AABArchives Animal BreedingAABArch. Anim. Breed.2363-9822Copernicus PublicationsGöttingen, Germany10.5194/aab-60-259-2017Invited review: Further progress is needed in procedures for the biological evaluation of dietary protein quality in pig and poultry feedsLiebertFrankflieber@gwdg.deChair of Animal Nutrition, University of Göttingen, Kellnerweg 6, 37077 Göttingen, GermanyFrank Liebert (flieber@gwdg.de)8August201760325927012April201728June20175July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://aab.copernicus.org/articles/60/259/2017/aab-60-259-2017.htmlThe full text article is available as a PDF file from https://aab.copernicus.org/articles/60/259/2017/aab-60-259-2017.pdf
Recently, biological procedures for feed protein evaluation
in pig and poultry diets have been based on the amino acid composition of feed
ingredients considering the animal's losses during processes of digestion
or total protein utilization in a different manner. Such a development
towards individual amino acids (AAs) was inevitable according to the
disadvantage of traditional protein quality measures, like biological value (BV)
or net protein utilization (NPU), to be non-additive in complex animal
diets. In consequence, such measures are generally not suitable for
predicting the final protein quality of protein mixtures from the individual
protein value of feed ingredients. Otherwise, recent measures of AA
disappearance from the small intestine up to the end of the ileum (ileal
AA digestibility) also do not provide a true reflection of the biological
availability of individual feed AAs independent of the extent of taking
into account endogenous AA losses during digestion processes. Sophisticated
procedures for protein evaluation are needed considering the AA losses, both
during absorption and utilization after absorption. Advantages and
limitations of important developments in procedures are discussed.
Accordingly, the development of an exponential modelling approach is
described (the “Göttingen approach”), which overcomes some of the traditional
disadvantages by measuring the individual AA efficiency. Connecting feed protein
evaluation, the modelling of quantitative AA requirements, and improved ideal
protein concepts offers different fields of application. In addition, as
demonstrated by example, the modelling of nitrogen losses per unit protein
deposition and the minimizing of this parameter yields a further interesting
tool for lowering the nitrogen burden from protein utilization processes.
Finally, it is pointed out that traditional laboratory procedures also need
to be updated, adapted to current knowledge, and validated according to the
increasing hurdles for animal studies from the viewpoint of animal welfare.
Modelling is a procedure with the potential to reduce the number of
experimental animals significantly. This development needs more attention,
higher acceptance, and wider application in the future of protein evaluation.
Introduction
Today, the importance of valid protein evaluation systems in animal
nutrition is not a point of dispute. However, the procedures underlay a
continuous development over more than 75 years. The implications for the
sustainable use of feed protein resources in animal nutrition, which are
partly in concurrency with human needs, are clear. Environmental
aspects also increase the pressure to further lower the dietary protein supply
in animal diets without a decline in the animal's performance data.
Consequently, an extended number of indispensable amino acids (AA) have become
more interesting as a feed additive to compensate for the suboptimal dietary supply
of individual AAs. This process yields lower nitrogen (N) loads which
have to be eliminated from the animal's metabolism by urea or uric acid
synthesis. In summary, all these factors are a driving force for ongoing
research on protein evaluation in animal nutrition. The current review aims
to summarize the important steps in the development of this important area of
nutritional research over decades and also to discuss the advantages and
limitations of approaches and draw some conclusions for focusing further
research work. In contrast to earlier reviews (Bock, 1975; Bergner, 1994),
an extended focus on biological protein evaluation (Hackler, 1977) will
overcome the excessive view on digestibility-related processes in the
digestive tract. This current procedure is supported by the general view of
Fuller (2012) who pointed out that digestibility is not the only determinant
of nutrient bioavailability; an integration of factors is needed for factors
that limit the extent of absorption and the availability of AA for
metabolism. Despite the well-known difficulties of such an integrated
procedure, it will provide the most validated information from a nutritional
point of view and consequently also the guideline for the present review.
Important developments
The starting point of intensified protein research would be expected in the
middle of the 19th century, but it seems speculative to name the
first scientist who recognized the nutritional importance of N-containing
substances in feed or food. An excellent review by Block and
Mitchell (1946) indicates that up to the beginning of the 20th century
it was believed that only intact proteins were of nutritional value for the
consumer. However, several studies applying hydrolyzed proteins provided the
experimental background for the current view that protein nutrition is in
fact an AA nutrition, and last but not least, that it is necessary to decide
between dispensable and indispensable AAs depending on species and age
(see Block and Mitchell, 1946).
