Note: Descriptions are shown in the official language in which they were submitted.
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B&P File No. 6580-148
Title: Method of Identifying High Immune Response Animals
FIELD OF THE INVENTION
The invention relates to a method of identifying and
breeding high immune response animals within a population of animals
under stress, such as during peripartum.
BACKGROUND OF THE INVENTION
It has been found that there is an association between
stress and disease occurrence in animals (T. Molitor and L. Schwandtdt,
"Role Of Stress On Mediating Disease In Animals", Proc. Stress
Symposia: Mechanisms, Responses, Management. Ed., N.H. Granholm,
South Dakota State University Press, April 6-7, 1993). Further it has been
suggested that stress can lead to a compromised immune system. (T.
Molitor and L. Schwandtdt, "Role Of Stress On Mediating Disease In
Animals", Proc. Stress Symposia: Mechanisms, Responses, Management.
Ed., N.H. Granholm, South Dakaota State University Press, April 6-7,
1993/ Morrow-Tesch J.L. et al. 1996 J. Therm. Biol. 21(2):101-108) This can
have significant effect on populations of animals such as commercial
livestock including cattle, pigs, poultry, horses, and fish, wherein stress
can be related to growth inhibition, infertility, and decreased milk or egg
production (where applicable). It has been shown that the peripartum
period or periparturition, in animals is a period of stress. (L.G. Johnson,
"Temperature Tolerance, Temperature Stress, and Animal
Development", Proc. Stress Symposia: Mechanisms, Responses,
Management. Ed., N.H. Granholm, South Dakaota State University
Press, April 6-7, 1993; J.J. McGloner, "Indicators Of Stress In Livestock
And Implications For Advancements In Livestock Housing", Proc. Stress
Symposia, . Mechanisms, Responses, Management. Ed., N.H.
Granholm, South Dakaota State University Press, April 6-7, 1993; T.
Molitor and L. Schwandtdt, "Role Of Stress On Mediating Disease In
Animals", Proc. Stress Symposia: Mechanisms, Responses, Management.
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Ed., N.H. Granholm, South Dakaota State University Press, April 6-7,
1993; M.J.C. Hessing et al, "Social Rank And Disease Susceptibility In
Pigs", Vet Immunol. Immunopath 43:373-387, 1994; F. Blecha,
"Immunoligcal Reactions Of Pigs Regrouped At Or Near Weaning", Am.
J. Vet. Res. 46(9): 1934-1937, 1985; D.L. Thompson et al., "Cell Mediated
Immunity In Marek's Disease Virus-Infected Chickens Genetically
Selected For High and Low Concentrations Of Plasma Corticosterone",
Am. J. Vet. Res. 41(1):91-96, 1980; Kehrli, H.E. et al., 1989a & b, Am. J.
Vet. Res. 50(2):207 and 215)
Impairment of bovine host defense during the
peripartum period may be associated with high concurrent disease
occurrence. Impaired resistance may be due to endocrine factors
associated with metabolic and physical changes occurring during
gestation, parturition and lactation (Smith et al., 1973; Guidry et al., 1976;
Burton et al., 1993). Infectious diseases of the peripartum period include
mastitis, metritis and pneumonia. Metabolic and some reproductive
diseases also predominate during this period and include retained
placenta, milk fever, ketosis, and displaced abomasum. Mastitis is the
most economically relevant disease. Estimated annual losses from
mastitis are $35 billion (U.S) worldwide (Giraudo et al. 1997), $2 billion
(U.S.) in the United States (Harmon, 1994) and $ 17 million (Can.) in
Canada ($140-300 Can./cow) (Zhang et al., 1993).
Mastitis is an inflammation of the mammary gland
characterized by local and systemic responses (Burvenich et al., 1994).
Mastitis can be clinical or subclinical, when signs are not directly
observable, but somatic cell counts in milk (SCC) increase and overall
production performance decreases. Mastitis is caused by a number of
Gram positive and Gram negative bacteria which are either major or
minor pathogens. Major pathogens induce the greatest compositional
changes in milk and have the greatest economic impact (Harmon, 1994).
They include Staphylococcus aureus, Escherichia coli, Streptococcus
agalactiae, Klebsiella spp., and others, while minor pathogens include
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coagulase negative staphylococci, and Corynebacterium bovis. The
incidence of udder infection and clinical mastitis is usually highest at
parturition and during early lactation (Smith et al., 1985). Coliforms
such as E. coli and Klebsiella are the most common major pathogen
during this period. Since coliform mastitis is difficult to treat, natural
defence mechanisms of the mammary gland have been investigated in
pursuit of control procedures (Burvenich et al., 1994). Coliform mastitis
may be peracute and fatal, or subclinical. Most commonly it is acute
clinical mastitis, with local and systemic signs of disease. Coliforms are
Gram-negative microorganisms from the family Enterobacteriaceae
which include important species from the genera Escherichia, Klebsiella,
Enterobacter, Citrobacter and Proteus (Harmon, 1994; Kremer et al., 1994).
The structure of the cell wall of coliform bacteria plays an important role
in the virulence of the bacteria and subsequently in the pathogenesis of
mastitis. The cell wall of E. coli has an inner cytoplasmic membrane, a
peptidoglycan layer, an outer membrane that consists of two layers: a
phospholipid protein layer and an outer lipopolysaccharide layer (LPS),
and finally some strains possess an additional capsular polysaccharide
layer. The LPS layer has three components: the O-specific polysaccharide
chain, a polysaccharide core, and lipid A. Lipid A mediates the biological
properties of LPS (endotoxin). Endotoxemia causes clinical signs of
disease including high fever, drowsiness, appetite loss, dehydration, loss
in milk production, cardiovascular failure, shock and often death
(Kremer et al., 1994; Burvenich et al., 1994). Factors which contribute to
susceptibility to mastitis include the complex environment (pasture,
bedding, cleanliness of holding areas), management (milking practices,
antibiotic therapy during lactation and dry-off) and physical trauma to
the teat and/or udder (Cullor, 1995).
Various attempts have been made to develop vaccines
against S. aureus as a treatment for mastitis, but without success.
Vaccines have included toxoid, protein A, capsule and fibronectin in
varying combinations and concentrations (reviewed by Sordillo, 1995).
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While these preparations may reduce the severity and duration of
mastitis, new infections are not prevented. Inclusion of capsular
polysaccharide in vaccine preparation slightly reduced the rate of new
infection (Watson and Schwartskoff, 1990). More recently, the
combination of a crude extract of S. aureus exopolysaccharides and
inactivated unencapsulated S. aureus and Streptococcus spp. in a vaccine
decreased incidence of intramammary infections caused by S. aureus
(Giraudo et al., 1997). Newer vaccines against environmental coliforms
contain rough or R-mutants of E. coli or Salmonella typhimurium. The
surface core antigens of these mutants induces formation of cross-
protective antibody that provides protection against various gram-
negative diseases of animals including mastitis and calf scours. (Parker et
al., 1994). These vaccines decrease incidence and severity of clinical
disease but do not affect prevalence of coliform infections (Sordillo, 1995).
Direct selection for disease resistance may be done either
by selecting the most disease-resistant breeding stock under normal
environmental conditions, or by challenging the breeding stock with
specific pathogens (Hutt, 1959). Indirect selection is based on
identification of reliable indirect markers of disease resistance (Detilleux
et al., 1993). Phenotypic indicators include morphological markers (eg.
eye margin pigmentation in bovine infectious keraconjunctivitis),
physiological markers (eg. hemoglobin type in malaria), and innate or
immune response traits (eg. PMN function, antibody response and CMI).
Genotypic indicators include candidate genes (eg. MHC genes, Ig genes,
TcR genes), and anonymous molecular genetic markers (eg. RFLPs,
tandem repeats loci, microsatellite loci) (Detilleux et al., 1993).
Experiments using immune response variation as
selection criteria have been successful at directing response to be high or
low (Biozzi et al., 1968; Ibanez et al., 1980; Siegel et al., 1980; Van der
Zijpp
et al., 1983; and Mallard et al., 1992). The continuous distribution
antibody response suggests that response is under multigenic control
(Puel and Mouton, 1996) and that characteristic quantitative antibody
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responsiveness is controlled by several independently segregating loci
(Stiffel et al., 1987). The first selection experiment using antibody
response following immunization was reported in guinea pigs
assortatively mated for five generations. The immunogen used was
diphtheria anatoxin and the immune responses of progeny were
progressively modified in upward and downward directions (Shiebel,
1943). A similar experiment was conducted using rabbits selected for two
generations based on antibody produced to Streptococcus sp. (Eichmann
et al., 1971). A more extensive examination of antibody response
variability in mice was demonstrated by Biozzi et al. (1979). Several
independent selective matings were carried out with mice for antibody
responsiveness to sheep red blood cells (SRBCs). SRBCs are
multideterminant antigens which are strongly immunogenic in all
strains of mice (fuel and Mouton, 1996). Assortative mating of mice
with extreme phenotypes in upward or downward directions were
repeated for successive generations until maximal divergence of the two
lines was achieved (Biozzi et al., 1972). The relevance of this dichotomy
pertains to the ability of mice to mount strong responses, either antibody
or cell mediated immune response, to extra or intra cellular organisms.
The low line (L line) was determined to be more resistant than the high
line (H line) to intra-cellular organisms such as Salmonellae, Yersinia,
Mycobacteria, and Brucellae, and when the macrophage provides the
dominant defensive barrier. The H line was more resistant to
extracellular microorganism including Pneumococcus, Klebsiella,
Plasmodia, and Trypanosoma. The major genetic modification which
explained differences between these selected lines was at the level of the
macrophage. Antigen was observed to be slowly catabolized and
persisted on the macrophage membrane of the H line mice, whereas it
was rapidly destroyed in L line macrophages. Selection of chickens based
on antibody response to SRBC has also demonstrated variation and the
consequent divergence of high and low lines of chickens (Siegel and
Gross, 1980; Van der Zijpp et al., 1983; Pinard et al., 1992). Antibody
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response to SRBC and chicken erythrocytes was similarly evaluated in
guinea pigs, which diverged to high and low immune response lines
after successive selection for 8 generations (Ibanez et al., 1980). Yorkshire
pigs selected using estimated breeding values (EBVs) for both antibody
and cell mediated immune response, were reported to diverge into high
and low immune response lines (Mallard et al., 1992). The maximum
divergence of high and low responses were observed between generation
1(G1) and 3 (G3) with little or no response to selection after generation 4
(G4) (Mallard et al., 1997). Although a few studies have examined the
effect that selecting for milk production has on various innate and
immune response parameters, no breeding studies have been conducted
using immune response variation as selection criteria.
Selective breeding of cattle for resistance to mastitis using
somatic cell count (SCC) is currently under evaluation. Current industry
trends favour a low somatic cell count in milk secretions. A SCC that is
too low may be detrimental to innate mechanisms of resistance to
mastitis and therefore must be used with caution. Genetic correlation
between SCC and mastitis vary, but values are mainly positive (r= 0.81;
Madsen, 1989; r=0.3, Weller et al., 1996). SCC is now considered the
primary trait used to evaluate susceptibility to mastitis which enables
indirect selection for resistance to mastitis (Shook, 1994; Dekkers et al.,
1998). Selection based on occurrence of clinical mastitis is unreliable
since it is not routinely recorded, it has complex aetiology, and
observations on the occurrence and severity of mastitis are subjectively
evaluated by producers. Several records on SCC are available through
dairy herd improvement corporations which provide a substantial
database from which to determine estimated breeding values for SCC.
SCC and its logarithmic transformation, SCS, have higher heritability
(h2=ranging between 0.10-0.12) (Emmanuelson et al., 1988; Banos and
Shook, 1990; Boettcher et al., 1992) than clinical mastitis (h2=0.03)
(Emmanuelson, 1988; Madsen, 1989). However, low heritability
estimates of SCS, in contrast to some production traits, indicate that SCS
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is not influenced to a greater degree by environmental factors. Low
heritabilities suggest that SCS and mastitis will respond more slowly to
genetic improvement than milk yield (Shook, 1993; Boettcher et al.,
1992). Research conducted in Ontario by Dekkers and Burnside (1994)
evaluating estimated transmitting abilities (ETAs) for linear somatic cell
score (LSCS) indicated that daughters of the poorest sires had double the
average SCC (transformed from LSCS) of daughters of the best sires, and,
sires whose daughters had a higher LSCS tend to have more mastitis
problems. This research indicated that, although adding LSCS to genetic
selection will reduce genetic progress for production by <2 percent, it will
also slow down the current genetic deterioration of resistance to mastitis.
Its inclusion would be relevant since there would be lower treatment and
other related mastitis costs and there would be an increase in the
revenue per cow per year by 0.3 to 1.0 percent, despite a slight decrease in
milk sales. While there is some benefit to using SCS as a selection tool, it
is not as heritable as some aspects of immune response phenotype.
Antibody response to ovalbumin (OVA) in dairy calves was reported by
Burton et al. (1989) to be moderately heritable (h2=0.48), and in contrast to
SCS may be more promising as a selection tool for improved inherent
disease resistance (Burton et al., 1989).
Dekkers et al. (1996a) recently developed a sire index
called the total economic value index (TEV) which includes
economically weighted traits of importance. It includes production, herd
life and udder health. Production accounts for 64% of the TEV, herd life
for 26% and udder health, which includes SCS, accounts for 10% of the
TEV. While production still is the most economically important, more
emphasis can now be placed on the costs associated with mastitis by
evaluating SCS. Once more heritable candidate markers of immune
response are determined, more information about udder health could be
added to the TEV.
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SUMMARY OF THE INVENTION
The present invention relates to a method of identifying
high immune response animals under stress and a method of
determining an animal's susceptibility to stress related disease. The
method involves evaluating the animal's antibody response to an
antigen over a time interval spanning the stress, for example in
periparturition, the pre- and postpartum period. Based on the response
to the antigen, the animals can be classified as a high, average or low
immune responder. Accordingly the present invention provides a
method of ranking the immune response of a test animal within a
population of animals under stress comprising:
(a) immunizing the animals with at least one antigen at least
once before onset of the stress; and
(b) for each of the animals within the population,
measuring an antibody response to the at least one antigen at least once
before the onset of the stress and at least once during the stress.
wherein an antibody response from the test animal that is greater than
the average antibody response of the population during the stress
indicates that the test animal is a high immune responder.
According to another embodiment of the present
invention there is provided a method of ranking the immune response
of a test animal within a population of animals under stress comprising:
(a) immunizing the animals with at least one antigen at least
once before onset of the stress and at least once during the stress; and
(b) for each of the animals within the population,
measuring an antibody response to the at least one antigen at least once
before the onset of the stress and at least once during the stress,
wherein an antibody response from the test animal that is greater than
the average antibody response of the population during the stress
indicates that the test animal is a high immune responder.
Where the stress is periparturition, the high immune
responders comprise animals that have a sustained antibody response in
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both the pre and postpartum period, (herein referred to as Group 1
animals). These animals are least likely to develop peripartum disease.
The average (herein referred to as Group 2 animals) and the low (herein
referred to as Group 3 animals) immune responders comprise animals
that initially have an average antibody response which declines either
prior to, or at, partuition. In particular, the average immune responders
comprise animals that have an average antibody response up until
parturition, and thereafter show a lack of measurable antibody response.
The low immune responders comprise animals that have an average
antibody response until several weeks prepartum, (e.g., 3 weeks) and
show a progressive decline in antibody response thereafter.
Measuring the antibody responses to the antigen over
time intervals, rather than at a discreet point in time, allowed the present
inventors to develop a mathematical index which can be used to rank
the animals. The mathematical index as part of the immunization and
measurement schedules of the present invention provide a method of
ranking the immune response of a test animal within a population of
animals under the stress of periparturition. The method with the index
comprise the following:
(a) immunizing the animals with at least one antigen at least
once before onset of the stress and at least once during the stress; and
(b) for each of the animals within the population, measuring
an antibody response to the at least one antigen at least once before the
onset of the stress and at least three times during the stress, and at least
once after the stress,
(c) calculating the mathematical index of the antibody
response wherein the mathematical index is: y=primary response +
secondary response + tertiary response + quaternary response wherein,
(i) y is the total antibody response;
(ii) the primary response is the difference in antibody
quantity at a first period of time preperipartum and at a
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second period of time prepartum, wherein the animal is
immunized at the first period of time preparipartum;
(iii) the secondary response is the difference in antibody
quantity at the second period of time prepartum and at
about parturition, wherein the animal is immunized at
the second period of time prepartum;
(iv) the tertiary response is the difference in antibody quantity
at about parturition and at a first period of time
postpartum, wherein the animal is immunized at about
parturition; and
(v) the quaternary response is the difference in antibody
quantity at the first period of time postpartum and a
second period of time post peripartum,
wherein animals exhibiting negative secondary or tertiary responses are
weighted with a positive coefficient and the test animal having a y value
greater than about one standard deviation above the average of the
population is a high responder.
The inventors have also shown that exposing a
population of animals to an antigen which can evoke a cell mediated
immune response (CMIR) and measuring at least one indicator of the
CMIR of each animal during stress, when combined with the
immunization and measurement of antibody schedule of the present
invention, there is provided yet another embodiment of the present
invention for ranking the immune response of a test animal within a
population of animals under stress. According to this embodiment of
the invention the method comprises:
(a) immunizing the animals with at least one antigen at least
once before onset of the stress;
(b) for each of the animals within the population,
measuring antibody response to the at least one antigen at least once
before the onset of the stress and at least once during the stress;
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(c) exposing the animals to an antigen which can evoke a
cell mediated immune response (CMIR); and
(d) measuring at least one indicator of the CMIRof each
animal during the stress,
wherein the measurement of the indicator is combined with the
measurement of the antibody response to provide an immune response
and a test animal having an immune response greater than the average
immune response of the population indicates that the test animal is a
high immune responder.
The mathematical index as part of the immunization and
measurement schedules of the present invention according to the
embodiment just described provides a further embodiment of a method
of ranking the immune response of a test animal within a population of
animals under the stress of periparturition. The method with the index
comprise the following:
(a) immunizing the animals with at least one antigen at least
once before onset of the stress;
(b) for each of the animals within the population,
measuring antibody response to the at least one antigen at least once
before the onset of the stress and at least once during the stress;
(c) exposing the animals to an antigen which can evoke a
cell mediated immune response (CMIR);
(d) measuring at least one indicator of the CMIRof each
animal of the population during the stress; and
(e) calculating the mathematical index of the antibody
response and CMIR wherein the mathematical index is: y=primary
antibody response + secondary antibody response + tertiary antibody
response + quaternary antibody response + CMIR wherein,
(i) y is the total antibody response;
(ii) the primary response is the difference in antibody
quantity at a first period of time preperipartum and at a
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second period of time prepartum, wherein the animal is
immunized at the first period of time preparipartum;
(iii) the secondary response is the difference in antibody
quantity at the second period of time prepartum and at
about parturition, wherein the animal is immunized at
the second period of time prepartum;
(iv) the tertiary response is the difference in antibody quantity
at about parturition and at a first period of time
postpartum, wherein the animal is immunized at about
parturition;
(v) the quaternary response is the difference in antibody
quantity at the first period of time postpartum and a
second period of time post peripartum; and
(vi) CMIR is the measurement obtained from at least one
method of determining CMIR,
wherein animals exhibiting negative secondary or tertiary antibody
responses are weighted with a positive coefficient and a test animal
having a y value greater than about one standard deviation above the
average of the population is a high immune responder.
The methods of ranking the animals according to the
present invention can be used to identify animals that are least
susceptible to developing a post-partum disease. In particular, the
present inventors have demonstrated that high immune responder dairy
cows have a lower incidence of mastitis as compared to animals that are
ranked as average or low immune responders. Accordingly, the present
invention provides a use of a method of the invention to identify
animals that are selected from the group consisting of: animals that are
less susceptible to developing a peripartum disease wherein antibody
quantity and quality are relevant host resistance factors; animals that are
less susceptible to developing a peripartum disease wherein antibody
quantity and quality and CMIR mediate broad-based disease resistance;
animals with increased growth hormone; and animals with increased
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IGF-1 outside the peripartum period and with decreased IGF-1 inside the
peripartum period.
Once animals have been ranked by the method of the
present invention, the high immune responder animals may be
selectively breeded in order to produce animals that have lower
incidence of peripartum disease.
The methods of the present invention may be used in a
wide range of animals including cows, pigs, chickens and other
commercially useful animals.
Other features and advantages of the present invention
will become apparent from the following detailed description. It should
be understood, however, that the detailed description and the specific
examples while indicating preferred embodiments of the invention are
given by way of illustration only, since various changes and
modifications within the spirit and scope of the invention will become
apparent to those skilled in the art from this detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention will now be described in relation to the
drawings in which:
Figure 1A and B are graphs showing the anti-OVA
antibody levels versus time for animals of Group 1, Group 2 and Group
3.
Figure 2 is a bar graph showing the percentage of disease
occurrence in the animals of Group 1, Group 2 and Group 3.
Figure 3 is a graph showing the anti-OVA antibody levels
versus time for the animals of Group 1, Group 2 and Group 3.
Figure 4A-C are graphs showing the anti-OVA antibody
levels in whey versus time for the animals in Group 1, Group 2 and
Group 3.
Figure 5A-C is a graph showing the anti-E. coli antibody
levels versus time for the animals of Group 1, Group 2 and Group 3.
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Figure 6A and B are bar graphs showing antibody levels
versus time in the animals of Group 1, Group 2 and Group 3.
Figure 7 is a bar graph showing the rate of mastitis
occurrence based on antibody response within a herd.
Figure 8A-C is a graph showing the somatic cell score
versus time for the animals in Herd 1, Herd 2 and Herd 3.
Figure 9 is a graph showing Con A stimulated
lymphocyte proliferatives versus time for the animals of Group 1, Group
2 and Group 3.
