energy balances, marginal responses and fever

Experiments with both pathogenic antigen challenges and challenges with live pathogens have shown maintenance requirements to be 1.05 to 1.35 times as great. There appears to be highly specific pathogen differences, with the greatest energetic cost occurring for pathogen or antigen challenges that lead to a fever e.g. Verstegen et al. (Reference Verstegen, Zwart, Van der Hel, Brouwer and Wensing1991), Zwart et al. (Reference Zwart, Brouwer, Van der Hel, Van den Akker and Verstegen1991) and Demas et al. (Reference Demas, Chefer, Talan and Nelson1997). Local pathogen challenges may cause less of an effect as was demonstrated by Van Diemen et al. (Reference Van Diemen, Henken, Schrama, Brandsma and Verstegen1995) who considered the effect of atrophic rhinitis on heat production in pigs.

Gastro-intestinal parasites may cause an increase in energy requirements through the repair of damaged tissues in the gut, or through the reprocessing of nitrogen leaked into the GIT that appears to later be partly reabsorbed (Poppi et al., Reference Poppi, MacRae, Brewer and Coop1986). MacRae et al. (1979) performed both energy and nitrogen balances of sheep, at different time points of a challenge with Trichostrongylus colubriformis. No differences were observed prior to week 4 between challenged sheep and their pair-fed controls, after which the metabolisability (ME/GE) was reduced compared with pair-fed controls, as is shown in Figure 4 together with their faecal egg counts.

Figure 4 The relationship between food metabolisability (ME/GE), faecal egg counts (FEC) and time of a sub-clinical trickle infection of sheep with the intestinal worm Trichostrongylus colubriformis (MacRae et al., Reference MacRae, Smith, Sharman, Corrigall, Ekern and Sundstol1982).

This suggests a significant increase in excretion of energy either in the urine or faeces, which is associated with the peak of the pathogen challenge; as in later balance periods there was no difference in metabolisability. As was the case for the nitrogen cost of damage, it appears necessary to predict the energetic cost of repairing damaged tissues, which appears to be related to the level of challenge of the host.

A significant body of evidence exists from experiments with Trypanosoma vivax infection in West African goats (Verstegen et al., Reference Verstegen, Zwart, Van der Hel, Brouwer and Wensing1991; Zwart et al., Reference Zwart, Brouwer, Van der Hel, Van den Akker and Verstegen1991; Van Dam, 1996; Van Dam et al., Reference Van Dam, Schrama, Vreden, Verstegen, Wensing, Van der Heide and Zwart1997 and Reference Van Dam, Hofs, Tolkamp and Zwart1998) on both energy and nitrogen requirements. Trypanosoma vivax in these experiments caused an increase in body temperature (consistently 0.8 to 1°C) and damage to red blood cells (Van Dam, Reference Van Dam1996). Energy requirement for maintenance was between 1.15-1.27 times that of healthy control animals. The findings of Van Dam (1996) and Van Dam et al. (Reference Van Dam, Schrama, Vreden, Verstegen, Wensing, Van der Heide and Zwart1997) are also shown in Figure 3.

The above changes in energy expenditure are likely to be underestimates as FI was reduced in all cases and the goats showed behavioural coping responses (Van Dam, 1996), which may have further masked the energetic cost. The cost due to fever has been shown to differ for goats that were either standing or lying (Van Dam, 1996). The most crucial finding, however, is that of Zwart et al. (Reference Zwart, Brouwer, Van der Hel, Van den Akker and Verstegen1991) who found that an increase in heat production due to fever is different, when measured during either day or night. Van Diemen et al. (Reference Van Diemen, Henken, Schrama, Brandsma and Verstegen1995) also found time dependent effects on heat production in pigs challenged by Pasteurella multocida. This is important for future experiments that attempt to quantify the energetic cost of pathogen challenges. If heat production is measured over a sample period during the day (a common practice) then the cost due to fever will be underestimated. Marginal response experiments with pigs are in support of increases in maintenance requirement by 1.7 and 2.2 times in pigs challenged with a Trypanosome (Fagbemi et al., 1991; Otesile et al., 1991).