Up to now, it has been a fundamental principle of biological protein evaluation to
relate the effect of a given protein intake to the animal's response as
measured by different, but mostly growth-related, criteria. Osborne et al. (1919)
have set the starting point by creating the protein efficiency
ratio (PER) in experiments with the laboratory rat to define the maximum
PER for individual protein sources based on experiments with graded
dietary protein supply.
PER(proteinefficiencyratio)=Bodyweightgain:Proteinintake
In fact, the observed maximum PER of individual feed proteins differs
depending on the dietary protein quality, but the maximum PER is
achieved with a different dietary protein supply. In consequence, several
later PER applications have modified the original approach through a
standardization of protein intake. Block and Mitchell (1946) discussed these
procedures in detail and mostly under critical view. However, based on the
rather easy way of measuring both the protein intake and the gain response in
experimental animals, the PER approach is also currently in use as a complex
measure of dietary protein quality, mainly for protein substitution studies
in fish nutrition (e.g. Peres and Olivia-Teles, 2005; Slawski et al., 2011;
Piccolo et al., 2017) or as response criteria in requirement studies
(e.g. Ahmed and Khan, 2004). Several limitations in the procedure are
mostly overlooked. In spite of the uncomplicated measure of body weight gain,
the age-dependent variation in body nutrient composition is not taken into
account. However, the response of this influence factor to derived
dietary protein quality is lower in standardized rat growth trials, but not
in agricultural animals.
In addition, Eggum et al. (1971, cited by Bock, 1975) proposed a nitrogen
efficiency ratio (NER) to eliminate effects resulting from the transfer factor (6.25)
for crude protein calculation from analysed nitrogen content on
protein quality assessment. In consequence, a more precise distinction
between different feed proteins was expected.
NER(nitrogenefficiencyratio)=BodyweightgainNitrogenintake
This assumption was not validated in general, and consequently the
modification was not widely introduced in animal nutrition.
The recommendation of Mitchell (1924) to evaluate feed proteins based on
the biological value (BV) became much more precise by taking into account
more physiologically based data, like N deposition (ND), N maintenance
requirement (NMR), and the true digestibility of the feed protein:
BV(biologicalvalue)=ND+NMRNintakeastrulydigested×100.
In addition to the observed N deposition (ND) data as response criteria
provided by N-balance studies, information about the quantity of
endogenous N losses was required. The nitrogen maintenance requirement (NMR)
is a reflection of the N quantity needed to replace the metabolic
(endogenous) N losses via faeces and urine, respectively. Finally, data about
BV were achieved by relating the sum of N deposition (ND) and NMR to the
uptake of truly digested feed protein as a measure of N utilization
following the process of absorption. Over decades, this procedure
dominated the field of feed and food protein evaluation for single-bowl
animals. In consequence, based on N-balance studies, fundamental concepts
were developed to provide comparable information about the complex protein
value of individual feedstuffs or diets, in spite of the fact that the knowledge about
protein metabolism and functional properties of individual AAs increased (Lintzel, 1939).
Mitchell and Carman (1924) created a net protein value taking into
account protein content, protein digestibility, and BV as the three
important
factors for the dietary protein value. The net protein value of an
individual protein source was achieved by multiplying these data (Mitchell
et al., 1945). Later on, multiplying the coefficient of true protein
digestibility and BV provided a useful measure of total utilization or
net utilization of a dietary protein (Block and Mitchell, 1946).
Accordingly, Bender and Miller (1953a) defined the net protein value (NPV)
based on results of the traditional N-balance technique. However,
due to an elevated number of N analyses and a time-consuming procedure, a short rat assay estimating the N content in the body from a strong
correlation between body water and whole body nitrogen content (Bender and
Miller, 1953b) was later recommended (Miller and Bender, 1955) for assessing net
protein utilization (NPU):
NPU(netproteinutilization)=B-(Bk-Ik)I,
where B is the total body N of the rats on the test protein,
Bk is the total body N of the rats on a non-protein diet,
I is the N intake of the test protein group, and Ik is the N intake of the non-protein group.
Expressed as a percentage, the NPU reflects the efficacy of net protein
utilization (Miller and Bender, 1955; Bender and Doell, 1957). From the current
point of view, the term “net” indicates that a separate non-protein group
of rats was utilized to create a measure for metabolic N losses, which
need to be replaced by the dietary protein supply.
Summarizing the expressiveness of both true N digestibility and BV, Lintzel (1941)
proposed the term “Physiologischer Nutzwert”:
PhysiologischerNutzwert=TrueNdigestibility×biologicalvalue100.