Figure 10 is a bar graph showing the percent increase in
skin thickness after challenge with PPD in cows and heifers.
Figure 11 is a bar graph showing the lymphocyte counts
versus time for the animals in Group 1, Group 2 and Group 3.
Figure 12A-C are bar graphs showing the production
versus antibody response for the animals of Group 1, Group 2 and Group
3.
Figure 13A and B are graphs showing the anti-OVA
antibody levels versus time for the animals of Group 1, Group 2 and
Group 3.
Figure 14A-C are graphs showing the hormone
concentration versus time for the animals of Group 1, Group 2 and
Group 3.
Figure 15 is a bar graph showing the percentage disease
occurrence and the antibody response in the animals of Group 1, Group 2
and Group 3.
DETAILED DESCRIPTION OF THE INVENTION
As hereinbefore mentioned, the present invention is
directed to a method of ranking the immune response of an animal
within a population of animals. Further the present invention is directed
to a method of calculating a mathematical index of the immune response
in an animal. The present invention is also directed to the use of the
methods of the invention to decrease the incident of disease, to enhance
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growth hormone (GH) and IGF-1 levels in animals during periods of
stress and to breed high immune response animals.
More particularly, the invention is directed to a method
of ranking the immune response of a test animal within a population of
animals under stress comprising: (a) immunizing the animals with at
least one antigen at least once before the onset of the stress; and (b) for
each of the animals within the population, measuring antibody response
to the at least one antigen at least once during the stress, wherein an
antibody response from the test animal that is greater than the average
antibody response of the population during the stress indicates that the
test animal is a high immune responder.
In a preferred embodiment, each animal is further
immunized at least once during the stress. In yet a further embodiment,
the antibody response of the animals of the population is also measured
at least once before the onset of the stress.
According to another embodiment of the invention, the
method of ranking the immune response further comprises exposing
the animals of a population to an antigen, preferably under stress, which
can evoke a cell mediated immune response (CMIR), measuring an
indicator of the CMIR at least once during the stress and combining it
with the measurement for antibody response, to obtain an immune
response, wherein an immune response of a test animal that is greater
than the average immune response of the population during stress
indicates that the test animal is a high immune responder. Preferably,
the CMIR is specific to the antigen. The antigen used to evoke the CMIR
is preferably different than the antigen used to invoke the antibody
response.
Suitable indicators of CMIR include, but are not limited
to: the measurement of one or more predetermined cytokines [for
example, as described in L.T. Jordan et al. "Interferon Induction in SLA-
Defined Pigs", Res. Vet. Sci. 58:282-283, 1995; J. Reddy et al., "Construction
Of An Internal Control To Quantitate Multiple Porcine Cytokine
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mRNAs by rtPCR", BioTechniques 21:868-875, 1996; W.C. Brown et al.,
"Bovine Type 1 And Type 2 Responses", Vet. Immunl. Immunopath
63:45-55, 1998]; measuring delayed-type hypersensitivity (for example as
described in Mallard, 1992, PCT/CA93/00533); and measuring in vitro
lymphocyte proliferation to at least one antigen (for example, as described
in Mallard B.A. et al., Animal Biotech 1992 ref. PCT/CA93/00533).
"Stress" as defined herein, is any acute or chronic increase
in physical , metabolic, or production related pressure to the animal. It is
the sum of the biological reactions to any adverse stimulus, physical,
metabolic, mental or emotional, internal or external, that tends to disturb
an organisms homeostasis. Should an animal's compensating reactions
be inadequate or inappropriate, stress may lead to various disorders.
Many events can place an animal under stress. These include, but are
not limited to: weaning, castration, dehorning, branding, social
disruption, change in ration, temperature exercise and parturition.
Examples of social disruption include, but are not limited to: change of
location, shipping, and addition or removal of animals from immediate
environment. The onset of parturition (also known as "prepartum"),
parturition and after parturition (also known as "postpartum"), herein
collectively referred to as "periparturition" or "peripartum", are also
known causes of stress in animals. The time of periparturition, the time
around parturition, is hereinafter referred to as the "peripartum period".
In cows the peripartum period is from about three weeks before to about
three weeks after parturition. Therefore, in cows, about 8 weeks prior to
parturition would be prior to onset of the periparturition stress; about 3
weeks prior to parturition to about 3 weeks postparturition would be
during the periparturition stress; and after about 3 weeks postparturition
would be after the peripartum stress.
Although the examples below use cows as the animal
model, a person skilled in the art, upon reading this description, would
understand that the present invention could be applied to other animals,
preferably animals used for commercial use, such as pigs, poultry, fish,
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horses, and companion animals such as dogs and cats. Accordingly,
"animal" as used herein includes, all members of the animal kingdom.
In a preferred embodiment of the invention, the animals used are from
the bovine genus and more preferably are selected from the group
consisting of multiparous and primiparous cows. Further, it is
understood that when conducting a method of the invention relatives
may be used as the animal to define the rank of other relatives.
One skilled in the art would appreciate that the gestation
period differs between animal species. As such, when peripartum is the
stress, such a person upon reading this description would know that the
optimum times for immunizing and measuring an animal's immune
response, as provided in this description for cows, may have to be
adjusted, if another animal species is used.
In one embodiment of the invention, pre-peripartum or
before the on-set of stress, preferably refers to 2 or more weeks before the
onset to stress.
According to one embodiment of the invention, when
periparturition is the stress and cows are the animals, the animals are
immunized at least once before the stress at about 8 weeks before
parturition and at least once during the stress at about 3 weeks before
parturition and at about parturition.
According to a preferred embodiment of the invention,
when periparturition is the stress and cows are the animals, the anti
response is preferably measured at about 8 weeks before parturition, at
about 3 weeks before partuition and at about parturition. In a more
preferred embodiment the antibody response is further measured at
about 3 weeks after parturition. "At about 8 weeks before parturition", as
used herein, means at 8 weeks before parturition +/- 4 days. "At about 3
weeks before parturition" , as used herein, means at 3 weeks before
parturition +/- 4 days. "At about parturition", as used herein, means at
or up to one week after parturition, but not before parturition. "At about
CA 02255423 1998-12-10
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3 weeks after parturition", as used herein means at one week, and
preferably at or up to 3 days, after parturition +/- 4 days.
"Antigen" as used herein, refers to any agent to which an
animal is exposed and elicits an immune response. Suitable antigens for
use in the present invention can be of bacterial, viral , synthetic, or other
origin. For instance in cows, suitable antigens include but are not limited
to ovalbumin, hen egg white lysozyme, human seralbumin, red blood
cells from any animal other than the cow; tyrosine - glutamine - alanine -
lysine co-polymer (a synthetic antigen). In choosing suitable antigens for
the present invention, the antigens are preferably ones to which the
animal is not normally exposed, and preferably one to which they have
not been exposed.
The antigens can be formulated into a vaccine, such as
Ecoli J5, as used in the examples discussed herein. Examples of other
possible vaccine antigens for use in cows include but are not limited to:
Presponse (Merial) and IBR/PI3/BVD/BRSV combination vaccine
(Bovilan 4K) etc.
The term "greater than average antibody response" as
used herein means the production of antibody in response to an antigen
in an amount that is greater than approximately one standard deviation
(sd) above that of the population mean. The preferred source for
measuring antibody response in the present invention is milk or blood.
"Milk", as used herein, is meant to include both the milk and the
colostrum.
The term "greater than average immune response" as
used herein means a measure of an indicator of cell mediated immune
response combined with the indicator of antibody response, which
together provide a value that is one standard deviation above the
population mean.
The term "population" as used herein refers to a group of
animals of the same species in which the measurements are obtained.
For instance, in the examples of the present invention, three different
CA 02255423 1998-12-10
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groups or herds are used to obtain the population data. Population as
used herein can also refer to a sample of the population, in so far as
obtaining the ranking of immune response in a significant sample of a
population can enable one to estimate or predict the immune response
ranking of other related animals within the population.
According to one embodiment of the present invention,
there is a method of ranking the immune response of a test animal
within a population of animals under the stress of periparturition
comprising: (a) immunizing the animals with at least one antigen at least
once before onset of the stress and at least twice during the stress; and (b)
for each animal of the population, measuring antibody response to the at
least one antigen at least once before the onset of the stress, at least three
times during stress and at least once after the stress, calculating the
mathematical index of the antibody response wherein the mathematical
index is: y=primary response + secondary response + tertiary response +
quaternary response wherein,
(i) y is the total antibody response;
(ii) the primary response is the difference in antibody
quantity at a first period of time before preperipartum and at a second
period of time during prepartum, wherein the animal is immunized at
the first period of time prepartum;
(iii) the secondary response is the difference in antibody
quantity at a second period of time prepartum and at about parturition,
wherein the animal is immunized at the second period of time before
prepartum;
(iv) the tertiary response is the difference in antibody
quantity at about parturition and at a first period of time postpartum,
wherein the animal is immunized at about parturition; and
(v) the quaternary response is the difference in antibody
quantity at the first period of time postpartum and at a second period of
time after post partum,
CA 02255423 1998-12-10
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wherein animals exhibiting negative secondary or tertiary responses are
weighted with a positive co-efficient, preferably about 1.5. This is done to
discriminate against animals with low antibody response during stress.
Test animals having a y value greater than about one standard deviation
above the average of the population are high immune responders.
The mathematical index of the total immune response
can also be obtained with the method of the present invention, wherein
the CMIR is added to the above-noted equation and results in y= primary
response + secondary response + tertiary response + quartenary response
+ CMIR, wherein "y" is the total immune response of each animal of a
population, and test animals having a "y" value greater than about one
standard deviation above the average of the population are high
immune responders.
In one embodiment the present invention relates to a
modification of the mathematical index in which all phenotypic
indicators of immune response are converted to estimated breeding
values. The use of this method is as previously described and includes:
to identify animals with high immune response; and allow breeding of
animals with increased accuracy for inherent increases in immune
responsiveness.
The methods of this invention can be used to identify
preferred animals selected from the group consisting of: animals that are
less susceptible to developing a peripartum disease wherein antibody
quantity and quality are relevant host resistance factors; animals that are
less susceptible to developing a peripartum disease wherein antibody
quantity and quality and CMIR mediate broad-based disease resistance;
animals with increased growth hormone; and animals with increased
IGF-1 outside the peripartum period and with decreased IGF-1 inside the
peripartum period.
The methods of the invention can also be used to obtain a
population of animals through traditional hereditary breeding
techniques by calculating estimated breeding values (EBVs) of the
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indicators of immune responsiveness (Veterinary Genetics, F.W.
Nicholas, Oxford Science Publications, 1987; D.S. Falconer. An
introduction to quantitative genetics. Longman, London, 1981),
preferably cows, which are high, average or low immune responders.
The methods of the invention can also be used to predict
or estimate the immune response ranking of an animal by having
knowledge of the immune response ranking of at least one of the
animal's relatives. Factors which would increase the accuracy of the
estimate or prediction of such an immune response ranking of an
animal, include but are not limited to: (i) Degree of separation from the
animal (the knowledge of the ranking of the animal's full siblings and
parents would result in a better estimate than with knowledge of the
ranking of only cousins or partial siblings); (ii) The amount of data (the
greater the database of knowledge of the ranking of one's relatives, the
better the estimate or prediction); and (iii) The similarity of
environmental factors.
Experimental Design
Identifying variation in immune response traits during
the peripartum period, and any association with disease or production
traits is the first step toward breeding dairy cows with superior health
attributes. To evaluate phenotypic variation in peripartum antibody and
cell-mediated immune responses of dairy cows, a total of 136 Holstein
dairy animals (88 cows and 49 heifers) from 2 research herds (Herd 1,
n=32, 6 heifers and 26 cows; Herd 2, n=67; 34 heifers and 33 cows) and 1
commercial herd (Herd 3, n=37, 8 heifers and 29 cows) were examined
weekly from dry-off (approximately eight weeks prepartum; wk-8) to six
weeks postpartum (wk 6). To stimulate specific antibody response during
the peripartum period, all cows and heifers received intramuscular (im)
injections of a mastitis endotoxemia preventive vaccine, an Rc mutant of
Escherichia coli 0111:B4 (Rhone Merieux Escherichia coli J5, Rhone
Merieux, Lenexa, KS) with the manufacturer's adjuvant. In addition,
cows were simultaneously administered ovalbumin antigen (OVA, Type
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VII, Sigma Chemical Co., St. Louis, MO) approximately 8 weeks (4 mg)
and 3 weeks (2 mg) prior to predicted calving dates. At parturition (wk 0),
cows received an additional immunization of the OVA dissolved in
phosphate buffered saline (PBS - 0.1 M, pH 7.4) (2 mg, im). Peripheral
blood was sampled via tail venipuncture at weeks -8, -3, 0, 3, 6, and 9
relative to parturition, and centrifuged to monitor serum IgGl&2, as well
as specific antibody responses to OVA and J5 E. coli. Colostrum and milk
samples were also collected to measure specific antibody to OVA and
total IgGl and IgG2 in whey. Colostrum was collected at the first milking
following parturition. Milk samples were stripped from all quarters
approximately 2-4 hr after morning milking. Colostrum and milk
samples were stored frozen without preservative at -20°C until time of
whey separation and Ig quantification.
In order to evaluate delayed type hypersensitivity (DTH)
as a measure of cell-mediated immune (CMI) response a subset (n=36) of
cows from research Herd 2 (Ponsonby Research Station, Elora, Ontario;
n=15 cows and 21 heifers) were given a 1.5 mg/mL intradermal injection
of the Bacillus Calmette Guerin (BCG; Connaught, Mississauga, Ontario)
vaccine in the left caudal tail fold at wk 1 postpartum. At wk 3
postpartum, animals that had received the BCG vaccine were given a 0.1
mL (250 US Tuberculin Units) intradermal injection of the purified
protein derivative (PPD) of Mycobacterium tuberculosis and 0.1 mL of
the control (PBS), in the right caudal tail fold. These sites were located
proximally to one another, about 4 cm apart. Injection sites in the left
and right caudal folds were located approximately the same distance from
the base of the tail head (10 cm) and across from one another. Double
skinfold thickness was measured at 48 and 72 hours using Harpenden
Skin Calipers (John Bull, England). As a measure of peripartum
lymphocyte proliferation, lymphocytes were harvested from whole blood
at weeks -3, 0, 3, and 6 relative to parturition and cultured with OVA
antigen (5 ~g/mL) and the T-cell mitogen concanavalin A (Con A; 5
~g/mL).
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Production Data
Production data were obtained through monthly reports
from the Ontario Dairy Herd Improvement Corporation (Ontario DHIC).
All monthly milk samples were tested by the Central Milk Testing
Laboratory, Guelph, Ontario, for SCC, and compositional content (fat%,
protein%). In addition, milk samples from cows in research Herd 1
(Shurgain Research Farm, Burford, Ontario; n=26 cows and 7 heifers)
were tested weekly by Ontario DHI. Projected 305 day production
parameters for milk, fat, and protein were used as a measure to compare
production between cows from the three herds investigated. Three
hundred and five day (305-day) projections were calculated based on at
least 100 days in milk (DIM). This allows comparisons between cows
which may not be at the same stage of lactation when a monthly milk
test is taken and between animals with varying lactation lengths.
Disease Data
Occurrence of infectious and metabolic diseases were
investigated throughout the study period. All disease events were
recorded by the herd manager. If an animal had two or more of the same
disease event, it was recorded as one event for the study period.
Specific Antibody Quantification by Enzyme Linked Immunosorbent
Assay (ELISA)
Anti-OVA antibody
Serum was separated from coagulated peripheral blood by
centrifugation (700 x g, 15 min) and stored frozen (-20°C) until time
of
assay. Milk samples were centrifuged twice (11000 x g, 15 min) to
separate fat from whey. Whey was stored frozen at -20°C. Antibody to
OVA was detected by ELISA according to the procedure described by
Burton, et al., 1993. Dynatech Imrrulon II flat bottom 96-well polystyrene
plates (Fisher Scientific, Don Mills, Ont.) were coated with a 3.11 x 10-5 M
solution of OVA (OVA, Type VII, Sigma Chemical Co., St. Louis MO)
dissolved in carbonate-bicarbonate coating buffer (pH 9.6). Plates were
incubated (4°C, 48h), then washed with PBS and .05% Tween 20 (Fisher
CA 02255423 1998-12-10
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Scientific, Don Mills, Ontario) wash buffer, (pH 7.4) using a EL403 plate
washer (Biotek, Mandel Scientific, Guelph, Ontario). Plates were then
blocked with a PBS-3% Tween 20 solution and incubated (rt, 1h). Plates
were washed and diluted test sera (1/50 and 1/200) or milk whey (Neat,
1 / 10, 1 / 100 and 1 /400) and controls were added using the quadrant
system described by Wright (1987). After blocking, sera samples were
added in duplicate, and milk whey samples were added in quadruplicate.
Plates were incubated (rt, 2h). Subsequently, alkaline phosphatase
conjugate rabbit anti-bovine IgG (whole molecule) (Sigma Chemical Co.,
St. Louis, MO) was dissolved in wash buffer, added to the plates and
incubated (rt, 2h). P-Nitrophenyl Phosphate Disodium tablets (pNPP)
(Sigma, St. Louis, MO) were dissolved in a 10% diethanolamine substrate
buffer, (pH 9.8). Plates were washed with wash buffer, pNPP was added to
the plates and then incubated (rt, 30 min). Plates were read on a EL311
automatic ELISA plate reader (BIO-TEK Instruments, Highland Park,
Vermont) and the optical density (OD) was recorded at 405 and 630
nanometres (nm) when the positive control reached OD>_.999. The 630
filter was used as a reference filter to correct for fingerprints and
irregularities in the plastic of the plates. The mean of the number of
replicates added to each plate was corrected to an OD = 1.0 by multiplying
by the inverse of the mean of the positive controls. Corrected means of
each dilution were then added together to give an additive OD value,
indicative of antibody response.
Negative and positive controls included a pooled sample
of pre-immunization sera and a pooled sample of sera from cows 14 days
post secondary immunization, respectively. Sera from 20 animals was
tested by ELISA to determine antibody responses at 4 dilutions (1/50,
1 /200, 1 /800, and 1 /3200). The dilutions 1 /50 and 1 /200 provided
responses with minimal prozone which corresponded to anticipated
antibody response curve kinetics based on the immunization schedule,
and allowed a clear differentiation between positive and negative
controls. Since for a small subpopulation of cows these dilutions
CA 02255423 1998-12-10
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exhibited some prozone effects, the dilutions were added together to
provide an index of antibody response. Similarly, in order to determine
the optimal sample dilutions that would be used to quantify antibody in
milk whey, milk from two cows was serially diluted (neat, 1/2,
1 /4...1 /512) to determine the dilution which had a minimal prozone, and
allowed optimal differentiation of responses of positive and negative
control sera. Acceptable dilutions included Neat, 1 / 10, 1 / 100 and 1 /400.
These dilutions were added together to give and index of whey antibody
response.
Anti-E.coli antibody
Lyophilized E.coli J5 (American Type Culture Collection,
Rockville, Maryland, USA) was grown in 5mL Tryptic Soy Broth (TSB)
for 2 days to obtain log phase growth. This culture was then transferred
to a 1 L flask of sterile TSB and sealed aseptically. The culture was
incubated (37° C, 12 hrs, 200 rpm) on an INNOVA platform shaker (New
Brunswick Scientific, Edison, New Jersey). A 1mL sample of cells was
diluted logarithmically and plated on blood agar to determine the colony
forming unit count (cfu). The number of cfu was 1.13 x 109. Live cells
were then pelleted by centrifugation (5000 g, 15 min). Cells were washed
in PBS and pelleted by centrifugation 3 times (first wash, 5000 x g, 15
min; second and third washes, 7500 x g, 15 min) Cells were suspended in
PBS at a final volume of 1 L. The culture was then heat-killed by boiling
for 2 hours. The final preparation was diluted until an absorbance
reading=1.0 at 540 nm was obtained. The E.coli J5 was stored frozen
(-20°C) until time of assay.
Serum was separated from coagulated peripheral blood by
centrifugation (700 x g, 15 min) and stored frozen (-20°C) until time
of
assay. According to the method described by Rhone-Merieux Animal
Health (Lenexa, KS; 1994 personal communication), heat-killed
Escherichia coli strain J5 (ATCC, Rockville, MD) was coated at a
concentration of 6.25 x 10~ cfu per mL onto Dynatech Imrrulon II
polystyrene 96 well flat bottom plates overnight at 4°C. After washing
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with wash buffer (PBS plus .05% Tween 20), 1% gelatin was added to
block non-specific binding and plates were incubated (rt, 1h). Plates were
washed and four replicates of test serum (dilutions of 1 / 1000, 1 / 1500,
1/2000 and 1/2500) were added using a modified quadrant system
(Wright, 1987). One column with PBS-05% Tween 20 was used as a
blank, one column of fetal calf serum (FCS, Bockneck Laboratories, Can
Sera, Rexdale, Ontario, Canada) was used as a negative control and one
column each of the negative and positive controls prepared from pooled
pre- and post immunization sera were plated, respectively. Test sera
were incubated (rt, 2h), and then the plates were washed with PBS-.05%
Tween 20. Horseradish peroxidase conjugate goat anti-bovine IgG whole
molecule in PBS (1/4000) (The Binding Site, Birmingham, England) was
added and the plates were incubated (rt, 1h). After subsequent washing
with PBS-.05% Tween 20, the substrate, 2,2'-azino-di-(4-ethyl-
benzthiazoline sulphonate-6) (ABTS) was added and plates were
incubated (rt, 30 min). Plates were then read on an EL311 automatic
ELISA plate reader (BIO-TEK Instruments, Highland Park, Vermont) and
the OD was recorded at 405 nm and 490 nm. The mean OD of the four
sample replicates were corrected for each plate by multiplying by the
inverse of the mean of the positive controls and used as an indicator of
antibody response. Based on the immunization protocol and phenotypic
observation of antibody response curve kinetics of all dilutions tested,
the 1 / 1000 dilution consistently allowed for differentiation between
positive and negative controls, exhibiting minimal prozone effect.
