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Journal of Animal Science - Animal Production

Effect of dam parity on litter performance, transfer of passive immunity, and progeny microbial ecology1

 

This article in JAS

  1. Vol. 91 No. 6, p. 2885-2893
     
    Received: Nov 02, 2011
    Accepted: Feb 25, 2013
    Published: November 25, 2014


    2 Corresponding author(s): tburkey2@unl.edu
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doi:10.2527/jas.2011-4874
  1. E. E. Carney-Hinkle,
  2. H. Tran,
  3. J. W. Bundy,
  4. R. Moreno,
  5. P. S. Miller and
  6. T. E. Burkey 2
  1. Department of Animal Science, University of Nebraska, Lincoln 68683

Abstract

Litter performance and progeny health status may be decreased in progeny derived from primiparous sows but improve with increasing parity. The objective was to evaluate litter performance, the production and passive transfer of Ig, and fecal microbial populations in progeny derived from first parity (P1) compared with fourth parity (P4) dams. Litter performance was recorded for P1 (n = 19) and P4 (n = 24) dams including number of pigs/litter (total born, born live, stillbirths, mummified fetuses, prewean mortality, and pigs weaned) and average litter and piglet BW at birth (d 0), d 7, d 14, and at weaning (average d 19). Blood samples were collected from all dams on d 90 and 114 of gestation and d 0 of lactation. Colostrum and milk samples were collected from each dam on d 0, 7, and 14 of lactation for quantification of IgG and IgA. Blood and fecal samples were collected from each litter (n = 6 pigs/litter) on d 1, 7, and 14 after parturition. Circulating IgG and IgA concentrations were quantified in all blood samples. Denaturing gradient gel electrophoresis (DGGE) was used to characterize similarity and diversity of fecal microbes among progeny. Progeny of P1 dams had decreased average litter BW at d 7 (25.7 vs. 30.0 kg; P < 0.03) and decreased average piglet BW throughout the experiment (d 0, 7, 14, and 19; P < 0.001) compared with P4 progeny. No parity × day interactions were observed with respect to immunoglobulin or microbial analyses. Concentrations of IgA tended to be greater (P = 0.09) in samples of colostrum and milk obtained from P4 compared with P1 dams. Serum IgG concentrations were greater (P < 0.02) in P4 progeny compared with P1 progeny. Results of DGGE revealed that P1 progeny had increased (P < 0.001) microbial similarity on d 7 and decreased (P < 0.03) microbial similarity on d 14 compared with P4 progeny. Progeny of P1 dams tended (P = 0.07) to have a greater Shannon’s diversity index compared with P4 progeny on d 1, and P1 progeny had a greater (P < 0.03) Shannon’s diversity index compared with P4 progeny on d 7. Litter performance, passive transfer of immunity, and progeny microbial ecology were affected by dam parity.



INTRODUCTION

Decreased rates of growth in first parity (P1) progeny compared with progeny derived from older dams [progeny derived from second parity or greater dams (≥P2)] may be attributed to reduced health status as a result of factors including animal stress, passive immunity, and susceptibility to pathogens (Mahan, 1998; Moore, 2001; Le Dividich et al., 2005). Passive immunity is provided by immunoglobulins in colostrum and milk. Previous work has revealed that IgA and IgG concentrations are greater in colostrum from older parity dams compared with younger dams (Inoue et al., 1980; Inoue, 1981a; Klobasa et al., 1985, 1986). Preliminary research conducted by our group evaluating the effects of dam parity [P1 vs. third parity (P3)] on passive transfer of immunity observed increased IgG and IgA concentrations in P3 dams (serum and milk/colostrum) and progeny compared with P1 dams and progeny (Burkey et al., 2007).

Increasing evidence has supported that the gastrointestinal (GIT) microbiota play an important role in immune system development. Germ-free animals have impaired development and maturation of immune factors (Tlaskalova-Hogenova et. al., 1970; Kelly et al., 2007; Round and Mazmanian, 2009) and microbial colonization of the gut promotes maturation of the immune system (Casola and Rajewsky, 2006; Round and Mazmanian, 2009). Research by our group (Tran et al., 2011) and others (Berg, 1996; Guarner and Malagelada, 2003) have used fecal microbial similarity and diversity indexes as indicators of GIT health. Differences in immunoglobulin concentrations among dams may affect progeny GIT microbial ecology as well as immune system development.

Based on preliminary data, we hypothesized that fourth parity (P4) dams and progeny would have increased litter performance and passive immunity and altered gastrointestinal microbiota compared with P1 dams and progeny. Therefore, the objective of this experiment was to evaluate litter performance, passive transfer of immunoglobulins, and fecal microbial ecology among P1 and P4 dams and progeny.