A new understanding about protein metabolism led to acceptance that a
mixture of absorbed exogenous and endogenous AA from protein catabolism can
be utilized to replace the endogenous metabolic losses. Lintzel and
Rechenberger (1940) and Gebhardt (1966) established the PNu as a benchmark
for evaluating dietary protein quality:
PNu(physiologicalvalueofprotein)=ND+NMRNI×100.
In fact, the application of this formula yields equal results with Lintzel (1941).
Additionally, all experimental data were related to the metabolic
body weight (BWkg0.67). The sum of ND and NMR was described as
N retention (NR) and needs to be distinguished from ND in terminology.
This was the initial situation when Gebhardt (1966) developed the new basic
concept of an exponential N utilization model. N balance experiments with
the laboratory rat and general agreement about the importance of
replacing endogenous N losses in future protein
evaluation systems provided the platform. An exponential function conforming to
the biological laws of growth (von Bertalanffy, 1951) provided a
physiologically well-founded response curve of body N deposition depending
on both the quantity and quality of feed protein intake. A significant
driving force for this research was the observed restriction for the application
of traditional procedures for complex protein evaluation of individual
feedstuffs or mixed diets. Unfortunately, traditional measures, like PER, BV,
and NPU, were not independent of the actual level of dietary protein intake
(Block and Mitchell, 1946). Each of these parameters was modulated with
characteristic course when the dietary protein supply of the same protein
was increased or lowered.
In Germany, a special working group on protein evaluation was established
to discuss fundamental problems of BV and NPU during the 12th annual
meeting of the Society for Nutrition Physiology (Gebhardt and Brune,
1960). This was indeed the starting point to improve the reliability of feed
protein evaluation. Accordingly, the new concept of Gebhardt (1966) was at
first focused on standardization to improve the comparability of protein quality
measures. Consequently, the exponential model was developed as a tool to
make dietary protein quality parameters independent of N intake. Due to the
common principle of several procedures taking into consideration the cost
of N maintenance metabolism, the common term NPU is subsequently applied for
protein quality measures making use of the relation between NR and N intake (NI):
NPU(netproteinutilization)=NRNI×100.
This application is valid independent of different methods and
different adequacy to reflect the real quantitative N costs for maintenance
metabolism. In this context, no distinction is made between N-balance
data and the results of comparative slaughter techniques with whole body analyses
to quantify ND in the animal. This type of model application is still in use
for evaluating the complex protein value of mixed feeds. However, in the meantime
the application field of the approach was significantly extended and
adapted to recent expectations for protein nutrition research in food-producing animals.
Results of N rise experiments with growing chickens (Ross 308)
depending on age period for estimating the threshold value NDmaxT
(Pastor et al., 2013).
Current applications
Several reports provide the details of current developments and applications
of the basic concept as initiated by Gebhardt (1966) and further developed
by Liebert and Gebhardt (1988). Today, the procedure is called the “Göttingen
approach” due to further developments over 2 decades at the University of
Göttingen (Liebert, 2015; Dorigam et al., 2017; Samadi et al., 2017).
However, it will not be possible to outline in detail how the different
issues of the current procedure differ from other approaches
recently in use. Model-specific parameters as utilized in current
applications were justified in earlier and recent publications (e.g. Liebert
et al., 2000; Thong and Liebert, 2004a–c; Samadi and Liebert, 2006a, b, 2007a, b,
2008; Liebert and Benkendorff, 2007a, b; Liebert, 2008, 2009,
2015; Liebert and Wecke, 2008; Samadi et al., 2017; Wecke and Liebert, 2009,
2010, 2013; Wecke et al., 2016; Dorigam et al., 2017)
and can be condensed as follows:
NR=NRmaxT1-e-NI⋅bND=NRmaxT1-e-NI⋅b-NMR,
where NR is daily N retention (ND + NMR) [mg (BWkg0.67)-1],
ND is daily N deposition or N balance (NI - NEX [mg (BWkg0.67)-1]),
NI is daily N intake [mg (BWkg0.67)-1],
NEX is daily N excretion [mg (BWkg0.67)-1],
NMR is daily N maintenance requirement [mg (BWkg0.67)-1],
NRmaxT is the theoretical maximum for daily NR [mg (BWkg0.67)-1],
NDmaxT= NRmaxT- NMR is the theoretical maximum
for daily ND [mg (BWkg0.67)-1],
b is the slope of the NR curve indicating the dietary protein quality (the
slope of the curve for a given protein quality is independent on NI), and
e is the basic number of the natural logarithm (ln).