Therefore 1 / 1000 was the dilution of choice for comparison between
animals.
The same pooled positive sera used in the OVA ELISA
was tested to ensure a differentiation between pre-immune negative sera
and post secondary immunization sera. This positive control was
determined to be suitable for this assay since an OD of 1.0 was reached at a
dilution of 1 /200 while the negative sera had an OD that was <0.100.
Negative control sera in this assay was prepared by absorbing boiled
CA 02255423 1998-12-10
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whole cell E.coli J5 in pooled non-vaccinated sera. FCS was also used as
a negative control.
Quantification of Immunoglobulin G1&2 by Radial Immunodiffusion
(RID)
Quantification of Total IgGl~2 in sera
Radial immunodiffusion (RID) was used according to a
modified method described by Mallard et al, 1992, to determine the
concentrations of IgG 1 &2 in serum at weeks 0, 3, and 6 relative to
parturition. Immunodiffusion medium was prepared by dissolving 2%
Seakem agarose (FMC Bioproducts, Mandel Scientific, Guelph, Canada)
and 2% Polyethylene Glycol 8000 (Carbowax 8000, Fisher Scientific,
Fairlawn, NJ) in PBS. Rabbit anti-bovine isotype specific IgGl&2 (VMRD,
Pullman, WA) was suspended in the immunodiffusion agarose at a
concentration of 33% (vol/vol) for IgG1 and 30% (vol/vol) for IgG2.
Immunodiffusion medium was held in a liquid state and poured into 5
mL immunodiffusion plates. Agarose was allowed to solidify and then
three rows of wells, 6 wells per row, were punched with a 3mm glass
pipette tip. Standard concentrations of IgG1 (1800 mg/100mL) and IgG2
(1600 mg/100mL) as controls (VMRD, Pullman, WA) were diluted (neat,
1 /2, 1 /4, 1 /8, 1 /16, 1 /32) and five microlitres of these standard serial
dilutions were added to the top row of each plate. Five ~L of each test
sample was added to the two bottom rows of each plate. Plates were
incubated (rt, 20 h) in a humidified chamber. Afterwards, ring diameters
were measured using a calibrated grid held over a fluorescent light
source. Ring diameters from standards were used to make a standard
curve for each plate determined by linear regression. By plotting ring
diameter on the x axis and the log of the concentration (mg/100mL) on
the y axis, the concentration of Ig could be determined.
Quantification of Total IgGl~2 in whey
In order to determine Ig concentration in colostrum,
immunodiffusion medium was prepared by dissolving 2% Seakem
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agarose (FMC Bioproducts, Mandel Scientific, Guelph, Canada) and 2%
Polyethylene Glycol 4000 (Carbowax 3350, Fisher Scientific, Fairlawn, NJ)
in PBS. Rabbit anti-bovine isotype specific IgGl&2 (VMRD, Pullman,
WA) was suspended in the immunodiffusion agarose at a concentration
of 33% (vol/vol) for IgGl and 30% (vol/vol) for IgG2. The procedure for
the preparation of RID medium and plates for colostral whey samples
was essentially the same as that described for sera except that
polyethylene glycol 3350 was used instead of 8000 to improve ring clarity.
Colostrum samples were centrifuged twice (11000 x g, 15 min) to separate
fat from whey prior to plate application.
In order to determine Ig concentration in milk,
immunodiffusion medium was prepared by dissolving 2% Seakem
agarose and 2% Polyethylene Glycol 4000 (Carbowax 3350, Fisher
Scientific, Fairlawn, NJ) in PBS. Rabbit anti-bovine isotype specific IgGI
(VMRD, Pullman, WA.) was suspended in the immunodiffusion agarose
at a concentration of 12.5% (vol/vol). Milk samples were centrifuged
twice (11000 x g, 15 min) to separate fat from whey. The procedure for the
preparation of RID medium and plates for milk whey samples is
essentially the same as that described for colostrum except that the
concentration of goat-antibovine sera suspended in the
immunodiffusion media was 33% for IgG1 and 30 % for IgG2. Whey
from wk 3 was tested for both IgGl&2 subclasses. At wk 6 however, IgG1
only was tested in whey since very low concentrations of IgG2 exist in
normal milk.
Examination of the Cell- Mediated Immune Response (CMIR)
Delayed Type Hypersensitivity
A preliminary study was conducted to determine if the
Ponsonby herd was previously exposed to Mycobacterium tuberculosis or
other cross reactive antigens from Mycobacterium paratuberculosis. Five
cows and six heifers were injected intradermally with 0.1 cc of the PPD of
M. tuberculosis (Connaught, Mississauga) and a control dose of 0.1 cc PBS
CA 02255423 1998-12-10
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(pH 7.4) in the right caudal tail fold located proximally to one another
(approx. 4 cm apart) PPD was injected in a designated area above the PBS
site. Both injection sites were 10 cm from the base of the tail head. Prior
to injection, injection sites were encircled with a coloured marker and a
pre-test and pre-control thickness measurement was taken in triplicate,
using Harpenden skin calipers (John Bull, England). This measurement
was identified as the time=zero hours measurement. After 24 and 48
hours, skin thickness measurements were taken to assess the percent
increase in skin thickness of control and test sites. It was determined that
the herd had not previously been exposed to the M. tuberculosis antigen
since 95% of all the animals tested had very little or no increase in skin
thickness at the injection sites (i.e a 0-7% increase in skin thickness) and
that the BCG/PPD test system would be suitable to measure DTH
responses in this herd.
Two animals from the Ponsonby herd were selected to
determine the optimal time point following the injection of PPD that
would yield a maximal response and ensure that actual DTH responses
were induced. Animals were evaluated at 0, 6, 12, 24, 48 and 72 hours
post PPD challenge. Measurements taken at 6 to 12 hours were used to
ensure that the response to antigen was not characteristic of an antibody-
mediated reaction. In cattle, the maximal response to PPD is normally
observed around 72 hours (Radostits et al, 1990). Preliminary results
indicated that the response was optimal at 48 hours, therefore both time
points were evaluated for comparison between animals.
Prior to immunization using PPD, and a PBS control, a
pre-test and pre-control (at time= 0 hours) skin thickness measurement
was obtained in triplicate from each of the 36 animals evaluated. Forty
eight and 72 hours after secondary challenge, these measurements were
taken again. The amount of skin thickness increase at 48 and 72 hours
expressed as a percent increase in skin thickness was calculated as
follows:
increase in skin thickness =(((A-B)/B)-(C-D)/D)))X100
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where A=mean test thickness (at time=48, 72 hours),
B=mean of pre-test thickness (at time=0 hours),
C=mean of control thickness (at time=48, 72 hours),
D=mean of pre-control thickness (at time=0 hours).
Cows could be classified according to their % increase in
skin thickness as either non-responsive or low responders (less than one
sd below the mean), moderate responders (between one sd below and one
sd above the mean), or high responders (more than one sd above the
mean).
Lymphocyte Proliferative Response
Lymphocyte proliferation assays were performed
according to the procedure of Chang, et al. (1993). Peripheral whole blood
was centrifuged (850 x g, 15 min) and huffy coats were diluted in
phosphate buffered saline (PBS 0.1M, pH 7.4). Peripheral blood
lymphocytes (PBL) were separated by density gradient centrifugation
(1000 x g, 30 min) of huffy coats using aqueous Histopaque 1.077 (Sigma
Chemical Co. St. Louis, MO.) Cell pellets were washed by centrifugation
in PBS (400 x g, 7 min) and suspended in a volume of culture medium
(Rosewell Park Memorial Institute; RPMI- 1640, and 100 LU. penicillin-
streptomycin, prepared by Central Media Laboratory; Ontario Veterinary
College, University of Guelph, Guelph, Ontario.) and 10% FCS and
brought to a final concentration of 2.0 x 10 6 cells/mL in culture medium.
In order to determine specific clonal proliferative responses to antigen, a
stock solution (50 ~g/mL) of OVA (Sigma Chemical Co., St. Louis MO)
dissolved in RPMI - 1640 was prepared and stored in small aliquots at -
70°C. Five ~g/mL of OVA was added to 6 replicates of test lymphocytes
in 96 well flat-bottom plates (Nunc, Fisher Scientific, Don Mills, Ontario).
Medium was added to 6 well replicates of cells as non-stimulated controls
and this represented background or unstimulated cell proliferation. As a
general indicator of lymphocyte proliferation, Con A mitogen similarly
prepared from stock solution (50 ~g/mL) and diluted (5 ~,g/mL) was
added to 6 replicates of cells on a separate plate containing an additional 6
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wells as medium controls. Following 24 h of incubation with OVA or
Con A(37°C, 6% C02) cells received an 18 h 'pulse' incubation with
0.5
~Ci methyl tritiated thymidine per well (ICN Biochemical, Canada Ltd.
Montreal, PQ). Plates were frozen until cells were harvested using a plate
harvesting system (LKB Wallac, Turku, Finland) onto fiberglass filter
mats (LKB Wallac, Turku, Finland). Radioactivity was recorded as
counts per minute (cpm) of test minus non-stimulated controls of
retained radioactivity measured by a beta plate liquid scintillation
counter (LKB Wallac,Turku, Finland).
OVA antigen preparations were tested using the above
described method at a concentration of 5 ~g/mL, 10 ~g/mL, and 20
~.g/mL. Although lymphocyte proliferative responses did not differ
significantly between the tested concentrations, 5 ~.g/mL was selected to
induce PBL proliferation in subsequent assays. To determine the
concentration of the mitogen able to induce optimal PBL proliferation,
Con A concentrations were tested at 2~g/mL, 5~.g/mL and 10~g/mL.
Five ~.g/mL yielded maximal lymphocyte proliferative responses and
was therefore selected as the concentration applied in further
investigations.
Flow Cytometric Assay for the detection of CD Surface Molecules on
Lymphocytes either not stimulated or stimulated with Con A or OVA
In order to determine which lymphocyte subsets were
present after stimulation with either Con A or OVA, cells were stained
with monoclonal antibodies recognizing 5 cell surface markers according
to the method described by Van Kampen and Mallard (1997). The
monoclonal antibodies used in this study were kindly provided by Dr.
Jan Naessens of ILRAD (Institute for Animal Health, Compton,
Berkshire) and included antibodies to the following cell surface markers:
CD2+ (IL-A43), CD4+ (IL-A11), CD8+ (IL-A105), WCI (IL-A29), and IgM
(IL-A30). A subset of animals (n=10) from research Herd 2 (Ponsonby,
Elora, Ontario; n=7) and the commercial herd (Speedvalley Holsteins,
Fergus, Ontario;n=3) were evaluated for expression of these lymphocyte
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cell surface markers at weeks -3, 0, 3, and 6 relative to parturition.
Lymphocytes were prepared and cultured as previously described for
lymphocyte proliferation assays, however, each 96 well plate was divided
into quadrants each with 24 wells. Twenty four replicates each of Con A
stimulated, OVA stimulated (at 5~g/mL and 20~g/mL) and non-
stimulated controls were cultured for 42 hours (the same total duration
used in the lymphocyte proliferation assays). After 42 hours, cells were
harvested by pipette, washed with PBS and transferred to 10 mL glass test
tubes. Cells were centrifuged (400 x g,10 min), and supernatants were
poured off and cells were resuspended in 250 ~L PBS + 0.1M Azide.
Immunostaining was performed in 96-well round-bottom plates
(Corning, New York, NY). Fifty ~L of cells and 50 ~L of diluted primary
antibody were added to each well and incubated (20 min, rt). After
incubation, 100 ~L of PBS + 0.1M sodium azide (Fisher Scientific,
Fairlawn, NJ) was added to each well to wash the cells. Cells were
suspended by mixing on a shaker and centrifuged (400 x g, 6 min).
Supernatants were then removed using an aspirator. This washing
procedure was performed twice. Fifty ~L of FITC-conjugated goat anti-
mouse IgG(H+L) (Cedarlane Laboratories, Hornby, Ontario) was then
added to the cells and cells were incubated (rt, 20 min). After incubation,
plates were washed twice as described above. Cells were fixed in 1%
paraformaldehyde and transferred into 3 mL polystyrene tubes (Becton
Dickinson, Lincoln Park, NJ) containing 300 ~.L of 1% paraformaldehyde.
Tubes were covered with parafilm and refrigerated until time of assay.
A FACS Scan flow cytometer (Becton Dickinson, Lincoln
Park, NJ) was used to acquire all lymphocyte subset data. LYSIS II
software (Becton Dickinson, Lincoln Park, NJ) was used for fluorescence
data analyses. Lymphocytes were gated from other populations based on
their forward and side scatter characteristics. Five FITC histograms were
plotted for each cow, time point and culture condition observed.
Histograms representing fluorescence of cells expressing CD2 (pan T cell),
CD4 (helper T cells), CD8 (cytotoxic/suppressor T cells), WCI (gd T cells),
CA 02255423 1998-12-10
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and IgM (B cells) cell surface markers were examined. The region of
background fluorescence was established with the negative control
marker, M1. Everything to the right of this marker was considered
positive.
Complete Blood Cell Counts
Complete Differential Blood Cell Counts were
determined by the Clinical Pathology Laboratory at the Ontario
Veterinary College, University of Guelph, Guelph, Ontario, Canada.
Counts included the percent and number erythrocytes, banded
neutrophils, segmented neutrophils, lymphocytes, monocytes, basophils,
eosinophils, as well as total leukocytes.
Somatic Cell Counts in Milk
Weekly somatic cell counts (SCC) of the Shurgain herd
were obtained using the weekly sampling service offered by Ontario
DHI. Weekly samples of cows in the Ponsonby and Dunk herds
sampled 1-4 hours after morning milking were tested for SCC by the
Mastitis Laboratory at the Ontario Veterinary College, University of
Guelph, Guelph, Ontario Canada. Monthly somatic cell counts were
obtained from Ontario DHI records for all three herds.
Categorization of Cows Based on Antibody Response
Biological Classification Using Antibody Response Curves
Serum antibody responses to OVA from the first herd
investigated (Shurgain, Burford, Ontario, n=32) were graphed
individually for each cow at weeks -8, -3, 0, 3, and 6 to examine response
curve patterns. Evaluation of these curves during the peripartum
period through to peak lactation indicated that enough variation existed
to rank animals according to antibody response to OVA. Cows that
showed consistently above average responses to OVA were categorized as
high or Group 1. Cows that had an average antibody response up until
parturition and thereafter showed a lack of measurable (LOM) antibody
response were categorized as the post-partum LOM response group or
Group 2. Cows that had an average antibody response until three weeks
CA 02255423 1998-12-10
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pre-partum and showed a progressive decline in measurable antibody
response were categorized as the peripartum LOM group or Group 3.
Subsequent investigations of immune responses between
cows in the other herds studied revealed similarities. However, subtle
differences in the amplitude and direction of antibody response curves,
in relation to the immunization schedule, indicated that the data was
continuous in nature. Thus, the groups determined for Herd 1 wouldn't
necessarily apply to all herds. It was clear then, that antibody responses to
OVA were on a continuum, and any classification method implemented
would benefit from a quantitative approach to readily and appropriately
partition phenotypic variation between cows.
Quantitative Classification Using a Mathematical Index
Serum antibody responses to OVA were evaluated over
time intervals, rather than discrete points in time. Individual animal
antibody response curves from week -8 to week 6 relative to parturition
(week 0) were dissected into components reflecting the response to
antigen following immunizations. Primary response was defined as the
change in antibody to OVA from week -8 to week -3 relative to
parturition following primary immunization at week -8 (Primary= OD
value at week-3 minus OD value at week -8). Secondary response was
defined as the change in antibody to OVA from week -3 to parturition
following secondary immunization at week -3 (Secondary= OD value at
week 0 minus OD value at week -3). Tertiary response was defined as the
change in antibody to OVA from parturition to week 3 following tertiary
immunization at parturition (Tertiary=OD at week 3 minus OD at week
0). Quaternary response was defined as change in antibody to OVA from
week 3 to week 6 (Quaternary= OD value at week 6 minus OD value at
week 3). Quaternary response was included to observe the change in
antibody response between the end of the immediate postpartum period
(wk 3) and peak lactation. These responses were added together to give
an index of antibody response to OVA between wk -8 and wk +6 relative
to parturition as follows:
CA 02255423 1998-12-10
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yindex- primary + secondary + tertiary + quaternary
where,
y= total antibody response;
primary, secondary, tertiary, and quaternary responses are as previously
defined;
primary, secondary, tertiary, and quaternary responses when positive,
have an equal weight of 1.
Animals which exhibited negative secondary or tertiary
responses during the immediate pre-and post-partum period were
weighted with a coefficient of 1.5 instead of 1. Only secondary and
tertiary responses were weighted in this manner, since this is the period
when lowered host resistance mechanisms are thought to contribute to
increased occurrence of disease. The coefficients for weighting negative
secondary and tertiary responses were optimized using the original
biological assessment for grouping animals in the first herd investigated.
The quantitative ranking of animals had to reflect the biological
assessment of grouping animals based on the magnitude and direction of
response to immunization.
The mean of the antibody response index was determined
and animals that exceeded one standard deviation above the mean were
classified as high responders (Group 1). Animals that were one standard
deviation below the mean were classified as low responders (Group 3).
Animals with an index of antibody response that ranged between one
standard deviation below and above the mean were classified as average
responders (Group 2).
Statistical MetYcods
Least squares analysis of variance (ANOVA) and
corrected means (least square means, LS Means) were generated using the
General Linear Models (GLM) Procedure of the Statistical Analysis
System (Helwig and Council, 1982). A model was constructed for the
following dependent variables: antibody response to OVA in sera and
whey, antibody response to E.coli in sera, concentration of IgGl&2 in
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serum and whey, background lymphocyte proliferation and lymphocyte
proliferation following culture with Con A or OVA, DTH, SCS and
production variables. Sources of variation included in the model for
each dependent variable are summarized in Table 1. Data that did not
show a normal distribution, as indicated by the univariate procedure of
SAS (Helwig and Council, 1982), were transformed to natural
logarithms. The Proc CORR procedure of SAS was used to generate
Pearson product moment correlation coefficients between immune
response parameters and production variables. Results were considered
to be statistically significant if the p-value was <_0.05 and trends were
reported at a p-value <_0.10.
Models indicated are base models. Some parameters were excluded if
non-significant in order to generate LS Means.
Model l: Antibody response to OVA in serum and whey, Ig in
serum and whey and E. coli in serum
yl~~, _ ~ + herd; + season-yr~ + cow(group*parity)klm
+ weekn + groupk + parityl + (group*parity)kl
+ (group*week)kn + eq~klmno
where,
y;~~",o = observed response of cow m in group k and parity 1 for
each sample week of each cow,
. = the population mean,
herdi = fixed effect of herd (i=1,2,3),
season-yr~ = fixed season-year effect (j= Spring 1994, Summer 1994,
Fall 1994, Winter 1994/1995, Spring 1995, Summer 1995,
Fall 1995, Winter 1995/96),
groupk = fixed effect of group based on antibody response to OVA
(k=1,2,3),
parityl = fixed effect of parity (1=1,2, or >3 ),
(group*parity)kl = fixed effect of group*parity interaction,
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cow(group*parity)klm - random effect of cow-grouped within
group*parity term,
weekn = fixed effect of sample week (n= -8,-3, 0, 3, 6, 9),
(group*week)kn =fixed effect of group by week interaction
term;
eijklmno = random or residual error term.
When parity was not significant, the cow term was edited
to reflect the appropriate nested variable
Model 2
Cell Mediated Immune Responses and Lymphocyte proliferation
Ylj~t~,r,oP = ~ + herdi + season-yrj + cow(group*parity)kl~, + weekn +
groupk + parityl + (group*parity)kl + (group*week)kn +
replicateo + b(cov)ijkmo + eijklmnop
where all variables are as described for model 1 except,
ylj~,.r".,op = observed response of cow m in group k and parity 1 for
each replicate o at each sample week ,
replicateo = fixed effect of replicate (0=1,2,3,4,5,6), and
b(cov)ijkmo = regression coefficient of yijktmnop on resting cell
proliferation for the klmth cow
The model for DTH was:
Yij = ~ + group; + eij;
where, ~,= the population mean,
group; = fixed effect of group based on antibody response
to OVA (i=1,2,3),
e;j = random or residual error term.
Parity was not included in this model since it was not
significant, and when included with group, did not provide enough
degrees of freedom to run the analysis of variance.
Tests of hypothesis of group or parity were tested against
the MS random error term for cow. Type III Sums of Squares corrected
CA 02255423 1998-12-10
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for all other variable within the model were used account for the
variation in immune responses.
The following non-limiting examples are illustrative of
the present invention:
EXAMPLES
EXAMPLE 1
Periparturient Antibody Response Profiles of Holstein Cows: An Initial
Immunobiological Assessment
To evaluate phenotypic variation in peripartum immune
responsiveness of dairy cattle, 33 Holstein cows were immunized with
ovalbumin (OVA) and Escherichia coli J5 at weeks -8 and -3 prior to
parturition. At parturition (week 0), cows received an additional
immunization of OVA. Blood was collected at weeks -8, -3, 0, 3 and 6
relative to parturition to measure serum immunoglobulin (Ig)
concentration, and antibody to OVA and E.coli. Colostrum and milk
were also collected post-parturition to measure Ig and antibody to OVA.
All cows had a measurable antibody to OVA following primary
immunization, but not all cows responded to second and/or third
immunizations. Antibody response to OVA was used to classify cows
into three groups recognizing animals with sustained measurable
antibody response before and after parturition (Group 1), animals which
responded poorly or did not respond to immunization at parturition
(Group 2), and animals which did not respond to immunizations at week
-3 or at parturition (Group 3).