Experiments with other pathogens appear consistent with those using the Dwarf goat-Trypanosoma vivax model in terms of the energetic cost of fever. Baracos et al. (Reference Baracos, Whitmore and Gale1987) reviewed the energy requirements caused by fever and concluded that a 1°C increase in body temperature due to fever, gave an energy requirement of 1.1 times as much. It was discussed, however, that the effect of fever on metabolic rate was time dependent, which is likely explained by its close relationship to the pathogen load in a host. Different types of antigens (either LPS or heat killed Staphylococcus areus), however, may cause different energy requirements (Benson et al., Reference Benson, Calvert, Roura and Klasing1993), as chicks challenged with LPS grew much less lipid than controls or chickens challenged with heat killed S. aureus. A predictive framework would need to consider the relationship between fever and pathogen challenge (kind, dose and virulence) and have clear definitions of how to include other energetic costs such as production of immune proteins or repair of damage within the overall energy budget of a host.

Innate immune responses

The immune proteins that are produced during the acute phase of an infection, such as the acute phase proteins and complement are here defined as innate immune proteins. It is recognised that the acute phase proteins have multiple functions, which include supporting the acquired arm of the immune response and assisting in the repair of damaged tissue (Murata et al., Reference Murata, Shimada and Yoshioka2004). The question is whether these immune functions are affected by amino acid availability, in animals that are growing.

Dritz et al. (Reference Dritz, Owen, Goodband, Nelssen, Tokach, Chengappa and Blecha1996) found no significant effect of three different foods on the expression of the acute phase protein haptoglobin in pigs that were gaining weight. Sakamoto et al. (Reference Sakamoto, Fujisawa and Nishioka1998) reviewed evidence of how nutrition may affect another set of innate immune proteins (complement) and did not find any effects of nutrition in ‘normal’ animals. In malnourished animals, however, it would appear that an additional supply of resources may affect the expression of the complement system, but this was only the case for severely malnourished humans. Fleck (Reference Fleck1989) stated that “Prolonged protein-energy depletion in man sufficient to yield a decrease in body weight of 25% led to only a 7% decrease in concentration of albumin…When the increase in plasma volume is taken into account there was no change in the amount of albumin in the circulation”. It would appear, therefore, that costs relating to innate immune responses may need including as part of the requirements for maintenance. The marginal response studies of Webel et al. (Reference Webel, Johnson and Baker1998a and Reference Webel, Johnson and Bakerb) are in support as they used LPS as an antigen, which largely stimulates an acute phase response.

Acquired immunity

Coop and Kyriazakis (Reference Coop and Kyriazakis1999 and Reference Coop and Kyriazakis2004) reviewed information in the literature on the effects of protein supplementation on immune responses and growth in sheep challenged with gastro-intestinal parasites. They concluded that the expression of immunity was affected by nutrition from experiments with sheep (Bown et al., 1991; Kambara et al., 1993), cattle (Mansour et al., Reference Mansour, Rowan, Dixon and Carter1991 and Reference Mansour, Rowan, Dixon and Carter1992) and goats (Singh et al., 1995). There is also similar evidence in pigs (Van Heugten et al., 1996) and mice e.g. Ing et al. (2000). Other kinds of pathogen challenges were considered here to determine whether the improvement of the expression of acquired immunity is a general response across different types of pathogens.

Tsiagbe et al. (Reference Tsiagbe, Cook, Harper and Sunde1987) found that growth and immune responses (antibody expression) of chicks were increased with increasing levels of methionine supplementation. Antibody expression was only improved once a plateau had been achieved in growth rate, thus their data suggest that growth was prioritised over immune responses. Bhargava et al. (Reference Bhargava, Hanson and Sunde1970b), on the other hand, found that chicks challenged with the Newcastle virus had improved growth rates and geometric mean antibody production when given foods that had increasing proportions of L-threonine. This suggests a partial prioritisation of amino acid between growth and immune responses, shown in Figure 5.

Figure 5 The live weight at 18 days (LW) and geometric mean antibody titres (AT) for chicks challenged with a Newcastle virus and given foods that had different contents of l-threonine from Bhargava et al. (Reference Bhargava, Hanson and Sunde1970b). Linear plateau response was approximated as a linear function of threonine content for live-weight gain (LWG = 364.T – 64.4) until the plateau of 141.1 was reached. The linear regression of antibody titer against valine content was equal to AT = 23.63T – 0.143.