The attribute “theoretical” suggests that the threshold values (NDmaxT
or NRmaxT) are generally not in the scope of practical growth
performance data but yield an estimate of the genetic potential when each of
the limiting factors for maximum growth are eliminated (Samadi and Liebert,
2006a). This is a theoretical situation indeed, but at least not a
limiting factor to derive practical data from modelling. Figure 1 gives an
example for this application from current studies.
The genetic potential is defined as an unreachable
theoretical threshold value of the exponential function and cannot be
realized even with an optimized feeding strategy or in ideal environmental
conditions. If the ranking of such a threshold value is clear, no problem
exists for further model applications. Accordingly, individual amino acid (AA)
requirement data are derived for daily protein deposition data in line
with practical growth data. The threshold value (NDmaxT
resp. NRmaxT) is used only as a model parameter to relate the real rate
of deposition to the estimated genetic potential.
A validation of the model parameter b as a measure of dietary protein
quality, which is independent of the actual level of protein intake, has been
reported in several pig and poultry studies
(e.g. Thong and Liebert, 2004a, b, c; Wecke and Liebert, 2009; Farke, 2011; Pastor et al., 2013; Pastor, 2014).
According to the basic concept of standardizing NI for valid feed protein
evaluation, the model is also currently applied for assessing the dietary
protein quality in mixed diets with alternative protein sources making use
of a standardized value of NPU (Brede et al., 2016; Dietz et al., 2016;
Dietz and Liebert, 2017; Neumann et al., 2017). More diversified
applications of such an important tool could help to overcome misleading
conclusions about the reality of distinctions in feed protein value between
protein sources (Neumann et al., 2017).
Characterization of developing the genetic potential
As already discussed, the estimation of NDmaxT is required as a threshold
value for basic applications of the exponential model, but as a given
percentage of the theoretical threshold value NDmaxT real performance
data are utilized to derive AA requirement data depending on graded aimed
animal performance (e.g. Wecke et al., 2016; Samadi et al., 2017). However,
also from the viewpoint of animal breeding, the observed NDmaxT data are of
interest because they provide additional information about breeding success.
An example for this application with growing chickens is demonstrated in Fig. 1.
The threshold value of the exponential function (NDmaxT) is estimated
by statistical application of the Levenberg–Marquardt algorithm (Marquardt,
1963) as reported elsewhere (e.g. Samadi and Liebert, 2008; Wecke and
Liebert, 2009; Pastor et al., 2013). The applicability of the procedure was
also demonstrated in fish nutrition (Liebert et al., 2006) and utilized for
AA requirement studies in Oreochromis niloticus (Liebert and Benkendorff,
2007a, b; Liebert, 2009).
Age-dependent NDmaxT [mgN (BWkg0.67)-1 day-1] of
fattening pigs with different genders and years as derived from N-balance
studies with graded dietary protein supply and approximated functions for
NRmaxT depending on body weight (BW).
Average BWEstimated NDmaxT(kg)Boars1Female pigs2Boars330174025153800401538202033485013951696299860128714662716701201129324798011301157227690107110462098100102095519411109758771798
1 Gebhardt (1973); NRmaxT= 6995.7 × BWkg-0.3635; NMR = 292.
2 Liebert and Gebhardt (1988); NRmaxT= 29038 × BWkg-0.6776; NMR = 283 (Nörenberg, 1987).
3 Wecke and Liebert (2009); NRmaxT=-1619.3 ×lnBWkg+ 9733.6; NMR as a function of BW.
The summarized results of a series of experiments, both earlier and current,
are given in Tables 1–3. In consequence, the estimated NDmaxT data
give an indication of the influencing factors, like age period, gender, and
breeding progress. As demonstrated in Table 1, parameter
NDmaxT declines with increasing age, but the course of the threshold
value is also dependent on the gender. In addition, the breeding progress
in the modern genotype is clear.
The age and genotype effect is also valid in growing meat-type chickens (Tables 2 and 3).
It has to be repeated that NDmaxT data are not real data, but
theoretical values resulting from a statistical estimation of threshold
values of the N-rise curve dependent on N intake. This cannot be seen as a
disadvantage of the approach because modelling quantitative AA
requirements makes use of real ND data.
Amino acid requirements based on dietary amino acid efficiency
In addition to the validated evaluation of dietary protein quality (model parameter b or
standardized NPU), the “Göttingen approach” may also be applied to AA
requirement studies making use of the principles from the diet dilution
technique (Gous and Morris, 1985). Generally, a defined limiting AA (LAA)
in the diet under study is a prerequisite for these applications
because protein deposition in the animal is strictly limited by
the dietary supply of this AA.