The objectives of this example were threefold: 1) to
investigate antibody response during the peripartum period; 2) to
classify cows based on variation of antibody response; and, 3) determine if
antibody response is associated with the occurrence of disease.
Materials and Methods
Animals and Treatments
Antibody response of 33 Holstein cows were examined
from approximately eight weeks prepartum (week -8), based on predicted
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calving dates to six weeks postpartum (week 6). Twenty-six animals were
multiparous cows and seven were primiparous heifers. Cows received
an intramuscular (im) injection of a mastitis endotoxemia preventive
vaccine with the manufacturer's adjuvant (Rhone Merieux E. coli J5,
Rhone Merieux, Lenexa, KS) along with the antigen OVA (Type VII,
Sigma Chemical Co., St. Louis, MO), at weeks -8 (4 mg OVA) and -3 (2 mg
OVA). At parturition (week 0), cows received an additional
immunization of OVA without adjuvant dissolved in phosphate
buffered saline (PBS - 0.1 M, pH 7.4) (2 mg, im). Ovalbumin was chosen
as an inert soluble antigen to which these animals had likely not been
previously exposed. E. coli J5 was used as a complex, insoluble,
biologically relevant antigen to which most dairy cows were likely to
have been previously exposed. Antibody response to OVA was used to
classify cows into three groups recognizing animals with sustained
measurable antibody response before and after parturition (Group 1),
animals which responded poorly or did not respond to immunization at
parturition (Group 2), and animals which did not respond to
immunizations at week -3 or at parturition (Group 3)(Fig. 1A).
Blood and Milk Sam~nling Schedule
Blood was collected by tail venipuncture at week -8, and
weekly from weeks -3 to 6 relative to parturition. Serum was used to
monitor immunoglobulin G1&2 concentrations, and determine antibody
to OVA and E. coli J5. Colostrum and milk were collected to determine
antibody to OVA and to monitor IgG1 (weeks 0, 3, 6) and IgG2 (weeks 0
and 3) concentration. Colostrum was collected at the first milking
following parturition. Milk was obtained from all quarters
approximately 2-4 hr after morning milking. Colostrum and milk
samples were frozen without preservative at -20°C until the time of
whey separation and analysis.
Anti-OVA Enzume Linked Immunosorbent assau (ELISA)
As described in the General Methods section.
Anti-E. coli T5 ELISA
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As described in the General Methods section.
Radial Immunodi~'fusion Assai!
As described in the General Methods section.
Disease Occurrence
As described in the General Methods section.
Milk Somatic Cell Count
Milk (AM/PM composite sample) was collected weekly by
the herd milker during milking to determine somatic cell count (SCC).
Only SCC which coincided with the day of blood sample collection for
each week are reported. SCC, an indicator of subclinical mastitis, was
transformed to somatic cell score (SCS) for analysis. SCS is the natural
logarithm of SCC in cells/~L and is calculated as follows (Shook, 1993):
SCS=loge(SCC / 100) = loge(2) + 3
Statistical Methods
Type III least squares analysis of variance (ANOVA) and
corrected means (least square means, LS Means) were generated using the
General Linear Models (GLM) Procedure of the Statistical Analysis
System (SAS; Helwig and Council, 1982). The statistical models used
included fixed effects of antibody response groups (1,2,3), cow nested
within antibody response group, and week relative to parturition (weeks
-8, -3, 0, 3, and 6). In preliminary analysis, the effect of parity was not
significant and was therefore removed from all subsequent models. A
model was constructed for the following dependent variables: antibody
response to OVA in sera and whey, antibody response to E. coli J5 in sera,
and the concentration of IgGl&2 in serum and whey. Sources of variation
included in the model for each dependent variable are summarized in
Table 1. Data that were not normally distributed as indicated by the
univariate procedure of SAS, were transformed to natural logarithms.
(whey antibody to OVA, serum antibody to E. coli, serum and whey IgG2.
Pearson product moment correlation coefficients between immune
response variables were generated using the correlations procedure of
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SAS (Proc CORR). Results were considered to be statistically significant
if the P-value was <_.05 and trends were reported at P-values <_.10.
Results
Antibod~r Resbonse to OVA
Antibody in serum
Serum antibody to OVA varied significantly over the
peripartum period and individuals could readily be classified into three
immune response groups: high responders (Group 1, n=12; 6 heifers, 6
cows) versus animals which exhibited a LOM response to immunization
either postpartum (Group 2, n=12 cows) or pre- and postpartum (Group 3,
n=9; 8 cows, 1 heifer). Approximately 1/3 (Group 1) of the animals had
consistently above average serum antibody response to OVA following
immunization at weeks -8, -3, and 0 relative to parturition. The
remaining animals had OD values measuring antibody to OVA that were
close to the population mean or had responses lower than the
population mean and did not respond following immunization at week -
3 or parturition (Fig. 1A). All cows, including those of Group 3, had
serum antibody greater than background (week -8) at week -3 and
therefore were considered low responders rather than non-responders.
The statistical model (ANOVA) accounted for 94.19% of the total
variation in serum antibody to OVA over the peripartum period. Effects
of cow (P<_.0001), antibody response group (P<_.0001), week (P<_.0001), and
the interaction between antibody response group and week (P<_.0001),
contributed significantly to the variation in serum antibody to OVA
(Table 1).
Antibody in Whey
Cow (P<_.0001), week (P<_.0001), and antibody response
group (P5.0001) contributed significantly to the variation in antibody in
whey (Table 1). There was also a tendency for the interaction between
antibody response group and week (P<_.09) to account for variation in
whey antibody to OVA. Population LS Means of whey antibody to OVA
declined significantly following parturition, such that at week 0 the OD
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value was 1.456 compared to 0.645 (P5.004) at week 3 and 0.366 ~ .20
(P<_.0001) at week 6 (Fig. 1B). At weeks 3 and 6, Group 1 cows were
significantly higher than (P<_.05) Group 3 cows.
Antibodu Response to E. coli T5
Cow (P<_.0001), week (P<_.0001), and antibody response
group (.0001) all contributed significantly to variation in antibody
response to E. coli J5. OD values of pre-immunization sera (week -8)
indicated that these cows had minimal measurable E. coli J5 antibody
(population mean of OD = .296; n=33) compared to post-vaccination
antibody at week -3 (.739) and week 0 (.789). Antibody response to E. coli
J5 was positively correlated with antibody response to OVA (r2=.59,
P<_.0001).
~I ~ IgG2 in serum, colostrum, and milk
Antibody response group significantly contributed to the
variation of serum IgG2 (P<_.0001) only. Group 3 cows had a significantly
(PS0.05) higher serum IgG2 concentration than Groups 1 and 2 at
parturition. Antibody to OVA was negatively and significantly correlated
with serum IgG2 (r=-0.23; P<_0.05).
Disease Occurrence
Fifty four and a half % of the 33 animals evaluated were
considered healthy during this study. Of the diseased animals, seven
cows had mastitis (21.21%), seven had ketosis (21.21%) and three cows
had other diseases (9.09%). Animals in Group 1 that had above average
antibody to OVA, had the lowest percent occurrence of disease (17%) (Fig.
2) and actually had no clinical mastitis.
3.5. Somatic Cell Score (SCS)
At parturition, LS Means of SCS were significantly lower
(P<_.05) for Group 2 cows (SCS=3.2) compared to Group 1 (SCS=4.36) and
Group 3 (SCS=4.98) cows. At weeks 2,3,4, and 6 after parturition, all
groups differed significantly from one another, and, Group 1 cows
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consistently had the lowest SCS while Group 3 cows consistently had the
highest SCS.
Discussion
Antibody response before and after parturition has not
been thoroughly investigated. Antibody response to OVA, a test antigen
to which these animals would normally not have and had probably not
been previously exposed, was utilized to partition cows into three
immune response groups recognizing animals with sustained antibody
response before and after parturition (Group 1), animals which did not
respond to immunization at parturition (Group 2), and animals
responding poorly throughout the peripartum period (Group 3).
Variation in antibody response to E. coli J5, a biologically relevant
antigen, was more difficult to partition. Pre-immunization E. coli
antibody was significantly lower compared to post immunization
antibody in this herd. This indicates that the E. coli J5 antigen would be
useful for classifying animals in the herd evaluated according to their
antibody response but does not indicate that another herd will respond in
the same way. Pre-immunization antibody may be higher in other herds
where gram negative bacteria are frequently encountered.
Nagahata et al. (1992), examined B lymphocyte
populations in order to evaluate host defense in dairy cows during the
periparturient period. This study found no significant changes in the
number of B lymphocytes of cows from two weeks before until two
weeks after parturition. However, they did report a significant decrease
in antibody producing cells immediately after parturition. The authors
suggested this indicated a decrease in B lymphocyte function during the
immediate postpartum period. This is consistent with the low
peripartum antibody response seen in some animals in the present study.
Although it has been reported that serum antibodies
decline at parturition and colostral antibodies increase due to the
sequestration of immunoglobulins into the mammary gland (Detilleux
et al., 1995), this study suggests that lower antibody in serum does not
CA 02255423 1998-12-10
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necessarily relate to Ig transport. For instance, Group 1 cows, which had
the highest serum antibody responses, also tended to have higher whey
antibodies to OVA postpartum, when compared to cows of Groups 2 and
3. Initially, it was questioned whether low serum antibody may be
associated with higher antibody in the colostrum or milk. This data
indicates that animals with high serum antibody also supply high
concentrations of antibody to the mammary gland.
This example has demonstrated significant individual
variation during the peripartum period and confirms that not all cows
have depressed antibody response. In swine, animals with inherently
high and low immune response phenotypes can also be identified in a
population (Mallard et al., 1992). In light of previously reported
heritability (h2) estimates of bovine antibody response (Burton et al.,
1989), these data from this study may suggest that Group 1 animals could
be inherently better able to produce antibody, in spite of the metabolic
and physical stresses of the peripartum period. Cows in Group 1 did
have the lowest occurrence of peripartum disease, particularly mastitis
(0% occurrence), and significantly lower SCS scores following parturition
than cows in Groups 2 and 3, thus indicating that antibody response
should be considered as a potential marker of peripartum disease
resistance.
EXAMPLE 2
A Quantitative Approach to Classifying Holstein Dairy Cows Based on
Antibody Response, the Relationship Between Antibody Response and
Peripartum Disease Occurrence, and Heridability Estimates
A quantitative approach was developed to partition
phenotypic variation of peripartum antibody response profiles of
Holstein cows and to determine associations with peripartum mastitis.
Using a mathematical index, 136 cows and heifers from three herds were
ranked as high responders (Group 1), average responders (Group 2) or
low responders (Group 3) to OVA. Grouping animals by serum antibody
response to OVA indicated that animals ranked similarly for antibody to
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OVA in whey and antibody to Escherichia coli in serum. Differences in
serum and whey IgG 1 concentrations between antibody response groups
were not significant. Serum IgG2 concentration however, varied
between group, within herd and across time. Whey IgG2 did not differ
significantly between antibody response groups within herd. Occurrence
of mastitis was negligible for Group 1 animals. In contrast, Group 1
animals from Herd 2, had the greatest occurrence of mastitis while
Group 3 had the lowest. Milk somatic cell score (SCS), was lowest for
Group 1 animals in Herd 1 and lowest for Group 3 animals in Herd 2,
thus supporting the distribution frequency of clinical mastitis in those
herds. Herd 3 SCS did not differ significantly between antibody response
groups and did not underscore the distribution of clinical mastitis.
The objective of this study was to confirm the existence of
high and low antibody response profiles amongst individuals across
three herds and to devise a method for quantitatively classifying cows
into groups based on antibody response to standardized immunization
protocols. Relationships were evaluated between antibody response,
immunoglobulin concentration, milk somatic cell score, and disease
occurrence with respect to antibody response group.
Materials & Methods
Animals and Treatments
Antibody responses of 136 Holstein dairy cows and heifers
from 2 research herds (Herd 1, n=32, 6 heifers and 26 cows; Herd 2, n=67;
34 heifers and 33 cows) and 1 commercial herd (Herd 3, n=37, 8 heifers
and 29 cows) were examined from eight weeks prepartum (week -8)
based on predicted parturition dates to six weeks postpartum (week 6).
Forty nine animals were primiparous heifers, 47 animals were in their
second lactation and 41 were multiparous cows (>2 lactations). Antibody
responses were evaluated as previously described (Mallard et al., 1997;
Ch. V ). Animals received an intramuscular (im) injection of ovalbumin
(OVA; Type VII, Sigma Chemical Co., St. Louis, MO) and a mastitis
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endotoxemia preventive vaccine with the manufacturer's adjuvant
(Rhone Merieux E. coli J5, Rhone Merieux, Lenexa, KS) at weeks -8 (4 mg)
and -3 (2 mg). At parturition (week 0), animals received an additional
immunization of OVA in phosphate buffered saline (PBS - 0.1 M, pH 7.4)
(2 mg, im). OVA was chosen as an inert test antigen to which these
animals had not likely been previously exposed. E. coli J5 was used
because dairy cows could be expected to have been previously exposed to
E. coli, a complex antigen, having biological relevance.
Blood and Milk Samroling Schedule
Blood was collected by caudal tail venipuncture at
approximately week -8 relative to parturition, and weekly from weeks -3
to 6 relative to parturition. Samples were used to monitor serum
immunoglobulin G1&2 and serum antibody to OVA and E. coli J5.
Colostrum and milk samples were collected to monitor whey IgGl&2 and
antibody to OVA in whey. Colostrum was collected at the first milking
following parturition. Milk samples were stripped from all quarters
approximately 2-4 hr after morning milking. Colostrum and milk
samples were stored frozen without preservative at -20°C until time of
whey separation and immunoglobulin quantification.
ELISA for OVA Antibodu Detection In Serum and Wheu
Antibody to OVA was detected by ELISA, and quantified
based on optical density measurements according to a procedure
previously described (Mallard et al., 1997; Ch. V). Sera samples (weeks -8,
-3, 0, 3, and 6) diluted 1 /50 and 1 /200 were assayed in duplicate. Whey
samples (weeks 0,2,3,4, and 6) diluted 1 / 10, 1 / 100, 1 /400 and undiluted
were assayed in quadruplicate.
ELISA for E. coli X15 A~ Detection In Serum
Antibody response to E. coli J5 was measured according to
the method previously described (Mallard et al., 1997; Ch. V). Serum
samples (weeks -8,-3, 0, 3, and 6) diluted 1 / 1000 were assayed in
quadruplicate.
Radial Immunodi~fusion Assay
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Radial immunodiffusion was used according to the
method described by Mallard et al. (1992) to determine the concentrations
of serum IgGl&2 at weeks 0, 3, and 6 and whey IgGl at weeks 0, 3, and 6
and whey IgG2 at weeks 0 and 3.
Quantitative Classification of Animals Based on Antibodu Response
Serum antibody responses to OVA were evaluated over
time intervals, rather than discrete points in time. Individual animal
antibody response curves from week -8 to week 6 relative to parturition
(week 0) were dissected into components reflecting the response to
antigen following immunizations. Primary response was defined as the
change in antibody to OVA from week -8 to week -3 relative to
parturition following primary immunization at week -8 (Primary= OD
value at week-3 minus OD value at week -8). Secondary response was
defined as the change in antibody to OVA from week -3 to parturition
following secondary immunization at week -3 (Secondary= OD value at
week 0 minus OD value at week -3). Tertiary response was defined as the
change in antibody to OVA from parturition to week 3 following tertiary
immunization at parturition (Tertiary=OD at week 3 minus OD at week
0). Quaternary response was defined as change in antibody to OVA from
week 3 to week 6 (Quaternary= OD value at week 6 minus OD value at
week 3). Quaternary response was included to observe the change in
antibody response between the end of the immediate postpartum period
(wk 3) and peak lactation. These responses were added together to give
an index of antibody response to OVA between wk -8 and wk +6 relative
to parturition as follows:
YindeX= primary + secondary + tertiary + quaternary
where,
y= total antibody response;
primary, secondary, tertiary, and quaternary responses are as previously
defined;
CA 02255423 1998-12-10
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primary, secondary, tertiary, and quaternary responses when positive,
have an equal weight of 1.
Animals which exhibited negative secondary or tertiary
responses during the immediate pre-and post-partum period were
weighted with a coefficient of 1.5 instead of 1. Only secondary and
tertiary responses were weighted in this manner, since this is the period
when lowered host resistance mechanisms are thought to contribute to
increased occurrence of disease. The coefficients for weighting negative
secondary and tertiary responses were optimized using the original
biological assessment for grouping animals in the first herd investigated.
The quantitative ranking of animals had to reflect the biological
assessment of grouping animals based on the magnitude and direction of
response to immunization.
The mean of the antibody response index was determined
and animals that exceeded one standard deviation above the mean were
classified as high responders (Group 1; n=18). Animals that were one
standard deviation below the mean were classified as low responders
(Group 3; n=23). Animals with an index of antibody response that ranged
between one standard deviation below and above the mean were
classified as average responders (Group 2; n=95).
Heritability Estimates
Sire and error variance components of serum antibody to
OVA were estimates by REML using Variance Component Estimation
(VCE) software (Groeneveld, E. 1994). Sire and error variances were used
to estimate paternal half-sib heritabilities for serum antibody to OVA at
weeks -8, -3, 0, 3, and 6 relative to calving. Approximate standard errors
were computed from the variance covariance matrix of sire and error
variance component estimates.
Mastitis Occurrence
Occurrence of clinical mastitis was recorded throughout
the study period by herd managers. Two or more events of mastitis it
were recorded as one event for the study period (Martin et al., 1993).
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Incidence of mastitis occurrence was calculated by dividing the number
of animals within an antibody response group that had at least one
disease event by all the animals in that antibody response group, and
multiplying this number by 100. Mastitis occurrence was evaluated for
associations with antibody response group within each herd, using odds-
ratio (OR) (Martin and Meek, 1987). Odds-ratios in this study was
calculated on a within herd basis, as the ratio between the rate of mastitis
in one antibody response group versus the rate of mastitis in the rest of
the herd (i.e. the other two groups). Odds-ratio is the approximate
relative risk when the rate of disease in the population is relatively
infrequent (<5%) (Martin and Meek, 1987). Odds ratios values were
tested for significance using the chi-square test (Martin and Meek, 1987).
Milk Somatic Cell Count
Milk (AM/PM composite sample) was collected weekly to
determine somatic cell count (SCC), an indicator or subclinical mastitis.
Only SCC which coincided with blood sample collection for each week
were used in evaluation. SCC was transformed to somatic cell score
(SCS) for analysis. SCS is the natural logarithm of SCC in cells/mL and is
calculated as follows:
SCS=loge(SCC/100)-loge(2) + 3 (Shook, 1993)
Statistical Methods
Type III least squares analysis of variance (ANOVA) and
corrected means (least square means, LS Means) were generated using the
General Linear Models (GLM) Procedure of the Statistical Analysis
System (SAS; Helwig and Council, 1982) to evaluate the effects of herd,
season-year, cow, antibody response group, parity, week, and their
interaction terms on antibody response to OVA and E. coli, and
immunoglobulin concentration (Table 2). Tests of hypothesis of main
effects were tested against the MS for cow. Sources of variation that were
not significant were removed from the model in order to generate LS
Means. Data that did not show a normal distribution (E. coli antibody
response, serum IgG2 and whey IgG2) as indicated by the univariate
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procedure of SAS (SAS, 1982), were transformed to natural logarithms.
LS means were converted back to original units from loge transformed
data. Consequently, standard errors of means are not shown. The Proc
CORR procedure of SAS was used to generate Pearson product moment
correlation coefficients between immune response parameters. Results
were considered to be statistically significant if the p-value was <0.05
and trends were reported at the p-value <0.10.
Results
Serum Antibodi! to OVA
Cow, antibody response group, week, and the interaction
between antibody response group and week contributed to the variation
(P<0.0001) in serum antibody to OVA (Table 2). Herd did not
significantly contribute to the variation in serum antibody to OVA. As
expected, the rank of antibody response to OVA was Group 1>Group
2>Group 3 except at week -8 prior to immunization and significant
differences were noted between all groups at weeks -3, 0, 3, and 6.
Population LS means significantly (P<0.0001) increased from pre-
immunization (week -8) to week -3 (post primary immunization) in all
antibody response groups and OVA antibody response varied
significantly across time at weeks -3, 0, 3, and 6. (Fig. 3).
Heritability Estimates of Antibody to OVA
Heritability estimates (h2) of antibody to OVA at weeks -8,
-3, 0, 3, and 6 relative to calving were 0.64, 0.62, 0.32, 0.50, and 0.58
respectively. Standard errors could not be calculated by VCE software due
to the small sample size evaluated.
These heritability estimates can be used to obtain an
Estimated Breeding Value (EBV) of an animal in accordance with the
procedure described in PCT/CA93/00533 to Wilkie et al., filed December
9, 1992, entitled "Methodology For Developing A Superior line of
Domesticated Animals" (also see, Veterinary Genetics, F.W. Nicholas,
Oxford Science Publications, 1987; D.S. Falconer, An introduction to
quantitative genetics, Longman, London, 1981). EBV is an indicator of an
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animal's inherent ability to produce an immune response and its ability
to pass genes influencing these traits to offspring. For the purposes of the
present invention, EBV values are useful in selecting animals to be bred
in order to produce offspring which inherit the level of ability to produce
a high immune response when under stress. High immune response
may, in part, influence disease resistance.
When Antibodu to OVA
Herd contributed significantly (P<0.01) to variation in
antibody response to OVA and therefore, herds were further analyzed
separately. Cow, antibody response group, and week all significantly
contributed to the variation in antibody to OVA in whey (P<0.0001);
however, there was no significant contribution of the interaction term
antibody response group and week to the variation in response. For all
herds, antibody to OVA in whey by antibody response group, ranked
similarly to the antibody responses observed for serum, such that Group
1 >2>3. This was consistent for colostral and milk whey from parturition
until week 6 of lactation (Fig. 4A,B, and C). Least squares means of
antibody to OVA in whey for all herds declined significantly from
parturition to peak lactation. Correlation analysis between antibody to
OVA in sera with antibody to OVA in whey, indicated a positive and
significant relationship for Herd 1 (r=0.45; P<0.0001), Herd 2 (r=0.28;
P<0.001) and Herd 3 (r=0.45; P<0.001) respectively.