Figure 5 does suggest that as the animals reached a plateau in growth there was no distinct improvement in antibody titer, which would not be expected if protein was being partitioned between these two functions. Bhargava et al. (Reference Bhargava, Hanson and Sunde1970a), however, found a different response for chicks challenged with Newcastle virus and given food of different amino acid (valine) contents, shown in Figure 6. In this case, the response in antibody titer did further increase when the animal reached a plateau in terms of growth, and thus providing strong evidence that valine was being partitioned between growth and immune functions.

Figure 6 The responses in live weight over 18 days (LW) and antibody titres (AT) to increasing valine contents (V) of a food for chicks challenged with a Newcastle virus from Bhargava et al. (Reference Bhargava, Hanson and Sunde1970a). Linear plateau response was approximated as a linear function of valine content (LW = 217.V – 58.2) until the plateau of 153.6 was reached. The linear regressions of antibody titre against valine content until maximum LW was reached was AT = 16.37.V – 0.6325 whereas for the subsequent phase was AT = 41.35.V – 23.04.

The data in Figure 6 are in support of a partial prioritisation of additional levels of amino acid supply between growth and immune responses. Furthermore, these two experiments indicate that the requirements of specific amino acids and their partitioning need accounting for in predicting growth and immune responses. The level of challenge may affect the extent to which protein (amino acid) is partitioned between growing ‘body’ protein and immune proteins (Wannemacher et al., Reference Wannemacher, Powanda and Dinterman1974).

There are several publications in favour of additional levels of protein of amino acid supplementation having a beneficial effect on the host coping with several different kinds of pathogen challenges. Lee et al. (Reference Lee, Austic, Naqi, Golemboski and Dietert2002), for example, found that arginine supplementation (a limiting amino acid in birds) improved immune responses in chicks challenged with the infectious bronchitis virus. Low et al. (2003) found that mice given a whey protein based diet, which is rich in certain amino acids found in large amounts in immune proteins, had increased antibody responses to several bacterial and viral antigens.

The likely explanation for hosts coping better with pathogen challenges when provided with sufficient amounts of protein (amino acid) is the control and elimination of the pathogen from the host (Van Houtert et al., Reference Van Houtert, Barger, Steel, Windon and Emery1995). The level of pathogen load (in particular the maximum pathogen load) is directly related with survival e.g. Powanda et al. (Reference Powanda, Cockerell, Moe, Abeles, Pekarek and Canonico1975) and Berendt et al. (Reference Berendt, Long, Abeles, Canonico, Elwell and Powanda1977). Suzuki et al. (1993) found increased survival in mice challenged with Staphylococcus aureus when given glutamine supplementation. Unfortunately, the change in pathogen load and performance over time for different bacterial and viral pathogens has not been measured extensively, whilst nutrition has been manipulated. Sheep challenged with gastro-intestinal parasites show reductions in pathogen load (worm burden) when given additional levels of protein (Van Houtert et al., Reference Van Houtert, Barger, Steel, Windon and Emery1995; Donaldson et al., 2001) or amino acid (methionine: Miller et al. (Reference Miller, Blair, Birtles, Reynolds, Gill and Revell2000)) supply. In most of these cases relatively low levels of protein supply (compared with those required for maximal growth) were required to achieve such responses. The quantitative relationship between degree of resource scarcity and rate of pathogen removal is thus central to predicting performance during pathogen challenges.

The problem of predicting resource partitioning during pathogen challenges appears to consist of three sub-problems. Firstly, it is necessary to predict the partitioning of amino acids between growing ‘body’ and immune proteins during exposure to different levels and kinds of pathogens. Secondly, it would be necessary to be able to account for the net benefit an animal would gain from investing in an immune response. Finally, a predictive framework would need to adequately describe how different genotypes partition their resources between growth and immune functions. This would allow consequences of genetic selection strategies (i.e. increased potential for growth or resistance to pathogens) to be explored, and for management (nutrition) strategies to be better informed.