Age-dependent NDmaxT [mgN (BWkg0.67)-1 day-1] of
male meat-type chicken genotypes.
Estimated NDmaxTGenotypeRoss 3081Ross 3082Na/na3Na/Na3Age period (days) 10–20341245923741350125–352713430131663056
1 Farke (2011); 2 Pastor et al. (2013); 3 Khan et
al. (2015) (Na/na heterozygous; Na/Na homozygous naked neck meat-type chicken).
Age-dependent NDmaxT [mgN (BWkg0.67)-1 day-1] of
older male meat-type chicken genotypes (earlier data as summarized by Liebert, 2008).
Estimated NDmaxTGenotypeCobb 500Ross 308I 657*Red JA*Age period (days) 10–25363436632807278930–45278327512723268850–65178319361486141970–851386nd11911043
* Hubbard ISA extensive genotypes of meat-type chicken
(Samadi and Liebert, 2007a).
Example for modelling lysine (Lys) requirement data during the starter
and grower periods of male meat-type chickens (Ross 308) depending on graded
daily protein deposition, different in-feed efficiency of Lys, and predicted
daily feed intake (Wecke et al., 2016).
Starter period (d10–20, mean BW 600 g) PD (g day-1)9 10 11 BWG (g day-1)55 61 67 (1)(2)(3)(1)(2)(3)(1)(2)(3)bcLys-153.150.447.853.150.447.853.150.447.8Lys required(mg (BWkg0.67)-1 day-1)9019481001104410991160120612701340(mg day-1)640673711741780823857902952Lys content needed in the starter diet (%) FI (g day-1)700.910.961.021.061.121.181.221.291.36800.800.840.890.930.981.031.071.131.19900.710.750.790.820.870.920.951.001.06Grower period (d25–35, mean BW 1800 g) PD (g d-1)15 16.5 18 BWG (g day-1)91 100 109 (1)(2)(3)(1)(2)(3)(1)(2)(3)bcLys-164.561.358.164.561.358.164.561.358.1Lys required(mg (BWkg0.67)-1 day-1)75379383785890395397510261083(mg day-1)111711751241127213391413144615221606Lys content needed in the grower diet (%) FI (g day-1)1500.740.780.830.850.890.940.961.021.071700.660.690.730.750.790.830.850.900.951900.590.620.650.670.700.740.760.800.84
PD is daily protein deposition (N deposition × 6.25),
BWG is daily body weight gain (crude protein content in BWG 16.5 %),
bcLys-1 is lysine efficiency: (1) as observed,
(2) 5 % lower as observed, (3) 10 % lower as observed. Lys supply
required is the lysine requirement for targeted PD. FI is daily feed intake.
In this case, the shape of the NR curve is not only a function of NI, but
also of the daily intake of the LAA (LAAI) as a part of the feed protein
fraction. For that important application, the basic function (1) is
logarithmically transformed (natural logarithm, ln) and provides Eqs. (2)
and (3):
NI=lnNRmaxT-lnNRmaxT-NR:b,b=lnNRmaxT-lnNRmaxT-NR:NI.
The derived NI by Eq. (2) gives the daily quantity of dietary protein
(N × 6.25) which is needed to yield the intended level of growth performance
(in terms of NR) at a given or observed dietary protein quality (in terms of
model parameter b). In addition, the model parameter b is derived by
Eq. (3). Equations (1)–(3) have demonstrated earlier model applications
for which the main focus was on questions of complex protein evaluation and
the AA composition of the feed protein was not of top priority.
However, since the review by Block and Mitchell (1946), the importance of
feed protein AA composition as the most important factor in dietary protein
value is well known. When the emphasis of the model changes to AA-based
applications, a further important transformation is required: the function
needs to be adapted because the independent variable determining the
resultant dietary protein quality (b) is the concentration (c) of the LAA in
the dietary protein. This fundamental connection needs to be “translated”
into the traditional model applications. As reported in detail earlier
(e.g. Liebert and Gebhardt, 1988; Liebert, 1995, 2008; Samadi and Liebert, 2006a, b,
2007a, b; Liebert and Wecke, 2008; Liebert, 2015), Eq. (2) can be
transformed into Eq. (4) when taking into account the concentration of LAA
in the feed protein:
LAAI=lnNRmaxT-lnNRmaxT-NR:16bc-1,
where LAAI is the daily intake of the LAA [mg (BWkg0.67)-1],
c is the concentration of the LAA in the feed protein [g 16 gN-1],
and bc-1 is the observed dietary efficiency of the LAA.