Antibody to E. coli J5 in sera
Herd contributed significantly (P<0.003) to variation in
antibody response to E. coli J5 and therefore, herds were further analyzed
separately (Table 2).
Herd 1
Cow, antibody response group, and week each
significantly (P<0.0001) contributed to the variation in antibody to E. coli
J5. Although antibody OD was not significantly different between
antibody response groups from week -3 to week 6, the rank of LS Means
of antibody response to E. coli by antibody response group was Group
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1>Group 2>Group 3 (Fig. 5A). Least squares means of antibody to E. coli
J5 varied during the peripartum period (week -3 to week +3) and up to
peak lactation (week +6) and were significantly higher (P<0.0001) than
pre-immunization antibody at week -8 for all animals, regardless of
group (OD value = 0.275) (Fig. 5A). Correlation analysis, comparing
antibody to E. coli J5 with antibody to OVA in sera, indicated a positive
and significant relationship (r=0.56; P<0.0001). The correlation between
serum anti-OVA and E. coli for Group 1, 2, and 3 was 0.66(P<0.001), 0.59
(P<0.0001) 0.38 (P<0.06), respectively.
Herd 2
Cow, antibody response group by parity, parity and week
significantly contributed to the variation in antibody response to E. coli J5
(P<0.0001) for Herd 2. Antibody for Group 3 animals at week -8 was
significantly higher (OD value=0.386) than for animals of Group 1 (OD
value=0.257; P<0.005) and Group 2 (OD value=0.292; P<0.05). Optical
density values of antibody to E. coli for animals in Groups 1 and 2 from
week -3 to week 6 was similar to OD values of serum antibody to OVA.
Optical density values of antibody were consistently positive following
the immunization but were not significantly higher than the population
mean. In contrast to serum antibody to OVA, Group 3 animals had OD
values that were consistent but not significantly lower than the
population mean. (Fig. 5B). Least square means of antibody response to
E. coli J5 at weeks -3,0,3, and 6 were significantly higher (P<0.0001) than
pre-immunization antibody at week -8 regardless of group (OD
value=0.307). Correlation analysis between serum antibody to E. coli J5
and serum antibody to OVA indicated a positive and significant
relationship (r=.49; P< 0.0001). The correlation between antibody to E.
coli J5 and antibody to OVA for Groups 1, 2, and 3 was 0.65(P<0.0001),
0.54 (P<0.0001), and 0.31 (P<0.08) respectively.
Herd 3
Cow grouped within antibody response group, antibody
response group, week, and the interaction between week and antibody
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response group significantly contributed to the variation in antibody to E.
coli J5 (P<0.0001) in Herd 3. In this herd, antibody for Group 1 animals
was significantly lower (P<_.05) at weeks -8 and -3 compared to Group 2
and 3 animals. At parturition, Group 1 and 2 animals had higher
antibody to E. coli than Group 3 animals. At weeks 3 and 6, however,
the rank of antibody response group for antibody to E. coli was similar to
the other herds, in that Group 1>Group 2>Group 3 (Fig. 5C). LS Means
of antibody to E. coli J5 at weeks -3,0,3, and 6 were significantly different
across time and were significantly higher (P<0.0001) than pre-
immunization antibody regardless of group (OD value=0.224)(Fig. 5C).
Correlation analysis between serum antibody to E. coli J5 and serum
antibody to OVA indicated a positive and significant relationship (0.47;
P< 0.0001). Correlation between serum antibody to E. coli J5 and
antibody to OVA for Groups 1, 2, and 3 were 0.93(P<0.007), 0.48 (P<0.0001),
and 0.36(P<0.006) respectively.
~1 in serum and when
Analysis of variance indicated that the effect of week
contributed significantly (P<0.05) and the effect of antibody response
group tended (P<0.07) to contribute to variation in serum IgG 1. Except at
week 3, serum IgGl did not differ significantly between groups; however,
Group 1 animals tended to have lower serum IgG1 compared to animals
of Group 2 and 3. Least square means of total IgGl in sera increased
significantly (P<0.0001) from parturition (430.09mg/100mL) to week 3
(687.46 mg/100mL) and week 6 (799.51 mg/100mL) (Fig. 6A, population
mean). Correlations between serum IgGl concentration and serum
antibody to OVA and E. coli were not significant.
The effects of week and parity contributed significantly
(P<0.05) to the variation in IgGl concentration in whey. Although
antibody response group did not significantly contribute to variation in
whey IgG 1 (Fig. 6B), LS means of IgGl concentration (mg / 100mL) at
week 0 were significantly lower for Group 1 (768.16 mg/100 mL) and
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Group 3 (1081.39 mg/100mL) compared to Group 2 (1381.60 mg/100mL).
Concentration of IgGl did not differ significantly between groups at
weeks 3 and 6. Population LS Means of IgG 1 concentration in whey
declined significantly from parturition (1046.28 mg/mL) to week 3 (44.93
mg/100mL, P<0.0001). There was no significant change at week 6 (43.25
mg/100mL). Correlation analysis between whey IgGl concentration and
whey antibody to OVA indicated a positive and significant relationship
(r= 0.711 ;P<0.0001). The correlation coefficients between whey IgGl and
whey OVA antibody response for Groups 1, 2, and 3 were 0.52(P<0.0001),
0.76(P<0.0001), and 0.69(P<0.0001), respectively.
~Z in sera
Herd contributed significantly (P<0.0001) to variation in
serum IgG2 concentration and therefore, herds were analyzed separately.
Herd 1
Effects of cow and the interaction between antibody
response group and week contributed significantly (P<_.05) to variation in
IgG2 concentration for Herd 1. Antibody response group did not
significantly contribute to the variation in IgG2 ; however, LS means of
IgG2 in sera at weeks 0 and 3 was lowest for Group 1 animals and highest
for Group 3 animals. This trend reversed at week 6, such that Group 1
animals had the highest concentration of IgG2 and Group 3 animals had
the lowest. LS Means of IgG2 significantly increased from 1019.43
mg/100mL at parturition to 1534.56 mg/100 mL at week 3 but declined
significantly at week 6 to 1103.23 mg/100 mL. Correlation analysis,
between antibody to OVA in sera and concentration of IgG2, indicated a
negative and significant relationship (r=-0.23, P<0.03). Correlations
between antibody to OVA with serum IgG2 concentration indicated for
Group 1, 2, and 3 were 0.07(ns), -0.35(P<0.004) and -0.33 (ns). Significant
correlations were not observed between E. coli antibody response and
serum IgG2 concentration, even when examined by group.
Herd 2
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Cow significantly contributed (P<_.05) to the variation of
serum IgG2 concentration while antibody response group and the
interaction between antibody response group and parity tended to
contribute to the variation in serum IgG2 concentration. At parturition,
groups did not significantly differ in serum IgG2. At week 6, LS means of
IgG2 concentration for animals in Group 1 were significantly higher than
for Group 3 animals. Least square means of IgG2 concentration did not
differ significantly between weeks 0, 3, and 6. Correlation analysis
between serum IgG2 concentration and serum antibody to OVA
indicated a positive and significant relationship (r=.15; P<0.03).
Significant correlations were not observed between serum IgG2
concentration and serum antibody to OVA or serum antibody to E. coli
J5.
Herd 3
Cow (P<0.005) and parity (P<0.04) contributed
significantly to the variation of serum IgG2. concentration. Week
(P<0.09) tended to contribute to variation in serum IgG2 concentration.
Antibody response group did not significantly contribute to variation in
serum IgG2 concentration. Correlations between serum IgG2
concentration and antibody to OVA and E. coli were not significant.
~~ in whey
Herd contributed significantly (P<0.03) to the variation in
serum IgG2 concentration and therefore, herds were analyzed separately.
Herd 1
Week contributed significantly to variation in IgG2
concentration in whey. Whey IgG2 concentration did not differ
significantly between groups (Fig. 8A). LS Means of total IgG2
concentration in whey declined significantly from week 0 (327.34 mg/100
mL) to week 3 (26.31 mg/100 mL). Correlation analysis between whey
IgG2 concentration and antibody to OVA indicated a positive and
significant relationship (r=0.7; P<0.0001). Correlations between whey
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IgG2 concentration and whey antibody to OVA were r=0.9 (P<0.002), 0.6
(P<0.0003), and 0.8 (P<0.02) for Groups 1, 2, and 3, respectively.
Herd 2
None of the parameters in the linear model contributed
significantly to variation in whey IgG2 concentration, and, therefore, LS
means were not estimable. Correlations between whey IgG2
concentration and whey antibody to OVA indicated a positive and
significant relationship (r=0.3, P<0.009). Correlations between whey IgG2
and whey antibody to OVA was 0.2(ns), 0.5(P<0.0001), and 0.6 (ns) for
Groups 1, 2, and 3, respectively.
Herd 3
Antibody response group and week significantly
contributed to the variation in whey IgG2 concentration. Whey IgG2
concentration did not significantly differ between groups at parturition,
and responses at week 3 could only be estimated for Group 3 animals
since responses for Groups 1 and 2 were either low or too low to be
detected. Correlation analysis indicated no significant relationships
between whey IgG2 concentration and whey antibody to OVA.
Mastitis Occurrence
Percent mastitis occurrence varied between groups and
between herds. Rates of occurrence of clinical mastitis are presented in
table 3. Mastitis did not occur in Group 1 of either Herds 1 or 3. Mastitis
occurrence in Herd 1 was 21.7% and 33.3 % for Groups 2 and 3,
respectively. Mastitis occurrence in Herd 3 was 11.5 and 10% for Groups 2
and 3 respectively. However, in Herd 2, Group 1 animals had the
highest occurrence of mastitis (15.4%) which exceeded the percent
occurrence of mastitis in Groups 2 (2.1%) and 3 (0 %) (Fig. 7). Animals
with mastitis in Herds 1 (n=6 heifers; n=26 cows) and 3 (n=8 heifers; n=29
cows) were in their second or greater parity. Animals with mastitis in
Herd 2 (n=34 heifers; n=33 cows) were all heifers. Across all herds,
animals in Group 3 had the highest rate of mastitis occurrence (13.6%)
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compared to Group 1 (11.1%) and Group 2 (9.3%) (Table 3). These
differences across herds however, were not significant.
Odds-Ratio ~'or Mastitis
Within herd, odds-ratio calculations comparing animals
of one antibody response group with the other two groups indicated that
only animals in Group 1 of Herd 2 had a statistically significant higher
relative risk of having a mastitis event (by 7.57 times) compared to the
animals in the rest of the herd. Although the risk of mastitis occurrence
within Group 3 of Herds 1 and 3 was 2.16 and 1.8 times greater
(respectively) than for other groups, these values were not significant.
Somatic Cell Score
For Herds 1 and 2, cow, week and antibody response
group significantly contributed to the variation in SCS (Table 2). In Herd
3, only the effect of cow within antibody response group accounted for
the variation in SCS. Somatic cell score was significantly different
between groups in Herds 1 and 2 but not Herd 3. LS Means of SCS in
Herd 1 were lowest for the high antibody responder animals, and greatest
for the low antibody responder animals at weeks 3,4,5 and 6 following
parturition (Fig. 8A). Conversely, LS Means of SCS in Herd 2 were
significantly lower for low antibody responder animals compared to high
antibody responder animals (Fig. 8B).
Discussion
Example 1 indicated that animals could be classified
according to the amplitude and direction of their individual OVA
antibody response profiles, and that this ranking had some association
with mastitis occurrence. This herd, Herd 1 used in Example 1, was
evaluated with two more herds, Herds 2 and 3. The objective of the
current study was to verify the relevance of high and low antibody
response profiles across the three herds and to determine if it would be
possible to develop a quantitative measure of classification for antibody
response that reflected the initial qualitative assessment of animals. The
results indicated substantial variation in antibody response to OVA from
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the peripartum period to peak lactation and that animals could be ranked
using a quantitative index. Animals that ranked high, average or low for
serum antibody response, also ranked similarly for whey antibody to
OVA. Serum antibody to OVA was expected to be significantly different
between groups since animals were purposefully classified into high or
low groups based on their total antibody response curve slopes as either
less or greater than one standard deviation from the population mean of
the total index of antibody response, but antibody in whey was not
classified in this manner. Whey antibody responses within each herd
demonstrated that high and low serum OVA antibody responses were
also high and low in whey, respectively.
In all herds, antibody to the more biologically relevant
antigen, E. coli, ranked similarly to the ranking for antibody to OVA,
particularly at weeks 0, 3, and 6 after parturition. This can help identify
animals which respond best following immunization. Nonetheless,
ranking based on response to OVA may be preferable since OVA is not
normally encountered in the dairy cow's environment thus eliminating
the possibility of pre-existing antibody to OVA. In theory, any antibody to
OVA responses are expected to be evoked only by the immunization
protocol. Further, antibody to E. coli was significantly affected by herd
making comparisons of populations difficult.
Previous mathematical approaches to assess variation in
innate and immune host resistance mechanisms during the peripartum
period that have included work by Detilleux et al. (1994) who used a fitted
polynomial model to assess hyporesponsiveness during the peripartum
period. These results were utilized in an animal model to detect
variation between daughters of various sire groups. This method of
assessment of hyporesponsiveness was not suitable for this study since it
requires many data points across time. Variation in antibody to OVA
was partitioned using a simple model wherein animals that had any
hyporesponsiveness in the immediate peripartum period were ranked
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lower compared to animals that responded consistently and positively to
OVA immunization.
Antibody response to ovalbumin OVA in dairy calves has
been reported by Burton et al. (1989) to be heritable (h2 = 0.48). Though
standard errors of sire and error variances could not be calculated,
heritability estimates, of serum antibody to OVA were high (h2 > 0.50) at
time points before and after calving. That the heritability estimate at
parturition (h2 = 0.32) was lower than at other time points evaluated,
may be explained by the complex interactions that occur between
hormones and the immune system during the immediate post-partum
period. Taken together, these results indicate a possible significant
genetic component to bovine antibody to OVA, although heritability may
be lower at times when the dairy cow experiences the physical and
metabolic stresses of parturition and early lactation. These estimates will
need to be confirmed on larger populations but suggest that genetic
selection for increasing antibody responsiveness is possible, if deemed
significant, in the peripartum cow.
Correlation analysis between antibody to OVA in serum
or whey and IgGl&2 by antibody response group indicated some
significant relationships. However, antibody response group in the
statistical model did not significantly, but tended to contribute to the
variation in serum or whey IgGl&2. Serum and whey IgG2 distributions
by antibody response group differed for each herd and consequently,
significant relationships between groups that are common to all herds
were difficult to determine. Unpublished data from this laboratory and
other studies have indicated that the serum antibody to OVA is largely of
the IgG2 subclass (Gilbert et al., 1994) and therefore, may have indicated
some association between the two parameters investigated. However,
since herd differences existed, it was not feasible to relate previously
published results with IgG2 concentration investigated in the current
study.
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The incidence of mastitis by antibody response group was
not consistent between herds. In Herd 1 and Herd 3, the incidence of
mastitis was greatest for animals with low antibody response (Group 3).
All animals within these herds that had mastitis were in their second or
later parity. Though not significant, odds-ratio assessment for these
herds indicated that there was a 2.16 and a 1.80 times greater chance of
having a mastitis event if animals were classified in Group 3 versus
Groups 1 & 2. In contrast, animals from Herd 2 had a very different
distribution of mastitis occurrence among groups. Animals in Group 1
had the greatest rate of mastitis occurrence and according to the odds-
ratio parameter, were 7.57 (P<0.05) times more likely to have a mastitis
event. Further, all animals that had mastitis within Herd 2 were first
parity heifers. Differences in herd management and the distribution of
heifers and cows within each herd and antibody response group, may
help explain the differences in the distribution of mastitis occurrence.
Herd 1 (n=6 heifers; n=26 cows) and Herd 3 (n=8 heifers; n=29 cows) had a
greater ratio of cows to heifers within each antibody response group,
while Herd 2 (n=34 heifers; n=33 cows) heifers and cows were more
evenly distributed among all antibody response groups. Previous studies
have acknowledged an increase in the rate of occurrence of mastitis with
advancing parity (Todhunter et al., 1995, and McClure et al., 1994) which
may explain the disparity among herds. The unexpected distribution of
mastitis in the Herd 2 might further be explained by a more recent
investigation from Finland (Myllys et al., 1995) which indicated that in
well managed herds with high milk production and low somatic cell
counts, the rate of the treatment of heifers that had a mastitis episode
increased from 1.8% to 4.4% over an 8 year period. In contrast to clinical
mastitis observed in second parity and multiparous cows, that study
further indicated that mastitis in heifers only resulted in small
production losses, did not pre-dispose heifers to more mastitis or other
diseases later in lactation, and the recovery rate from mastitis was high as
indicated by a rapid decline in somatic cell counts (SCC) following
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infection. This may indicate that mastitis in heifers and in cows cannot
be compared directly. That disease occurrence in this study was not
consistent among herds, may be explained by a number of factors
including the relatively small sample size evaluated, environmental
(management) differences, distribution of heifers and multiparous cows,
and type of mastitis (subclinical vs. clinical, and the infecting pathogen).
Alterations in antibody response and the incidence of
mastitis indicates that immune response phenotype can be a potential
phenotypic marker for disease resistance and/or susceptibility.
It was determined that sufficient individual variation in
antibody response to OVA existed such that animals could be readily
classified quantitatively into high, average and low response groups
using a mathematical index based on OD values of antibody response
profiles from week -3 to week 6 relative to parturition. Detection of
immune response traits such as antibody response, which associate well
with disease resistance can provide a useful phenotype to begin selective
breeding of dairy cattle for improved inherent immune responsiveness
and disease resistance.
EXAMPLE 3
Relationships Between Cell Mediated Immune Response (CMIR) and
Antibody Response in Periparturient Holstein Dairy Cows
To examine variation in cell mediated immune response
(CMI) response as a function of peripartum serum antibody response to
ovalbumin (OVA), 136 Holstein cows and heifers from three herds were
evaluated from three weeks before parturition to week 6 following
parturition for lymphocyte proliferative responses to OVA and
concanavalin A (Con A), delayed type hypersensitivity (DTH) to purified
protein derivative (PPD) of tuberculin, differential complete blood cell
counts, and somatic cell score (SCS). Using a mathematical index,
animals were quantitatively classified based on their antibody responses
to OVA into high (Group 1), average (Group 2) or low (Group 3)
antibody response phenotypes. Lymphocyte proliferative responses to
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OVA (r=-0.28; P<0.0001) and Con A (r=-0.14; P<0.0001) were negatively
correlated with antibody to OVA. Animals classified as low antibody
response (Group 3) had the highest unstimulated and OVA-stimulated
lymphocyte proliferative responses. Proliferation of unstimulated
lymphocyte proliferative responses was depressed between week -3 and
parturition. Con A stimulated lymphocyte proliferative responses were
also depressed at parturition but this was significant (P<_0.05) only in
Group 1 which had high antibody response to OVA. Although animals
exhibiting high and low DTH response phenotypes could be identified,
DTH was not significantly associated to anti-OVA response. Delayed
type hypersensitivity at 48 and 72 hours were negatively and significantly
correlated with unstimulated (r=-0.21; P<0.002; r=-.17; P<0.01) and Con A
stimulated (r=-0.29, P<0.0001; r=-0.28, P<0.0001) lymphocyte proliferation,
respectively. Lymphocyte number in peripheral blood declined
significantly from week -3 to week 0. Milk somatic cell score (SCS) was
negatively, and significantly, correlated with in vitro lymphocyte
proliferative response to OVA in Herd 2 (r=.-13; P<0.0001) only. SCS was
not significantly correlated with Con A stimulation. SCS was also
negatively and significantly correlated with DTH at 48 hours post-
challenge (r=-0.21; P<0.01). Cumulative results indicate a variety of
negative phenotypic associations between measures of antibody response
and CMI, and among indicators of CMI. Since both antibody and CMI are
important in host resistance to infectious disease, use of a selection index
would be required to simultaneously enhance both parameters,
assuming there are beneficial associations with cow health.
Introduction
Innate and immune response mechanisms of dairy cows
are impaired during the peripartum period. Neutrophil function
(Detilleux et al., 1995, Kehrli et al., 1989b, Gilbert et al., 1994 and Cai et
al.,
1988), complement activity (Detilleux et al., 1995), conglutinin
concentration (Detilleux et al., 1995), IgGl (Detilleux et al., 1995), milk
somatic cell count (Shuster et al., 1996) and lymphocyte proliferation
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(Saad et al., 1989; Kehrli et al., 1989a; Ishikawa, 1987; Kashiwazaki, 1985;
Wells et a1.1977) are impaired either pre- or postpartum. Some
investigations however, indicated that not all animals exhibit a period of
hyporesponsiveness, at least with respect to antibody response. Mallard
et al. (1997; Ch. V) demonstrated that peripartum antibody responses to
ovalbumin (OVA) are continuous in nature, and that this variability
allowed animals to be readily classified as low, average or high antibody
producers. Further, animals of the high group had lower mastitis
occurrence than animals with average and low antibody response
(Mallard et al., 1997; Ch. II & V).
Given that both antibody and cell mediated immune
mechanisms are involved in response to infectious disease, it is relevant
to evaluate the relationships between antibody and indicators of CMI.
Since negative associations have been reported between antibody and
aspects of CMI, animals categorized on the basis of antibody response to
OVA may have the inverse rank for CMI responses (Biozzi et al., 1972;
Arthur and Mason, 1986). This would have practical implications if
antibody response was proposed as a candidate marker of disease
resistance of dairy cattle. The objectives of this paper were to evaluate
CMI responses with respect to antibody response group and to determine
if any associations exist with SCS as an indicator of udder health.
Materials and Methods
Exj~erimental Design
To evaluate phenotypic variation in CMIR of dairy cattle,
136 Holstein animals from two research herds Herds 1 and 2,
respectively) and one commercial herd (Herd 3) were examined every
three weeks from week -3 to six weeks postpartum (week 6). Eighty-eight
animals were multiparous cows and 48 were primiparous heifers. To
stimulate immune response during the peripartum period, animals
received an intramuscular (im) injection of ovalbumin (OVA, Type VII,
Sigma Chemical Co., St. Louis, MO) and with a mastitis endotoxemia
preventive vaccine, an Rc mutant of Escherichia coli 0111:B4 (Rhone
CA 02255423 1998-12-10
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Merieux Escherichia coli J5, Rhone Merieux, Lenexa, KS) approximately
eight weeks (4 mg OVA) and three weeks (2 mg OVA) prior to predicted
parturition dates. At parturition (week 0), animals received a single im
immunization injection of OVA (2 mg) dissolved in phosphate buffered
saline (PBS - 0.1 M, pH 7.4). Using a mathematical index, animals were
classified based on serum antibody to OVA into high (Group 1), average
(Group 2) or low (Group 3) response groups (Ch.II). At weeks -3, 0, 3, and
6, PBMC were stimulated in vitro with OVA (5 mg/mL) and
concanavalin A (Con A) (5 mg/mL), and proliferative response was
measured as described below (section 2.4). For lymphocyte proliferative
response, week-3 responses of animals that calved early or later than
predicted parturition dates were adjusted to reflect the true time point
evaluated (i.e. week -2 or week -4). In order to evaluate delayed type
hypersensitivity (DTH) as a measure of CMIR, a subset (n=36; 15 cows and
21 heifers) of animals from Herd 2 were given a l.5mg/mL intradermal
injection of the Bacillus Calmette Guerin (BCG; Connaught, Mississauga,
Ont.) vaccine in the left caudal tail fold at week 1 postpartum.
Delaued Tu~pe H~jnersensitivitu
Animals vaccinated with BCG (l.5mg/mL) received a 0.1
mL intradermal injection of the PPD of tuberculin (250 US Tuberculin
Units; Connaught, Mississauga, Ont.) and for control, received 0.1 mL
injection of PBS at week 3 int the right caudal tail fold. The PPD was
injected in a designated site approximately 4 cm from the PBS designated
site and both were located 10 cm from the base of the tail. Prior to
injection, sites were encircled with a coloured marker and double skin
thickness measurement was taken in triplicate (time=0), using
Harpenden skin thickness calipers (John Bull, England, UK). Forty eight
and 72 hours after intradermal injection of PPD and PBS, double skin
thickness was measured again. Skin thickness increase at 48 and 72
hours was calculated as follows:
increase in skin thickness =(((A-B)/B)-(C-D)/D)))X100
where A=mean test thickness (at time=48, 72 hours)
CA 02255423 1998-12-10
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B=mean of pre-test thickness (at time=0 hours)
C=mean of control thickness (at time=48, 72 hours)
D=mean of pre-control thickness (at time=0 hours)
Prior to conducting these experiments it was confirmed that the herd was
tuberculin test negative on the basis of negative results in 10 randomly
selected animals.
L~m~bhocute Proliferative Res~ onse
Lymphocyte proliferation assays were performed
according to the procedure of Chang et al. (1993). Briefly, blood was
centrifuged (850 x g, 15 min) and whole blood buffy coats were diluted in
phosphate buffered saline (PBS 0.1M, pH 7.4). Peripheral blood
mononuclear cells (PBMCs) were separated from diluted whole blood
buffy coats by density gradient centrifugation (1000 x g, 30 min) using
aqueous Histopaque 1.077 (Sigma Chemical Co. St. Louis, MO.) Cell
pellets were washed by centrifugation in PBS (400 x g, 7 min) and
suspended in culture medium (Rosewell Park Memorial Institute; RPMI-
1640, and 100 LU. penicillin-streptomycin, prepared by Central Media
Laboratory; Ontario Veterinary College, University of Guelph, Guelph,
Ont.) and 10% FCS and brought to a final concentration of 2.0 x 10 6
cells/mL. To determine specific clonal proliferative responses to
antigen, a stock solution (50 ~g/mL) of OVA (Sigma Chemical Co., St.
Louis, MO) dissolved in RPMI - 1640 was prepared and stored in small
aliquots at -70°C. Five ~,g/mL of OVA was added to each of 6 replicates
of
test PBMC in 96 well flat-bottom plates (Nunc, Fisher Scientific, Don
Mills, Ont.). Medium only was added to 6 well replicates of PBMC as
non-stimulated controls, to obtain background values for unstimulated
cell proliferation. The mitogen, concanavalin A (Con A; Sigma
Chemical Co., St. Louis, MO) prepared from stock solution (50 ~g/mL)
and diluted to (5 ~g/mL) for addition was added to 6 replicates of cells on
a plate with 6 non-stimulated control replicates. Following 24 h of
incubation with OVA or Con A(37°C, 6% COZ) cells were incubated for 18
h with 0.5 ~Ci methyl tritiated thymidine per well (ICN Biochemical,
CA 02255423 1998-12-10
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Canada Ltd. Montreal, Que.). Plates were frozen until cells were
harvested using a plate harvesting system (LKB Wallac, Turku, Finland)
onto fiberglass filter mats (LKB Wallac, Turku, Finland). Radioactivity
was recorded as counts per minute (cpm) by a beta plate liquid
scintillation counter (LKB Wallac,Turku, Finland).
Flow Cutometric Assau ,for the Detection off' CD Surface Molecules of
Peri~nheral Blood Lumbhoca
Cell phenotypes were characterized after stimulation with
either Con A or OVA, by staining with monoclonal antibodies
recognizing five cell surface markers as described by Van Kampen and
Mallard (1997). The monoclonal antibodies were kindly provided by Dr.
Jan Naessens of ILRI (ILRI, Nairobi, Kenya) and included antibodies to
the following bovine cell surface markers: CD2+ (IL-A43), CD4+ (IL-A11),
CD8+ (IL-A105), WCI (IL-A29), and IgM (IL-A30). Peripheral blood
lymphocytes from a subset of animals (n=10) from Herd 2 (n=7) and Herd
3 (n=3) were evaluated for these lymphocyte cell surface markers at
weeks -3, 0, 3, and 6 relative to parturition. Lymphocytes were prepared
and cultured as previously described for lymphocyte proliferation assays,
however, each 96 well plate was divided into quadrants each with 24
wells. Twenty four replicates each of Con A stimulated (5 ~g/mL), OVA
stimulated (at 5~.g/mL and 20~.g/mL) and non-stimulated controls were
cultured for 42 hours (the same total duration used in the lymphocyte
proliferation assays). After 42 hours, cells were harvested by pipette,
washed with PBS and transferred to 10 mL glass test tubes. Cells were
centrifuged (400 x g,10 min), and supernatants decanted and cells were
resuspended in 250 ~,L PBS + 0.1M sodium azide (Fisher Scientific,
Fairlawn, NJ). Immunostaining was performed in 96-well round-bottom
plates (Corning, New York, NY). Fifty ~L of cells and 50 ~,L of diluted
primary antibody were added to each well and plates were incubated (20
min, rt). After incubation, 100 ~L of PBS 0.1M Azide was added to each
well to wash the cells. Cells were suspended by mixing on a shaker and
centrifuged (400 x g, 6 min). Supernatants were then removed using an
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aspirator. This washing procedure was performed twice. Fifty ~,L of
FITC-conjugated goat anti-mouse IgG(H+L) (Cedarlane Laboratories,
Hornby, Ont.) was then added to the cells and cells were incubated (20
min, rt). After incubation, plates were washed twice as described above.
Cells were fixed in 1% paraformaldehyde and transferred into 3 mL
polystyrene tubes (Becton Dickinson, Lincoln Park, NJ) containing 300 ~L
of 1% paraformaldehyde. Tubes were covered with Parafilm and
refrigerated (4°C) ~g/mL until time of assay.
A FACS Scan flow cytometer (Becton Dickinson, Lincoln
Park, NJ) was used to acquire lymphocyte subset data. LYSIS II software
(Becton Dickinson, Lincoln Park, NJ) was used for analyzing data
describing the frequency of positively stained cells. Lymphocytes were
gated out from other populations based on their forward and side scatter
characteristics. Histograms representing fluorescence of cells expressing
CD2 (pan T cell), CD4 (helper T cells), CD8 (cytotoxic/suppressor T cells),
WC1 (y8 T cells), and IgM (B cells) cell surface markers were plotted for
each cow, timepoint, and culture condition observed. The region of
background fluorescence was established with the negative control
marker, M1. Events accumulated to the right of this marker were
considered positive. (Appendix III, Fig. 2).
Com~nlete Blood Cell Counts
Complete Blood Cell Counts were determined by the
Clinical Pathology Laboratory at the Ontario Veterinary College,
University of Guelph, Guelph, Ontario, Canada. Counts included the
percent and total number of leukocytes, erythrocytes, banded neutrophils,
segmented neutrophils, lymphocytes, monocytes, basophils, and
eosinophils.
Milk Somatic Cell Counts
Weekly milk somatic cell counts (SCC), an indicator of
subclinical mammary gland infection, were obtained from animals of
Herd 1 using the weekly sampling service offered by the Ontario Dairy
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Herd Improvement Corporation (Ontario DHI). Weekly samples of
animals in Herd 2 and Herd 3 sampled 1-4 hours after morning milking
were tested for SCC by the Mastitis Laboratory at the Ontario Veterinary
College, University of Guelph, Guelph, Ontario Canada. Monthly SCC
were obtained from Ontario DHI for all three herds. Somat cell cnunt~
were transformed to somatic cell score (SCS) for analysis. Somatic cell
score is the log-linear transformation of SCC in cells/mL and is calculated
as follows:
SCS=loge(SCC/100)=loge(2) + 3 (Shook, 1993)
Statistical Methods
Type III least squares analysis of variance (ANOVA) and
corrected means (least square means, LS Means) were generated using the
General Linear Models (GLM) Procedure of the Statistical Analysis
System (SAS; Helwig and Council, 1982) to evaluate the effects of herd,
season-year, cow, antibody response group, parity, week, and their
interactions on lymphocyte proliferation to OVA and Con A, DTH,
complete blood cell counts and SCS (Table 4). Sources of variation were
tested against the mean square (MS) for cow grouped within antibody
response group and parity to determine significance in the GLM.
Sources of variation that were not significant were removed from the
model in order to generate LS Means. Unstimulated lymphocyte
proliferation was used as a covariate in the GLM for OVA and Con A
stimulated lymphocyte proliferation since some variability in
unstimulated responses between dairy animals has been described
(Burton et al., 1991). Data that did not show a normal distribution
(unstimulated lymphocyte proliferation, OVA and Con A stimulated
lymphocyte proliferation, and total neutrophils) as indicated by the
univariate procedure of SAS (Helwig and Council, 1982), were
transformed to natural logarithms. Lymphocyte count data was
transformed using a square root transformation. Least square means
were converted back to original units from loge, or square root
transformed data. Consequently, standard errors of means are not
CA 02255423 1998-12-10
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shown. The Proc CORR procedure of SAS was used to generate Pearson
product moment correlation coefficients. Results were considered to be
statistically significant if the P-value was <0.05 and trends were reported
at the p-value <0.10.
Results
Unstimulated in vitro lumjhhoc~i~te ~nroliferation
Individual cow, week relative to parturition, the
interaction between antibody response group and week contributed
significantly to variation in unstimulated lymphocyte proliferation
(Table 4). Herd did not significantly affect the variation in unstimulated
lymphocyte proliferative response. Unstimulated lymphocyte
proliferative response significantly (P<0.05) declined at parturition, but
increased again at week 3 of lactation. When these responses were
evaluated by antibody response group, at weeks -3, 0, 3, and 6, lymphocyte
proliferative responses were significantly lower (P<0.01) for animals of
the high antibody response group and significantly higher (P<0.05) for
animals of the low antibody response group (Fig. 9A). The correlation
between antibody to OVA and unstimulated lymphocyte proliferation
across all groups was negative and significant (r=-0.26, P<0.0001; Table 5).
OVA stimulated lumrohocute~nroli eration
Individual cow and the interaction between antibody
response group and parity, replicate and unstimulated lymphocyte
proliferation, significantly contributed to variation in OVA lymphocyte
proliferative response (Table 4). Herd did not significantly contribute to
the variation in lymphocyte proliferative responses to OVA. Least
square means of lymphocyte proliferation did not differ significantly
across weeks. At weeks 0 and 3, lymphocyte proliferation to OVA was
significantly lower for Group 1 (P<0.01) compared to Group 3. At week 6,
the response of these groups was reversed. The correlation between
antibody to OVA across all groups and OVA stimulated lymphocyte
proliferation across all groups was negative and significant (r=-0.27,
P<0.0001).
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Con A Stimulated Lump hocute Proli eration
Individual cow, parity, the interaction between parity and
antibody response group, antibody response group, week and the
interaction between week and antibody response group significantly
contributed to variation in Con A stimulated lymphocyte proliferation.
Herd did not significantly contribute to variation in lymphocyte
proliferation to Con A. Least square means of Con A stimulated
lymphocyte proliferation declined, though not significantly, from week
4 and -3 to parturition (Fig. 9C). Proliferative responses increased
significantly (P<0.05) at week 3 compared to parturition. Group 1
animals had the highest Con A-induced lymphocyte proliferation at
weeks -4, -3, 0, 3, and 6. Response decreased in Group 1 (high response)
animals from week -4 to parturition and significantly increased from
parturition to week 3. Antibody response to OVA and Con A-stimulated
lymphocyte proliferation was negatively correlated (r=-0.14, P<0.0001).
Delaued Turoe Hubersensitivitu fDTH)
Antibody response category did not significantly affect
variation in DTH response. Cutaneous DTH responses at 48 and 72
hours were highly correlated (r=0.90; P<0.0001). At 48 hours DTH ranged
from 0 to 75% skin thickness increase with a mean of 30.7% while 72
hour values ranged from 0 to 79% with a mean of 29.5%. Antibody
response to OVA at week 3 did not correlate significantly with DTH
responses. The DTH response at 48 and 72 hours was negatively and
significantly correlated with unstimulated (r=-.21; P<0.002; r=-.17; P<0.01 )
and Con A (r=-0.29; P<0.0001; r=-.28; P<0.0002) stimulated lymphocyte
proliferative responses, respectively. DTH response at 48 hours was
significantly and negatively (r=-0.21; P<0.01) correlated with SCS.
Differential Comjnlete Blood Cell Counts
Counts of segmented neutrophils varied between
animals within all herds, but were not significantly affected by week or
antibody response group. Since banded neutrophils were not observed in
every animal, a general linear model could not be used to explain the
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variation in this response. Counts of lymphocytes declined significantly
(P<0.05) from week -3 (4.8 X 109 cells /mL) to week 0 (3.9 X 10 9 cells/mL)
(Fig. 11). Significant differences in lymphocyte numbers between
antibody response groups were observed only at weeks 3 and 6 of
lactation when Group 3 animals had significantly (P<0.05) more
lymphocytes compared to Groups 1 and 2. Across time, only Group 3
animals had a significant decline (P<0.05) in percent and total numbers of
lymphocytes from week -3 to parturition.
Milk Somatic Cell Score
For Herds 1 and 2, individual cow, week relative to
parturition and antibody response group contributed significantly to
variation in response. In Herd 3, only the effect of cow accounted for the
variation in response. Least square means of SCS in Herd 1 were lowest
for animals of the high antibody response group, and greatest in animals
of the low antibody response group at weeks 3,4,5 and 6 following
parturition. Conversely, LS Means of SCS in Herd 2 were significantly
lower for animals of the low antibody response group compared to
animals of the high antibody response group. Somatic cell score was
negatively and significantly correlated with OVA stimulated lymphocyte
proliferative responses in Herd 2 (r=.-13; P<0.0001). Delayed type
hypersensitivity at 48 hours was negatively and significantly correlated
with SCS (r=-0.21; P<0.01).
Lum~nhocute Subsets After Culture
Although lymphocyte subset proportions varied
depending on week relative to parturition (week -3 to week +6), the
percentage of cells positively expressing CD2+, CD4+, CD8+, WC1+, and
IgM were not significantly different between unstimulated control and
treatment groups. Cells expressing IgM were most frequent (38-60%)
regardless of treatment and WC1+ cells were least numerous (5-15%) at
all time points. Only at week 3 relative to parturition were there more
Con A-stimulated lymphocytes expressing IgM (60%) compared to
unstimulated controls (40%) or OVA stimulated PBMC (38-40%).
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Discussion
The previous example indicates that in the peripartum
period, Holstein animals varied in antibody to OVA and that animals
could be grouped into high, average and low groups based on this
response. The objectives of this study were to evaluate CMI responses
with respect to antibody response group and to evaluate possible
associations with SCS as an indicator of udder health.
In the current study, animals with the highest antibody
response (Group 1) had significantly (P<_0.05) lower unstimulated and
OVA-stimulated lymphocyte proliferative responses during the
peripartum period while low antibody response animals (Group 3) had
the highest lymphocyte proliferative responses. Con A-induced
lymphocyte proliferation and antibody response to OVA however, were
not inversely related, since animals with high antibody response also
had high Con-A stimulated lymphocyte proliferative responses,
indicating that relationships between antibody and CMI may vary
depending on the measurements made. Although no differences in
DTH were observed between antibody response groups, DTH responses
were demonstrated to vary between individuals. This variation in DTH,
a measure of CMI, indicates that it may be possible to select animals for
enhanced CMI.
Lymphocyte counts declined in agreement with Saad et
al.(1989). Group 3 animals, which that had higher unstimulated and
OVA-stimulated in vitro lymphocyte proliferation, had the sharpest
decline in lymphocyte numbers at parturition. This may indicate that,
although absolute numbers were decreased, lymphocyte function may
have been better in that particular group of animals. Neutrophil counts
have been reported to decline from week -3 to parturition (Detilleux et
al., 1995), however, no significant changes in neutrophil numbers were
observed in the current study.
Previous evaluation of SCS indicated that SCS in Herd 1
was lowest for animals of the high antibody response group, and greatest
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in animals of the low antibody response group at weeks 3,4,5 and 6
following parturition (Mallard et al., 1997; Ch. I). Conversely, SCS in
Herd 2 was significantly lower for animals of the low antibody response
group compared to animals of the high antibody response group (Ch. II).
In the current study, SCS was negatively and significantly correlated with
OVA stimulated lymphocyte proliferative responses and DTH response,
indicating that sustained selection for low SCS could compromise aspects
of CMI.
Depression of lymphocyte proliferation during the
postpartum period has been demonstrated previously in humans
(Weinberg, 1984), sheep (Burrels et al., 1978), and dairy cattle (Wells et
al.,
1977; Manak et al., 1982; Kashiwazaki et al., 1985; Ishikawa, 1987; and
Kehrli et al., 1989a). Ishikawa (1987) demonstrated decreased blastogenic
response in PBMC stimulated with Con A and pokeweed mitogen
(PWM) from the third trimester of pregnancy, which reached a
minimum at parturition. Saad et al. (1989) described a depressed Con A-,
phytohemagglutinin (PHA)-, and PWM-stimulated lymphocyte
proliferation that started only 1 week prior to parturition and was
minimal one day before parturition. Saad et al. (1989) also evaluated
milk mononuclear cell (MC) proliferative responses and, in contrast to
PBMC, milk MC did not increase in proliferative response two weeks
after lactation. Peripartum depression of lymphocyte proliferation was
observed in unstimulated lymphocyte proliferative responses between
weeks -3 and parturition (week 0), and a depression of response to Con A
was also observed. The largest (P<0.05) depression of Con A stimulated
lymphocyte proliferative responses at parturition were observed in
animals with a high antibody response phenotype (Group 1). Again, this
may indicate a negative association between high antibody response and
certain indicators of CMI in dairy animals, which would need to be
considered in the development of a selection index for high and low
Immune response .
EXAMPLE 4
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The Relationship Between Milk Production and Antibody Response to
Ovalbumin (OVA) During the Peripartum Period
Suboptimal innate and immune mechanisms of host
resistance during the peripartum period may contribute to increased
incidence of mastitis. To evaluate associations between antibody
response to OVA and milk production variables during the peripartum
period, 136 Holstein cows and heifers from 3 herds with known antibody
response profiles, were evaluated for projected 305-day milk, protein, and
fat yield. Using a mathematical index, animals were quantitatively
classified based on their antibody responses to OVA into high (Group 1),
average (Group 2) or low (Group 3) response groups. Group 3 had the
highest (P<0.0001) milk yield (8448.6 kg) compared to Groups 1 (8191.2 kg)
and 2 (8174.8 kg). Group 3 had the highest 305-day predicted protein
(279.8 kg) and fat yield (343.1 kg) compared to Groups 1 (263.5 kg, 314.0 kg)
and 2 (261.4 kg, 314.9 kg) respectively. However, in two out of the three
herds investigated, Group 1 animals had no incidence of clinical mastitis
compared to other antibody response groups. Although this suggests
that animals with low antibody response produce more milk, fat and
protein, and therefore more income, mastitis occurrence was observed to
be highest for these animals in two out of three herds investigated. The
development of animals that produce optimal levels of milk with
reduced occurrence of mastitis may be possible through selective
breeding for both production and enhanced immune response.
The objective of this example was to evaluate the effect of
antibody response group on 305-day projected production traits (milk, fat,
and protein) and relate production and immune response associations
with disease occurrence.