Equation (4) is widely applied for assessing quantitative AA requirement
data in both earlier (Liebert et al., 1987; Liebert and Gebhardt, 1988;
Thong and Liebert, 2004a–c; Samadi and Liebert, 2006a, b, 2007a, b; Liebert,
2009; Wecke and Liebert, 2009, 2010) and recent studies (Pastor et al.,
2013; Wecke and Liebert, 2013; Khan et al., 2015; Dorigam et al., 2017;
Samadi et al., 2017). An important precondition for validated conclusions is
that experimental data are available which describe the NR or ND response to
a defined LAAI at a specific level of dietary efficiency of the LAA, as
reflected by the model parameter (bc-1). The existing relationship
between the aimed daily ND, graded dietary efficiency of the AA under study,
and required LAAI in context with the expected level of feed intake is demonstrated in Table 4.
Optimal dietary ratios for individual amino acids as related to
lysine;
results of a meta-analysis (Wecke and Liebert, 2013).
It is shown by example that the finally recommended in-feed concentration of
lysine is under the influence of both animal factors and feed factors, which
need to be taken into account for the validity of the recommended in-feed AA
concentrations. The real feed intake depends on age, gender, and genotype,
but environmental variables, like climate, are also generally
underestimated influence factors. More attention has to be given to
the modulating effects of such zootechnical factors. If not, it cannot be
expected that requirement studies under controlled conditions will yield
generalizable requirement data. These factors are also important when
traditional dose–response experiments are applied in AA requirement studies,
but they are insufficiently taken into account as currently demonstrated by
Samadi et al. (2017).
Dose–response experiments are widely applied when the efficacy
of supplemented AAs is under study. However, misleading efficacy for L- and
DL-methionine isomers was concluded (Shen et al., 2014) when both the basic
preconditions for the application of statistical procedures and factors as
discussed above are ignored. In contrast, applications of the “Göttingen
approach” yielded similar methionine efficiency for both of the isomers in
chicken studies (Liebert et al., 2015) in agreement with recent reports
(e.g. Htoo and Morales, 2016). This example underlines the importance of a
verified experimental design and validated physiologically based statistical
procedures for generalized conclusions about the efficacy of supplemented feed AAs.
Improvements on the ideal protein concept
One approach to realize the high efficiency of protein utilization in
agricultural animals is the earlier concept to recommend an ideal dietary
protein composition for diet formulation (Almquist and Grau, 1944; Oser,
1951; Dean and Scott, 1965). Later on, the dietary ideal amino acid ratio (IAAR)
was introduced by Cole et al. (1980) and taken over by the
British ARC (1981) for pig nutrition.
Currently, the IAAR concept is widely accepted in pig and poultry nutrition
(e.g. Baker, 2003; Wecke and Liebert, 2013; Wecke et al., 2016). The individual indispensable AAs have to be related to a reference AA, mostly
lysine (Lys), which is almost exclusively utilized for body protein
deposition in growing animals. In addition to the quantitative AA
requirement data (Table 4), applications of the “Göttingen approach” may
also contribute to improving the IAAR both indirectly via individual AA
requirements and directly by relating the observed AA efficiency data (model
parameter bc-1) as reported recently (Samadi and Liebert, 2008; Pastor
et al., 2013; Wecke and Liebert, 2013; Khan et al., 2015; Liebert, 2015).
According to Samadi and Liebert (2008), the reciprocal relationship between
Lys efficiency (as reference AA) and the observed efficiency of the
individual LAA under study is utilized to derive optimal dietary AA ratios (Eq. 5):
IAAR=bcLYS-1:bcLAA-1.
As already mentioned, model parameter b linearly depends on LAA concentration (c)
in the protein, and the slope (bc-1) is an expression of AA
efficiency by summarizing both digestibility and post-absorptive utilization
of the LAA in general agreement with Lintzel (1941). In addition, the order of
observed AA efficiency data from the individual AA under study is indirectly
related to the specific physiological AA requirement per unit of protein
deposition. From this point of view, both feed factors and animal factors
are involved when comparisons are made at the level of observed AA
efficiency data. As pointed out by Wecke et al. (2016), the reliability of
measured AA efficiency data for the reference AA Lys is a fundamental
precondition for such applications. The summarized results of a meta-analysis
are given in Table 5.
IAAR of growing meat-type chickens as derived by directly relating
observed amino acid efficiency data according to Eq. (5) (Wecke and Liebert, 2013).