Materials and Methods
Animals and Treatments
Phenotypic variation in immune responses of 136
Holstein cows and heifers from 2 research herds (n=32; n=67) and 1
commercial herd (n=37) were examined from week -3 relative to calving
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(week 0) to six weeks postpartum (week 6). Eighty-eight animals were
multiparous cows and 48 were primiparous heifers. As described
previously (Mallard et al., 1997; Ch. V), to stimulate antibody response
during the peripartum period, animals received an intramuscular (im)
injection of ovalbumin antigen (OVA , Type VII, Sigma Chemical Co., St.
Louis, MO) and a mastitis endotoxemia preventive vaccine, an Rc
mutant of Escherichia coli 0111:B4 (Rhone Merieux Escherichia coli J5,
Rhone Merieux, Lenexa, KS) approximately 8 weeks (4 mg OVA) and 3
weeks (2 mg OVA) prior to predicted calving dates. At parturition (week
0), animals received a single immunization of the OVA dissolved in
phosphate buffered saline (PBS - 0.1 M, pH 7.4) (2 mg, im). Using a
mathematical model described previously (Ch. II), animals were
categorized based on their antibody response to OVA and grouped into
high (Group 1), average (Group 2) and low (Group 3) antibody response
phenotypes.
Production Variables
Projected 305 day milk, fat, and protein yields were
obtained from the Ontario Dairy Herd Improvement Corporation
(Ontario DHI). The last test day before the end of lactation was used to
calculate projected 305-day milk, fat and protein and was based on at least
100 days in milk (DIM).
Statistical Methods
Type III least squares analysis of variance (ANOVA) and
corrected means (least square means, LS Means) were generated using the
General Linear Models (GLM) Procedure of the Statistical Analysis
System (SAS; Helwig and Council, 1982) to evaluate the effects of herd,
season-year, antibody response group, parity, week, and their
interactions milk, fat, and protein yield (Table 6). Results were
considered to be statistically significant if the p-value was <0.05 and
trends were reported at the p-value <0.10.
Results
Ef ects of antibod~res~ oa nse grown on milk broduction variables
CA 02255423 1998-12-10
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Milk of i~ eld
Parity and the interaction between antibody response
group and parity contributed significantly (P<_.0001) and antibody
response group tended (P<_0.06) tended to contribute to variation in
projected 305-day milk yield (Table 6). Group 3 animals had a
significantly higher (P<0.0001) 305-day cumulative milk yield (8448.6 kg)
compared to average (8174.8 kg) and high (8191.2 kg) antibody responding
dairy animals (Fig. 12A).
Protein
Antibody response group, parity and the interaction
between antibody response group and parity contributed significantly
(P<0.0001) to variation in protein yield (Table 6). Group 3 animals had a
significantly higher (P<0.0001) 305-day protein yield (279.8 kg) compared
to average (261.3 kg) and high (263.5 kg) antibody responder animals (Fig.
12B).
Fat
Antibody response group, parity, and the interaction
between antibody response group and parity significantly (P<_.0001)
contributed to variation in 305-day fat yield (Table 6). Group 3 animals
had a significantly higher (P<0.0001) 305-day cumulative fat yield (343.1
kg) compared to average (314.9 kg) and high (314.0 kg) antibody
responding animals (Fig. 12C).
Discussion
The current study suggests that animals with the highest
antibody response have lower milk, fat and protein yield. However,
animals that have high antibody response in two out of the three herds
evaluated were reported (Ch.II) to have the lowest occurrence of mastitis
compared to animals with low antibody response. Given the positive
correlation between the selection for increased milk production and the
increased rate of clinical mastitis occurrence, one might hypothesize that
superior production could be associated with unfavourable changes in
host defense which could result in a higher occurrence of mastitis. The
CA 02255423 1998-12-10
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fact that animals of average and high antibody response (Groups 1 and 2)
tended to produce less milk and milk solids per lactation than animals of
the low antibody response group might indicate that selection based on
antibody response to OVA is not economically feasible in the short term.
At a price of $5.15/kg of fat and $8.39/kg protein (Ontario Milk Producer,
Oct.. 1997), animals with low antibody response would earn an estimated
revenue of Cdn$ 4114.49/lactation (based on fat and protein component
pricing only) followed by animals with high antibody response at
$3827.87 per lactation ($286.62 less than Group 3 animals), and animals
with average antibody response at $3814.04 per lactation ($300.45 less than
low antibody response animals and $13.82 less than high response
animals). In the long term however, it may be more beneficial to own
animals with superior health traits that minimize disease-related costs
(approximately $140-300/cow/lactation in Ontario; Zhang et al., 1993) and
still produce milk at an optimal level of production quantity and quality.
A previous U.S. study (Dunklee et al., 1994) determined that health costs
were positively associated with higher production, however, health costs
did not outweigh profit potential. Regardless of whether health costs do
or do not have an impact on the production profit potential of dairy
animals, reduced occurrence of mastitis will nonetheless be mutually
beneficial to dairy producers, processors and consumers. Milk producers
will benefit through a reduction in economic loss incurred by mastitis,
processors manufacturing milk products will benefit from an
enhancement in milk quality, and consumers concerned about animal
welfare and food safety standards will appreciate knowing that antibiotic
usage to treat mastitis has been reduced as a direct result of reduced
mastitis occurrence. Further, as disclosed in this description, as certain
immune response traits are heritable, it would be possible to select or
breed cows with a desired level of immune response which should
influence disease resistance. Milk quantity and quality may also be
influenced by such breeding practice. It may be that cows with higher
CA 02255423 1998-12-10
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than average disease resistance and with high, but not maximum milk
yields would result in maximum profits.
EXAMPLE 5
Effects of Growth Hormone, Insulin-like Growth Factor-I, and Cortisol
on Periparturient Antibody Response Profiles of Dairy Cattle
The objectives of this example were to determine
hormone and antibody response profiles from the prepartum period to
peak lactation, and evaluate potential immunomodulatory effects of the
classic endocrine hormones, growth hormone (GH), insulin-like growth
factor-I (IGF-I) and cortisol. Specifically, 33 Holstein cows were
immunized with ovalbumin (OVA) and Escherichia coli J5 at weeks -8
and -3 prior to parturition. At parturition (week 0), cows received an
additional immunization of OVA. Blood was collected at weeks -8, -3, 0,
3 and 6 relative to parturition and various samples were used to
determine plasma hormone concentration, serum immunoglobulin (Ig),
and specific antibody response to OVA and E. coli. Colostrum and milk
samples were also collected post-parturition to monitor local
immunoglobulin and antibody responses. Results indicated that not all
periparturient cows exhibited depressed immune response, and that
antibody response to OVA could be used to partition cows into 3 groups
recognizing animals with sustained measurable antibody response before
and after parturition (Group 1), animals which responded poorly to
immunization at parturition (Group 2), and animals which did not
respond to immunizations at week -3 or parturition (Group 3). Cows
with the highest antibody response to OVA (Group 1) also tended
(P<_0.10) to have the highest response to E. coli J5 at parturition and had
the lowest incidence of disease, particularly mastitis. Antibody response
to OVA measured in milk tended to be higher in Group 1 cows,
particularly at week 0 (P<_0.06) compared to cows of Group 3. IGF-I was
higher (P50.05) in cows of Group 1 than Group 3 at peak lactation (week
6).
CA 02255423 1998-12-10
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To further understand the complex endocrine-immune
interactions that occur around parturition and their impact on host
resistance, we utilized the dairy cow as a large animal stress model of
pregnancy, parturition, and lactation. To evaluate peripartum and peak
lactation immune response and hormone profiles, 33 cows were
immunized with ovalbumin (OVA) and Escherichia coli (E. coli) J5.
Blood samples were collected to measure antibody response, GH, IGF-I,
and cortisol concentrations at dry-off (approximately 8 weeks prepartum)
and weekly from week -3 to week 6 postpartum.
Materials and methods
Animals and Treatments
Antibody response and hormone profiles of 33 Holstein
cows were examined from approximately eight weeks prepartum (week -
8) based on predicted calving dates to six weeks postpartum (week 6).
Twenty-six animals were multiparous cows and seven were primiparous
heifers. To determine associations between periparturient immune
responses and hormone profiles, animals received an intramuscular (im)
injection of a mastitis endotoxemia preventive vaccine with the
manufacturer's adjuvant (Rhone Merieux E. coli J5, Rhone Merieux,
Lenexa, KS) along with the antigen, OVA (Type VII, Sigma Chemical
Co., St. Louis MO), at weeks -8 (4 mg) and -3 (2 mg). At parturition (week
0), cows received an additional immunization of OVA without adjuvant
dissolved in phosphate buffered saline (PBS - 0.1 M, pH 7.4) (2 mg, im).
OVA was chosen as an inert antigen to which these animals had not
been previously exposed. E. coli J5 was used as an antigen previously
recognized by most dairy cows and of more complex response, but of
biological relevance. Animals were initially classified according to their
serum antibody response curve kinetics to OVA as either high
responders (Group 1) relative to cows that exhibited a lack of measurable
response to immunization either postpartum (Group 2) or pre- and
postpartum (Group 3) (Fig. 13A).
Blood and Milk Sambling Schedule
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Peripheral blood was collected via tail venipuncture at
week -8, and weekly from weeks -3 to 6 relative to parturition. Various
samples were used to monitor plasma hormone concentrations (GH,
IGF-I, cortisol), serum immunoglobulin G1&z, and specific antibody
response to OVA and E. coli J5. Colostrum and milk samples were
collected to monitor specific antibody to OVA and to monitor total IgGl
(weeks 0, 3, 6) and IgG2 (weeks 0 and 3). Colostrum was collected at the
first milking following parturition. Milk samples were stripped from all
quarters approximately 2-4 hr after morning milking. Colostrum and
milk samples were stored frozen without preservative at -20°C until
time of whey separation and immunoglobulin quantification.
ELISA ,for OVA Antibodu Detection In Serum and Whey
Serum was separated from coagulated peripheral blood by
centrifugation and stored frozen (-20~ C) until time of assay. Milk
samples were stored frozen (-20~ C) until time of assay when they were
centrifuged twice (11,000 g, 15 min) to separate fat from whey. Antibody
to OVA was detected by ELISA and quantified based on optical density
measurements according to the procedure described by Burton, et al.
1993. Briefly, 96-well polystyrene plates (Fisher Scientific, Don Mills,
Ont.) were coated with a 3.11 x 10-5 M solution of OVA (OVA, Type VII,
Sigma Chemical Co., St. Louis MO) dissolved in carbonate-bicarbonate
coating buffer (pH 9.6). Plates were incubated (4~C, 48h), then washed
with PBS and .05% Tween 20 solution, (pH 7.4). Plates were blocked with
a PBS-3% Tween 20 solution and incubated (room temperature; rt, 1h).
Plates were washed and diluted test sera (1/50 and 1/200) or milk whey
(Neat, 1 / 10, 1 / 100 and 1 /400) and controls were added using a quadrant
system (Wright, 1987). Sera samples were added in duplicate, and whey
samples were added in quadruplicate. Negative and positive controls
included a pooled sample of pre-immunization sera and a pooled sample
of sera from cows 14 days post secondary immunization respectively.
Plates were incubated at rt for 2h. Subsequently, alkaline phosphatase
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conjugate rabbit anti-bovine IgG (whole molecule) (Sigma Chemical Co.,
St. Louis, MO) was dissolved in wash buffer, added to the plates and
incubated (rt, 2h). P-Nitrophenyl Phosphate Disodium tablets (pNPP)
(Sigma, St. Louis, MO) were dissolved in a 10% diethanolamine substrate
buffer, (pH 9.8). Plates were washed with wash buffer, pNPP was added to
the plates and was then incubated at rt for 30 minutes (min). Plates were
read on a EL311 automatic ELISA plate reader (BIO-TEK Instruments,
Highland Park, VT) and the optical density (OD) was recorded at 405 and
630 nanometres (nm) when the positive control reached OD>_.999. The
mean of the number of replicates added to each plate was corrected to an
OD = 1.0 by multiplying by the inverse of the mean of the positive
controls. Corrected means of each dilution were then added together to
give an additive OD value, indicative of antibody response.
ELISA for E. coli J5 Antibodu Detection In Serum
According to the method described by Rhone-Merieux
Animal Health (Lenexa, KS; 1994 personal communication), heat-killed
E. coli strain J5 (ATCC, Rockville, MD) was coated at a concentration of
6.25 x 10~ colony forming units per mL onto Dynatech Immulon II
polystyrene 96-well flat bottom plates overnight at 4°C. After washing
with wash buffer (PBS plus .05% Tween 20), 1% gelatin was added to
block non-specific binding and plates were incubated (rt, 1h). Plates were
washed and four replicates of test serum (dilutions of 1 / 1000, 1 / 1500,
1/2000 and 1/2500) were added using a modified quadrant system. PBS-
.05% Tween 20 was used as a blank and fetal calf serum (FCS, Bockneck
Laboratories, Can Sera, Rexdale, Ont.) was used as a negative control.
Negative and positive controls prepared from pooled pre- and post
immunization sera were plated respectively. Test sera were incubated (rt,
2h), and plates were washed with PBS-.05% Tween 20. Horseradish
peroxidase conjugate goat anti-bovine IgG whole molecule in PBS
(1/4000; The Binding Site, Birmingham, UK) was added and the plates
were incubated (rt, 1h). After washing, the substrate, 2,2'-azino-di-(3-
ethyl-benzthiazoline sulphonate-6) (ABTS; Boehringer Mannheim,
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Laval, Que.) was added and plates were incubated (rt, 30 min). Plates
were then read on an EL311 automatic ELISA plate reader (BIO-TEK
Instruments, Highland Park, VT) and OD recorded at 405 nm and 490
nm. The mean OD of the four sample replicates were corrected to an
OD=1Ø Based on the immunization protocol and phenotypic
observation of antibody response curve kinetics of all dilutions tested,
the 1 / 1000 dilution consistently allowed for differentiation between
positive and negative controls, exhibiting minimal prozone effect and
therefore was the dilution of choice for comparison between animals.
Radial Immunodi~;fusion Assai!
Radial immunodiffusion was used according to a method
previously described (Mallard et al. 1992) to determine the concentrations
of IgGl&2 in serum and whey from colostrum and milk. Whey from
weeks 0 and 3 were tested for the IgGl&2 subclasses. At week 6 however,
IgG1 only was tested in whey since very low concentrations of IgG2 exist
in normal milk (Butler, 1980).
Disease Occurrence
Occurrence of infectious and metabolic diseases were
recorded throughout the study period since connections within the
endocrine-immune axis may conceivably affect both. Disease events
were classified by number as follows: none = 0, mastitis = 1, ketosis = 2,
and other (diseases occurring at lower frequency in this study; for
example, milk fever and pneumonia) = 3.
Somatic Cell Count
Milk (AM/PM composite sample) was collected weekly
during milking to determine somatic cell count (SCC). Only the SCC
counts which coincided with the day of blood sample collection for each
week are reported. SCC, an indicator of subclinical mammary gland
infection, was transformed to somatic cell score (SCS) for analysis. SCS is
the natural logarithm of SCC in cells/~,L and is calculated as follows
(Shook, 1993):
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SCS=loge(SCC/100) = loge(2) + 3
Hormone Assaus
Peripheral blood samples collected for hormone assay
were immediately put on ice. Samples were centrifuged at 4°C and the
plasma was removed. Plasma from each cow was individually aliquoted
into multiple 1 mL containers and stored frozen at -20°C until time of
each hormone assay.
Cortisol
Plasma cortisol concentration (~g/dL) was determined
using a commercially available Gamma Coat Cortisol 1251, RIA kit
(INCSTAR Corporation, Stillwater, MN). The assay sensitivity was 0.21
~,g/dL and the inter- and intra-assay CV were less than 10%.
IGF-1 and GH
Radioimmunoassay (RIA), as described previously by
Elsasser et al. (1989), was used to determine the IGF-I concentration
(ng/mL) of samples. The IGF-I used for tracer and standards was
recombinant threonine-59-substituted human IGF-I (Amgen; Thousand
Oaks, CA). Based on duplicate samples, all performed on the same day,
the intra-assay CV was less than 10%. GH concentration (ng/mL) was
quantified using RIA (Elsasser et al., 1988).
Statistical Methods
Least squares analysis of variance (ANOVA) and
corrected means (least square means, LS Means) were generated using the
General Linear Models (GLM) Procedure of the Statistical Analysis
System (SAS; Helwig and Council, 1979). The statistical models used in
this study included fixed effects of antibody response groups (1,2,3), cow
nested within antibody response group, and week relative to parturition
(weeks -3, 0, 3, and 6). In preliminary analysis, the effect of parity was not
significant and was therefore removed from all subsequent models. A
model was constructed for the following dependent variables: antibody
response to OVA in sera and whey, antibody response to E. coli J5 in sera,
and the concentration of IgGl&2 in serum and whey. Sources of variation
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included in the model for each dependent variable are summarized in
Table 7. Hormone concentrations (GH, IGF-I, cortisol) were included as
covariates in all models. Data that did not show a normal distribution as
indicated by the univariate procedure of SAS, were transformed to
natural logarithms. Pearson product moment correlation coefficients
between immune response variables and hormone concentrations were
generated using the correlations procedure of SAS (Proc CORR). Results
were considered to be statistically significant if the P-value was <_.05 and
trends were reported at the P-value <_.10.
Results
Antibod~r Resroonse to OVA
erum
Serum antibody response to OVA varied significantly
over the peripartum period and individuals could be readily classified
into three immune response groups: high responders (Group 1, n=12; 6
heifers, 6 cows) relative to animals which exhibited a lack of measurable
response to immunization either postpartum (Group 2, n=12 cows) or
pre- and postpartum (Group 3, n=9; 8 cows, 1 heifer). Approximately 1/3
(Group 1) of the animals showed consistent, above average serum
antibody response to OVA following immunization at weeks -8, -3, and 0
relative to parturition. The remaining animals had either an average
amount of antibody, or had responses lower than the population mean
and did not respond following immunization at week -3 or 0 relative to
parturition (Fig. 13A). All cows including those of Group 3 exhibited
responses greater than background (week -8) at week -3 and therefore
were considered low responders rather than non-responders. ANOVA
indicated that the statistical model accounted for 94.19% of the total
variation in serum antibody response to OVA over the peripartum
period, and that the effects of cow (P<_.0001), antibody response group
(P<_.005), and the interaction between antibody response group and week
(P<_.0001), contributed significantly to the variation in antibody response
to OVA (Table 7). Growth hormone (GH) exhibited some tendency to be
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positively associated with antibody response to OVA (P<_.15). Animals in
Group 1, with the highest antibody response to OVA, consistently had
the highest GH concentrations in plasma at each sample week in
comparison to animals in Groups 2 and 3 (Fig. 14A). Although these
differences as determined in the ANOVA may not have been statistically
significant (Table 7), correlation analysis indicated a significant and
positive relationship (r2 = .29, P<_.001) between antibody response to OVA
and GH, regardless of week or antibody response group (Table 8). This
would suggest that there is biological significance to the consistently
higher GH concentrations in the high immune response group (J.L.
Burton 1991, PhD Thesis, University of Guelph). LS Means of IGF-I and
cortisol concentrations in plasma (Fig. 14B,C) were not significantly
different between immune response groups, except at week 6 when
Group 1 cows had higher concentrations of IGF-I (P<_.05) compared to
cows in Group 3 (Fig. 14B). Correlation analysis of antibody response to
OVA indicated relationships with IGF-I (r2=-.19, P<_.04) and cortisol
(r2=.17, P<_.06) (Table 8).
W
ANOVA indicated that cow (P<_.006), antibody response
group (P<_.003) and the interaction between IGF-I concentration and week
(P<_.005) contributed significantly to the variation in whey antibody
response (Table 7). There was a tendency for week relative to parturition
(P<_.06) to associate with antibody response to OVA in whey. Corrected
population least square means (LS Means) of antibody response to OVA
in whey declined significantly following parturition, such that at week 0
the OD value was 1.68 ~ .17 compared to .85 ~ .17 (P<_.004) at week 3 and
.50 ~ .20 (P<_.0001) at week 6 (Fig. 13B). At parturition, there was a
tendency (P<_.06) for antibody response to OVA in whey to differ between
Groups 1 (1.96~.26) and 3 (1.33~.23). Comparable to the antibody response
to OVA in serum, correlation analysis indicated a significant relationship
between OVA antibody response in whey with GH (r2 = .31, P<_.0005) and
IGF-I (r2 = -.22, P<_.01) (Table 8).
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Antibodu Res~nonse to E. coli l5
Only the effect of cow (P<_.0002) contributed significantly
to the variation in antibody response to E. coli J5 . Pre-immunization
sera (week -8) indicated that these cows had minimal background OD
values of measurable E. coli J5 specific antibody prior to vaccination
(population mean ~ SEM = .314 ~ .11; n=33) compared to post-vaccination
natural antilogarithm OD values at week -3 (.663) and week 0 (.830).
Antibody response to E. coli when grouped by antibody response group
(1, 2, or 3), indicated that only at parturition (week 0) did Group 1 animals
tend (P<_.10) to have a higher concentration of E. coli specific antibody
(OD value = 1.053) than Group 3 animals (OD value =.702). Correlation
analysis indicated that GH was significantly correlated with antibody
response to J5 E. coli (r2 =.18, P<_.04) (Table 8). Antibody response to E.
coli J5 was positively correlated with antibody response to OVA (r2=.59,
P<_.0001).
~~ F~ IgG2 in serum, colostrum, and milk
Antibody response group significantly contributed to the
variation of serum IgG2 (P<_.002) only. There was a tendency for the
interaction between IGF-I and week relative to parturition to account for
variation in total whey IgGI concentration (P<_.07). The model
constructed for whey IgG2 was unable to explain the variation in this
response. Correlation analysis indicated a significant, negative
relationship between GH and IgGl in serum (r2=-.26; P<_.01). Conversely,
IGF-I tended to correlate positively with total IgGl in serum (r2=.19,
P5.07). Growth hormone (r2=.26, P<_.03) and IGF-I (r2=-.20, P<_.10)
correlations with IgGl in whey were reversed from that in serum.
Disease Occurrence
Records of disease events indicated that 54.5% of the 33
animals evaluated were considered healthy during this study. Of the
diseased animals, 7 cows had mastitis events (21.21%), 7 had ketosis
events (21.21%) and 3 cows had other disease events (9.09%) while on
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this study. Group 1 animals which showed a consistent above average
antibody response to OVA, had the lowest percent occurrence of disease
(Fig. 15) and actually had no occurrence of clinical mastitis.
Somatic Cell Score (SCS)
At parturition, LS Means of SCS were significantly lower
(P_<.05) for Group 2 cows (SCS=3.2) compared to Group 1 (SCS=4.36) and
Group 3 (SCS=4.98) cows. At weeks 2,3,4, and 6 after parturition, all
groups differed significantly from one another, and, Group 1 cows
consistently had the lowest SCS while Group 3 cows consistently had the
highest SCS.
Hormones
At parturition, least square mean (LS Mean)
concentrations of GH (Fig. 14A) and cortisol (Figure 14C) were at a
maximum while IGF-I (Fig. 14B) was at a minimum. After parturition,
GH concentrations decreased (P<_.05) until week 6. Cortisol
concentrations also decreased (P5.05) post-parturition and then increased
slightly after week 3. In contrast, IGF-I concentrations decreased (P<_.05) at
parturition and then continued to increase (P<_.05) toward peak lactation.
The present study is the first to simultaneously evaluate
specific antibody responses and hormone profiles during the peripartum
period and has revealed some associations between IGF-1 and antibody
response, however, no actual cause and effect relationship can be
established from this study. In addition, an elevation of plasma IGF-I
during the latter stages of pregnancy followed by a dramatic decline
around parturition with a steady increase in concentrations following
parturition was demonstrated. Some of these observations have been
confirmed in the literature; for instance, Vega et al. (1991) attributed
changes in IGF-1 and GH to the decrease in metabolic demands associated
with the cessation of milk production, during late gestation, followed by
an increase in metabolic demand associated with the onset of lactation at
parturition. As well, the demand of the mammary gland may alter the
transport of IGF-I by sequestering it from the blood. Lactogenic
CA 02255423 1998-12-10
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hormones, such as prolactin and cortisol, may also prevent the synthesis
of IGF-I and IGF-I binding proteins (Vega et al., 1991).
As previously reported (Hoshino et al., 1991), circulating
concentrations of GH increased around parturition, concurrent with
early milk production, and decreased as lactation progressed. The
inverse relationship between peripartum IGF-I and GH is noteworthy in
that IGF-I production is normally dependent on GH as blood
concentrations influence liver production of IGF-I (Burton et al., 1992).
However, due to the peripartum uncoupling between these two
hormones, it may be possible to evaluate the influence of each hormone
separately on both the innate and humoral aspects of the immune
system.
Although the interaction of GH, and to a lesser extent
IGF-I, with the immune system has been widely reported in a variety of
species including dogs, humans and mice, direct effects of GH on
lymphoid cells have not been unequivocally demonstrated. For
example, GH deficient patients often are not found to be immuno-
compromised (Fornari et al., 1994). Furthermore, various studies have
demonstrated that the immune systems of GH deficient children treated
with GH can be normal, suppressed or even enhanced (Gupta et al., 1983;
Kelley, 1990; Petersen et al., 1990). It is also suggested that some effects
of
GH on the immune system are a result of IGF-I (Burton et al., 1992;
Badolato et al., 1994). In general these studies indicate that the precise
relationships between these hormones and immune responsiveness will
be challenging to untangle. In the present study, GH concentration was
positively, and IGF-I negatively correlated with antibody response.
Animals in Group 1 with the highest antibody response to OVA, tended
to have the highest GH concentrations. Although some of the results
were not necessarily statistically significant, correlation analysis indicated
a positive relationship between antibody and GH. For this reason the
present invention may be used to select high immune responders with
naturally enhanced levels of growth hormone. Thus the benefits of
CA 02255423 1998-12-10
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higher levels of growth hormone in animals could be obtained, while
avoiding the side effects associated with artificially enhancement of
growth hormone levels such as those associated with the use of synthetic
growth hormones. For the most part, IGF-I concentrations were not
different among immune response groups, except at week 6 when cows
of Group 1 had significantly higher concentrations than Group 3 cows.
Thus, the methods of the present invention could be used to identify or
select for animals with high post- peripartim IGF-1 levels. These results
are consistent with the work of Yoshida et al., (1992) which demonstrated
that GH stimulates B cell growth and Ig synthesis by B cells and B cell
lines. Growth hormone has been reported to alter antibody synthesis in
response to T-dependent antigens, as well as increase activity of T
lymphocytes and natural killer (NK) cells (Geffner et al., 1990;
Schurmann et al., 1995). Badolato et al., (1994) found that B cells
displayed relatively high numbers of GH receptors, whereas T and NK
cells showed much lower numbers of receptors. In addition, increased
GH concentrations can enhance otherwise suppressed antibody response
due to stress released glucocorticoids (Franco et al., 1990). Again, it has
been suggested that the effects of GH may be mediated through IGF-I, a
lymphocyte growth factor (Franco et al., 1990), but whether this is true
during the peripartum period when these hormones become uncoupled
seems unlikely.
While the present invention has been described with
reference to what are presently considered to be the preferred examples, it
is to be understood that the invention is not limited to the disclosed
examples. To the contrary, the invention is intended to cover various
modifications and equivalent arrangements included within the spirit
and scope of the appended claims.
All publications, patents and patent applications are
herein incorporated by reference in their entirety to the same extent as if
each individual publication, patent or patent application was specifically
and individually indicated to be incorporated by reference in its entirety.
CA 02255423 1998-12-10
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DETAILED FIGURE LEGENDS
Figure 1. LS Means of antibody response to OVA in A) serum and B)
whey by antibody response group following immunization at weeks -8, -
3, and 0 as measured by enzyme linked immunosorbent assay (ELISA).
Group 1 = high measurable antibody response; Group 2 - lack of
measurable response to immunization postpartum (week 0); Group 3 -
lack of measurable response to immunization pre- and postpartum; Pop
= population mean. Animal classification is based on serum antibody
response to OVA. Significant differences between animals in the three
groups are indicated by different letters above error bars (P<_.05).
Figure 2. Percent disease occurrence by antibody response group. Group
1 = high measurable antibody response; Group 2 = lack of measurable
response to immunization postpartum (week 0); Group 3 = lack of
measurable response to immunization pre- and postpartum. Animal
classification is based on serum antibody response to ovalbumin (OVA).
Figure 3. LS Means of serum antibody response to ovalbumin (OVA) by
antibody response group. Group 1 = high antibody response, Group 2 =
average antibody response, and Group 3 = low antibody response based
on described index, and Population mean (PM). Significant differences
between groups are indicated with lower case letters between groups and
differences over time are indicated by different uppercase letters (P<0.05).
Figure 4. LS Means of whey antibody response to ovalbumin (OVA) by
antibody response group for A) Herd 1, B) Herd 2 and C) Herd 3. Group 1
= high antibody response, Group 2 = average antibody response, and
Group 3 = low antibody response based on described index, and
Population mean (PM). Significant differences between groups are
indicated with lower case letters and differences over time are indicated
by different uppercase letters (P<0.05).
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Figure 5. LS Means of sera antibody response to E. coli for A) Herd 1, B)
Herd 2 and C) Herd 3. Group 1 = high antibody response, Group 2 =
average antibody response, and Group 3 = low antibody response based
on described index, and population mean (PM). Significant differences
between groups are indicated with different lower case letters (P<0.05).
Figure 6. LS Means of IgGl in A) sera and B) whey. Group 1 = high
antibody response, group 2 = average antibody response, and Group 3 =
low antibody response based on described index, and Population mean
(PM). Significant differences between groups are indicated with lower
case letters and differences over time are indicated by different upper case
letters(P<0.05).
Figure 7. Rate of Mastitis occurrence (%) by antibody response group
within herd.
Figure 8. LS Means of Somatic Cell Score by antibody response group for
A) Herd 1; B) Herd 2; and C) Herd 3. Group 1 = high antibody response,
Group 2 = average antibody response, and Group 3 = low antibody
response based on described index, and Population mean (PM).
Significant differences between groups are indicated with different lower
case letters (P<0.05).
Figure 9. Type III LS Means of counts per minute (cpm) measuring
unstimulated (1A) and stimulated lymphocyte proliferation to
ovalbumin (OVA; 1B) and concanavalin A (Con A; 1C). Group 1 = high
antibody response to OVA, Group 2 = average antibody response to
OVA, and Group 3 = low antibody response to OVA and Population
mean =PM. Significant differences between groups are indicated with
lower case letters and differences over time are indicated by different
upper case letters(P<0.05).
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Figure 10. Percent increase in skin thickness 48 hours after challenge
with the purified protein derivative of tuberculin (PPD) in cows and
heifers previously sensitized to BCG.
Figure 11. Type III LS Means of lymphocyte counts (cells/mL) in blood
during the peripartum period. Group 1 = high antibody response to
OVA, Group 2 = average antibody response to OVA, and Group 3 = low
antibody response to OVA and Population mean =PM. Significant
differences between groups are indicated with lower case letters and
differences over time are indicated by different upper case letters(P<0.05).
Figure 12. Type III LS Means of projected 305 day yield for milk (1A),
protein (1B), and fat (1C). Group 1 = high antibody response, Group 2 =
average antibody response, and Group 3 = low antibody response based
on described index, and Population mean (PM): Significant differences
between groups are indicated with lower case letters (P<0.05).
Figure 13. LS Means of antibody response to OVA in A) serum and B)
whey by antibody response group following immunization at weeks -8, -
3, and 0 as measured by enzyme linked immunosorbent assay (ELISA).
Group 1 = high measurable response; Group 2 = lack of measurable
response to immunization postpartum (week 0); Group 3 - lack of
measurable response to immunization pre- and postpartum; Pop =
population mean. Animal classification is based on serum antibody
response to OVA. Significant differences between animals in the three
groups are indicated by different letters above error bars (P<_.05).
Figure 14. LS Means of hormone concentrations by antibody response
group as determined by radioimmunoassay (RIA). Figure 14A = growth
hormone (GH); Figure 14B = insulin-like growth factor-I (IGF-I); Figure
14C = Cortisol. Group 1 = high measurable response in serum; Group 2 =
CA 02255423 1998-12-10
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lack of measurable response to immunization postpartum (week 0);
Group 3 = lack of measurable response to immunization pre- and
postpartum; Pop = population mean. Animal classification is based on
serum antibody response to ovalbumin (OVA). Nat. log. - natural
logarithm. Significant differences between animals in the three groups
are indicated by different letters above standard error bars (P<_.05).
Figure 15. Percent disease occurrence by antibody response group. Group
1 = high measurable response in serum; Group 2 = lack of measurable
response to immunization postpartum (week 0); Group 3 = lack of
measurable response to immunization pre- and postpartum. Animal
classification is based on serum antibody response to ovalbumin (OVA).
CA 02255423 1998-12-10
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Table 1. Analysis of variance of antibody response to ovalbumin (OVA) and E.
coli
J5,
and the concentration of immunoglobulin G1&2 in serum and whey
Source of Variation
Dependent R2a C.V.b Cow Week Groupa Group* W
Variable (%) (%) (Group)°
Antibody
Response
Serum anti-88.31 21.66 0.0001 0.0001 0.0001 0.0001
OVA
Whey anti-74.97 -127.86 0.0001 0.0001 0.0001 0.09
OVA
Serum anti-78.29 -60.42 0.0001 0.0001 0.0001 nsf
E. coli
Immunoglobuli
n concentration
Serum IgGI64.91 6.97 ns 0.0001 ns ns
Serum IgG267.19 4.22 ns 0.0001 0.0001 0.004
Whey IgG~ 87.16 18.92 ns 0.0001 ns ns
Whey IgG2 95.11 14.15 ns 0.0001 ns ns
a R2 = coefficient of determination
b C.V. = coefficient of variation
c Cow(Group) = Cow nested within group
d Group= variation due to antibody response group
in which cows
are classified as high or low responders based
on antibody response
to OVA
a negative C.V. are from log-transformed data
f ns= not significant
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Table 2. Analysis of variance of antibody response to ovalbumin (OVA) and E.
coli J5, the
concentration of immunoglobulin G~&2 in serum and whey, and somatic cell score
(SCS)
Source of Variation
Dependent Variable Rza C.V_h Herd Season- Cow° Group' Parity Group*
Week Group* Parity*
(%) (%) yr' parity Week Week
Antibody
Response 79.4127.63 - - 0.0001 0.0001 nst 0.00010.0001
Serum anti-OVA 0.096 -
Whey anti-OVA73.7332.16 0.02 - 0.0001 0.0001 ns 0.0001ns -
ns
Herd 1 75.34- - - 0.0001 0.0001 - 0.00010.05 -
0.0001
130.01
h
Herd 2 71.29-524.16- - 0.0001 0.007 - - 0.00010.07 -
Herd 3 82.726682.1- - 0.0001 0.0002 - 0.0001ns -
-
Serum anti-E,74.23-43.720.003 - 0.0001 - 0.0004 - 0.0001- 0.0001
cofi
Herd 1 78.63-54.79_ - 0.0001 ns - - 0.00010.06 -
Herd 2 76.89-45.16- - 0.0001 ns 0.0001 0.00010.0001ns -
Herd 3 70.63-31.53- - 0.0001 ns - - 0.00010.002
I -
l
b
li
mmunoQ
o 49.747.53 - - ns 0.07 ns ns 0.0001ns -
u
n
Concentration
SerumIgGt
SerumIgGz 63.144.34 0.0001 - 0.0001 ns 0.04 ns 0.025 ns -
Herd 1 59.974.67 - - 0.02 ns - - 0.0015ns -
Herd 2 48.494.36 - - 0.021 0.08 ns 0.08ns ns -
Herd 3 56.143.7 - - 0.005 - 0.04 - 0.09 - ns
Whey IgGt 90.115.11 - - ns ns 0.01 ns 0.0001ns -
Whey IgGz 96.8513.5 0.03 - ns ns 0.0009 ns 0.0001ns -
Herd 1 94.8514.96 - - ns ns - - 0.0009ns -
Herd 2 ns ns - - ns ns 0.08 ns 0.02 ns -
Herd 3 97.4112.94 - - ns 0.097 - - 0.0001ns -
Somatic
Cell Score83.5144.89 - - 0.0001 ns - - 0.0013ns -
SCS(Herd
1)
SCS(Herd 81.4346.7 - - 0.0001 ns - - 0.0001ns -
2)
SCS(Herd 78.8426.54 - - 0.0001 ns - - ns ns __
3)
a Rz = coefficient
of deterntination
b C.V. = coefficient
of variation
c Season-Year = season
and year of calving
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d Cow nested or 'grouped' within the interaction between antibody response
group and parity. i.e.
Cow(group*parity). If parity is not significant, parity is removed and the
model becomes cow nested within
antibody response group only.
a Group = variation due to antibody response group in which cows are
classified as high or low responders
based on antibody response to OVA
f ns= not significant
g ------- = not significant therefore removed and no longer relevant to that
dependent variable
h Coefficient of variation is negative due to analysis of variance of natural
logarithm transformed data
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Table 3. Percent Occurrence (%) of clinical mastitis by antibody response
group within herd
% Occurrence an Antibody
of Mastitis
within
Response Group
Herd Group Group 2 Group Overall
1 3
Mastitis
Frequency
by Herd
Herd # of animals n= 4 n=22 n=6 n=32
1
% with mastitis0 21.7 33.3 21.2
Herd # of animals n=13 n=47 n=7 n=67
2
% with mastitis15.4 2.1 0 4.5
Herd # of animals n=1 n=26 n=10 n=37
3
% with mastitis0 11.5 10 10.8
All herds# of animals n=18 n=95 n=23 n=136
% Overall Mastitis11.1 9.3 13.6 -
Frequency by
Group
CA 02255423 1998-12-10
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Table 4. Analysis of Variance of lymphocyte proliferation to ovalbumin (OVA)
and concanavalin A (Con A), lymphocyte and neutrophil number, delayed type
hypersensitivity and somatic cell score
DependentR2a CVb Herd Season-CoWd GroupParityGroupWeek Group Par-
Variable Yearc a *P~tY *Week ity*V
(%)
ek
Lvmuhocvte
Proliferation
Unstimulated58.69I1.5 --f -- 0.0001nsg ns 0.00010.00010.05 --
O V A 85.756.34 -- -- 0.00010.01 0.00060.0001ns 0.009 --
Con A 67.2 5.15 -- -- 0.0001ns 0.0040.00010.00010.0002--
Com
Blood
Cell
ounts
Lymphocytes83.1617.31-- -- 0.0001ns 0.00010.00010.08 ns --
Segmented37.422.81 -- -- 0.003ns ns ns ns ns --
Neutrophils
Banded ns ns -- -- ns ns ns ns ns ns --
Neutrophils
Somatic
Cell
Score
Herd 1 83.5 44.89-- -- 0.0001ns -- -- 0.001ns --
Herd 2 81.4346.7 -- -- 0.0001ns -- -- 0.0001ns --
Herd 3 78.8426.54-- -- 0.0001ns -- -- ns ns
a R2 fficient
= of
coe determination
b CV= coefficient of variation
c Season-Year = season and year of calving
d Cow nested or 'grouped' within the interaction term between
antibody response group and parity i.e. cow(Group*parity). If
parity is not significant, it is removed and the cow term then
becomes 'grouped' within antibody response group
a Group = variation due to antibody response group in which cows
are classified as high or low responders based on antibody response
to OVA
f - = not relevant to that dependent variable and therefore removed
from the model
g ns= not significant
CA 02255423 1998-12-10
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Table 5. Correlation analysis of antibody to ovalbumin (OVA) with unstimulated
and stimulated lymphocyte proliferation to OVA and concanavalin A (Con A), and
cutaneous delayed type hypersensitivity (DTH) response to purified protein
derivative
(PPD) of M. tuberculosis.
Dependent VariableIndependent Variable r2 P-value
Unstimulated Antibody to OVA -0.26 0.0001
Lymphocyte
Proliferation
OVA-Stimulated Antibody to OVA -0.27 0.0001
Lymphocyte
Proliferation
Con A-StimulatedAntibody to OVA -0.14 0.0001
Proliferation
DTH - 48 hours Antibody to OVA ns ns
DTH - 72 hours Antibody to OVA ns ns
CA 02255423 1998-12-10
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Table 6. Analysis of Variance (ANOVA) of projected 305-day milk, protein and
fat
yields
DependentR2a(%) CVb He~l Season-YearGroups ParityGroup *Parity
Variable
Milk yield19.5 14.98 - a - 0.06 0.00010.0001
Protein 15.26 14.76 - - 0.0001 0.00010.0001
yield
Fat Yield17.51 13.53 - - 0.0001 0.00010.0001
a R2 = coefficient of determination
b CV= coefficient of variation
c Season-Year = season and year of calving
d Group = variation due to antibody response group in which cows are
classified
as high or low responders based on antibody response to OVA
- = not relevant to that dependent variable and therefore removed from the
model
CA 02255423 1998-12-10
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Table 7. Analysis of variance of antibody response to ovalbumin (OVA) and E.
coli
J5,
and the concentration of immunoglobulin G1&2 in serum and whey
Source of Variation
Dependent Rza C.V.n Cow Week Group Group GHe GH* IGF-If IGF-I* Corte Cort*
Variable loll 1%1 (Group)° ° * Week Week week
Week
Antibody
Response
Setvm OVA 94.19 14.01 0.0001 nsh 0.005 0.0001 0.15 ns ns ns ns ns
Whey OVA 83.81 37.36 0.006 0.06 0.003 ns ns ns 0.12 0.005 ns ns
E. coli 79.1 97.18 0.0002 ns ns ns ns ns ns ns ns ns
Immunoglobulin
Serum IgGt 73.17 7.88 ns ns ns ns ns ns ns ns ns ns
Serum IgG2 75.47 4.63 ns ns 0.001 ns ns ns ns ns ns ns
Whey IgG~ 94.66 16.46 ns ns ns ns ns ns ns 0.07 ns ns
Whey IgG2 87.25 30.92 ns ------ ns ------- ns ------- ns ------- ns -------
_ _ _ -
a R2 = coefficient of determination
b C.V. = coefficient of variation
c Cow(Group) = Cow nested within group
d Group= variation due to antibody response group in which cows
are classified as high or low responders based on antibody response
to OVA
e,f,g = growth hormone, insulin-like growth factor-I, cortisol,
h ns= not significant
i ------ = not relevant to that dependent variable
CA 02255423 1998-12-10
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Table 8. Correlation analysis of hormone concentration with antibody
response to ovalbumin (OVA), and E. coli J5, and the concentration
of IgGI&2 in serum and whey
Dependent Variable Independent Variable r2a P value
Antibody Response
Serum OVA GHb 0.29 0.001
IGF-I~ -0.19 0.04
isold
~
Whey OVA ~ 0.31 0 0005
IGF-I -0.22 0.01
Cortisol -- ns
E. coli JS GH 0.18 0.04
IGF-I -- n s
Cortisol -- n s
Radial Immunodiffusion
Serum IgGI GH -0.26 0.01
IGF-I 0.19 0.07
Cortisol -- n s
Serum IgG2 GH -- ns
IGF-I -- n s
Cortisol -- ns
Whey IgGI GH 0.26 0.03
IGF-I -0.2 0.1
Cortisol -- ns
Whey IgG2 GH -- ns
IGF-I -- ns
Cortisol -- n s
a r2 = SAS Pearson Product Moment Correlation Coefficient
b, c, d, = growth hormone, insulin-like growth factor-I, cortisol