Actually, the complete information about the IAAR of indispensable AAs with
the
“Göttingen approach” is not available. A summary of current results based on
applications of Eq. (5) is given in Table 6.
According to the fact that both feed and animal factors modulate the
observed AA efficiency data, further studies have to enlighten their
individual quantitative importance. The sulfur-containing AAs
methionine and cysteine are the focus of ongoing experiments.
Future applications
Eggum and Christensen (1974) basically demonstrated the additivity of
the protein digestibility data in a mixture in relation to the protein digestibility
of individual ingredients. However, the missing additivity of traditional
protein quality parameters for individual feed proteins, as discussed above, is
the main limitation to making use of these parameters in optimizing animal
feeds. Consequently, the further development of protein quality evaluation
systems had to be founded on evaluation of individual AAs. At least the
specific contribution of the individual feed proteins is added, and in summary it yields
the AA content of the final diet.
Over many years, only the chemically analysed total AA content was utilized
in feed formulation for monogastric agricultural animals. A next step to
come closer to the utilization process in the animal was focused on AA
digestibility as measured at the end of the digestive tract (digestible AA).
However, increasing knowledge about the significance of microbial processes
in the digestive tract, namely in the post-ileal sections of the intestine,
led to procedures for measuring the individual AA digestibility up to the
end of the small intestine (e.g. Low, 1980; Sauer and Ozimek, 1986;
Van Leeuwen et al., 1987; Lemme et al., 2004; Stein et al., 2007). Since
Low (1980), it is generally accepted that ileal measurement is preferred to
the faecal method in simple-stomached animals when the digestion and absorption of
AAs is to be evaluated. However, ileal digestibility may be expressed as
apparent, standardized, or true digestibility. Endogenous losses are
separated into basal and specific losses, and specific losses are induced
by feed ingredient characteristics, like fiber content, type of fiber, and
anti-nutritional factors (Stein et al., 2007). In consequence,
a high modulation of endogenous AA losses can be expected but is
sufficiently taken into account only in part. Currently, only basal AA
losses are estimated depending on feed intake and providing a standardized ileal
digestibility. In consequence, a database for standardized AA digestibility
in pig and poultry was created (e.g. Evonik, 2016). The advantage is that
standardized AA digestibility data are more likely to be taken into account in mixed
diets compared with apparent ones (Stein et al., 2005). In this context,
it is important to note again that standardized ileal AA digestibility only
means that basal endogenous AA losses are considered. In addition, several
proposals were made to standardize the experimental procedures as a whole,
namely the section of the small intestine taken for chyme sampling
in poultry studies (e.g. Kluth and Rodehutscord, 2006, 2009). Generally, for
an improved validity of the observed AA digestibility data, a standard type
of experiment is required taking into account more than the procedure of
chyme sampling (Ravindran et al., 2017).
However, according to Stein et al. (2007) all measures of AA digestibility
are generally based on the disappearance of AA from the digestive tract
only. These measures do not reflect the net breakdown or synthesis of AA in
the intestinal lumen and the absorption of chemical forms, like Maillard
reaction products (Maillard, 1912) with Lys, which are precluded from
metabolic utilization for protein synthesis. The ε-amino group
of Lys is the primary target for an attack by reducing carbohydrates, and up
to 70 % of the Lys residues of a protein are reactive and can be damaged
depending on the factors time and temperature (Finot et al., 1977). Previous
work with growing pigs has demonstrated that the ileal digestibility assay
overestimates the availability of Lys, but also threonine, methionine, and
tryptophan in heat-processed proteins (Batterham et al., 1990; Batterham,
1992). It appears that a considerable portion of these amino acids is
absorbed but inefficiently utilized. In the case of isoleucine, it was
indicated that ileal digestibility more closely reflected the proportion of
the AA that can be utilized by the pig (Batterham and Andersen, 1994).
Consequently, in the case of heat-processed feed proteins it cannot be expected
that measures of the ileal AA digestibility are generally a valid indicator
of the available AA supply in pigs. According to Carpenter (1973), reactive
amino groups can also be provided by arginine and histidine, indicating that
Lys represents not the only problem but the most important one.
In addition, microbial fermentation in the small intestine may also
contribute to the synthesis and catabolism of AA, and in consequence to
discrepancies between ileal AA digestibility data and AA bioavailability,
which include AA utilization following the absorption process (Fuller, 2003).
Summarizing these aspects with a focus on future developments in feed protein
evaluation, it cannot be accepted to commit only to ileal AA digestibility.
In addition, strengthened animal protection laws are limiting surgery
techniques to make use of fistulated pigs or caecectomized birds. In
consequence, it remains doubtful whether the needed database update can be
sufficiently ensured by in vivo studies. The applications of
traditional procedures, like feeding experiments and digestibility and balance
studies, are also relevant from the viewpoint of animal welfare when metabolism cages
restrict activities, movement, and inter-individual contact.
Consequently, the demand from the viewpoint of animal science needs to be
stated for further scientific development (Committee for Requirement
Standards of the Society of Nutrition Physiology, 2017).
Unfortunately, measures of AA bioavailability based on the response of growth
parameters or body protein deposition, which can sort below the maximum
permissible load from the viewpoint of animal protection, are generally
restricted to investigating the LAA under study. In consequence, both the
procedure AA efficiency (“Göttingen approach”) and each of the other
techniques to measure AA bioavailability cannot provide an enlarged
database usable for feed protein evaluation systems. The only way
out for routine protein evaluation is to create more in vitro techniques as
proposed earlier (e.g. Savoie and Gauthier, 1986; Galibois et al., 1989;
Huang et al., 2000; Van Kempen and Bodin, 1998; Boisen, 2000).
In addition, analytical procedures for the evaluation of AA
bioavailability, extensively starting with Carpenter (1960, 1973) and
Booth (1971), may yield improved information when they are further developed
(e.g. Hurrell et al., 1979; Nordheim and Coon, 1984). The use of the rat
as a model animal for growing pigs was discussed by Rutherford and Moughan (2003).
The potential for such alternative procedures can be seen
when they are adapted to current knowledge and validated in vivo.
However, systemic developments in this field are unfortunately missing.
The further potential of the modelling procedure as presented consists of
estimating N losses during protein conversion processes in the animal, depending on both
feed factors and animal factors (Dänicke and Liebert, 1992;
Liebert, 1996; Liebert and Wecke, 2010, 2012). Such a tool has the potential
to be developed into a physiologically based estimate for N excretion (NEX) per
unit ND (NEX : ND) deposition depending on the aimed animal's performance (ND)
and the available feed protein in terms of quantity and quality. An example for this application is given in Fig. 2.
Clearly, the lowest ratio NEX : ND in a 50 kg growing pig was achieved at
approximately 2500 mg NI per BWkg0.67, corresponding to 215 g of daily
crude protein intake and providing 115 g of daily protein deposition. It is
indicated that both a lower and higher protein supply create a higher ratio
NEX : ND. However, the course of the response curve is also dependent on the age
period and the dietary protein quality. In consequence, the better
the protein quality, the lower the required protein supply, and the ratio
NEX : ND will further decline. In addition, requirement recommendations for
individual AAs can be derived for an optimal level to make use of the
NDmaxT depending on genotype and corresponding to a minimized NEX : ND.
Such a sophisticated application of the modelling procedure needs an
enlarged database for model parameter NDmaxT (e.g. Nörenberg,
1987; Farke, 2011; Wecke and Liebert, 2009; Khan et al., 2015) and observed
individual AA efficiency data in mixed diets with and ingredient composition
near practical feeding conditions (e.g. Liebert, 2008; Samadi and
Liebert, 2008; Wecke and Liebert, 2009, 2010, 2013; Wecke et al., 2016;
Pastor et al., 2013; Samadi et al., 2017), which may reflect the real
variation in this model parameter in common feedstuffs.
Finally, modelling protein metabolism with the physiologically based
“Göttingen approach” lays the foundations for the most important applications
in the field of current protein evaluation for simple-stomached growing animals:
defining the genotype in terms of the theoretical potential for
N deposition (NDmaxT);
assessing feed protein value based on observed efficiency of the limiting AA;
concluding AA requirements taking into account graded dietary AA efficiency;
modelling AA requirements depending on the aimed level of performance
(percent of NDmaxT);
evaluating the efficacy of supplemented AAs as related to protein-bound AAs
or different isomers or analogues of the added-feed AAs;
and modelling the N losses from the N utilization process in terms of minimized
N excretion per unit ND.
Course of N excretion per unit N deposition (NEX : ND) as derived
from N balance data and exponential function of NEX dependent on N intake
in growing pigs of 50 kg BW (Wecke and Liebert, 2009).
Greater acceptance by both scientific societies and applied
research groups is needed to make use of each type of complex modelling
procedure. It would be desirable to compensate for the upcoming limitation
on in vivo studies due to increasing standards for animal welfare and animal
protection through the extended application of physiologically based modelling, also in
the field of protein evaluation for pig and poultry diets.
Data are available in the original papers cited.
The author declares no conflict of interest.
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