MATERIALS AND METHODS

The experimental protocol was reviewed and approved by the Institutional Animal Care and Use committee of the University of Nebraska, Lincoln.

Experimental Design

Dams (Large White × Landrace) used in the current study included P1 (n = 19) and P4 dams (n = 24) that farrowed during a 22-d period beginning December 17, 2007, and ending January 7, 2008. Dams and pigs used in this experiment were of a high health status with no clinical signs of porcine reproductive and respiratory syndrome virus. Dams (P1 and P4) were commingled and housed in stalls during gestation and moved to farrowing crates approximately 5 d before their expected farrowing date. Cross fostering of piglets occurred within parity to equalize litter size. Dam and litter performance parameters recorded included number of pigs/litter [total born, born live, stillbirths, mummified fetuses, pigs that died before weaning (mortality), and pigs weaned] and average litter and piglet BW at birth (d 0), d 7, d14, and at weaning (d 19). On d 0 (after parturition between 0600 and 1000 h) all piglets were processed including injection of 200 mg Fe (Fe dextran; Uniferon 200; Watchung, NJ), tail docking, and ear tagging. No antibiotics were administered during the current experiment. Boar piglets were castrated before 3 d of age. All piglets from each litter were weighed on d 0, 7, and 14 and at weaning (19 ± 1 d of age). The entire experiment was conducted at the University of Nebraska Agricultural Research and Developmental Center Swine Unit near Mead, NE.

Blood, Milk, and Fecal Sample Collection

All blood, milk, and fecal samples were collected between 0600 and 1000 h. Blood samples (7.5 mL of blood) were collected from all sows via jugular venipuncture at 2 time points during gestation (d 90 and 114) and after parturition (d 0; between 0600 and 1000 h; 1 to 3 h postfeeding). During lactation, on d 0, 7, and 14 colostrum/milk samples were collected by hand from all functional mammary glands (5 to 10 mL per dam) in a sterile tube and frozen (–20°C) for subsequent analyses. Oxytocin was not administered to sows on d 0 of collection. For d 7 and 14 milk sample collection, 1 mL (20 IU) of oxytocin (Oxytocin-RXV; Bimeda-MTE-Animal Health, Inc., Cambridge, Canada) was administered (intramuscularly near the vulva) to facilitate milk collection.

Blood samples (2.0 to 4.0 mL blood) were collected from 6 piglets from each litter (n = 19 for P1 and n = 24 for P4 litters, respectively) via jugular venipuncture on d 1, 7, and 14. Piglets were randomly selected for sampling on d 1 and then assigned ear tags to identify same pigs on d 7 and 14. Dam and piglet serum was harvested by centrifugation (1,500 × g for 20 min at 4°C) and frozen (–20°C) for subsequent analyses.

Due to limited gel capacity and heavy gel bias only 3 sows per parity were chosen for fecal microbial analysis. Sows were chosen based on location (all sows were in the same room) and expected farrowing date to reduce effect of environment on fecal microbiota. Fecal samples were collected directly from the rectum from individual pigs using a fecal loop (KV Supply, David City, NE) on d 1, 7, and 14 from 3 piglets from each litter. Fecal samples were stored in PBS and frozen (–20°C) for subsequent analyses (described below).

Milk and Serum Analyses

Colostrum and milk samples were diluted (1:50,000) and concentrations of IgA and IgG were quantified as described below. Dam and piglet serum samples were diluted (1:100,000 and 1:25,000, respectively) for IgG and IgA analyses. Concentrations of IgA and IgG in serum, colostrum, and milk were quantified via swine-specific ELISA (Bethyl Labs Inc., Montgomery TX) using goat anti-pig antibody. The range of the immunoglobulin ELISA was 7.81 to 1,000 ng/mL and sensitivity was 2.0 ng/mL. The intra- and interassay CV for the IgA ELISA was 2.9 and 8.7% and 3.1 and 8.9%, respectively, for serum and milk analyses. The intra- and interassay CV for the IgG ELISA was 3.1 and 8.5% and 3.3 and 9.0%, respectively, for serum and milk analyses.

Fecal Microbial DNA Extraction

Extraction of DNA from all fecal samples was performed according to the methods described by Rasmussen et al. (2009). Briefly, fecal samples were thawed and a 500-mL aliquot of each sample was used for DNA isolation. All samples were washed with PBS and centrifuged (8,000 × g for 5 min at 4°C); the wash step was repeated 3 times. The bacterial pellet was resuspended in 750 μL Lysis buffer [200 mM NaCl, 100 mM Tris (pH 8.0), 20 mM EDTA, and 20 mg/mL Lysozyme] and transferred to a microcentrifuge tube containing 300 mg of 0.1 mm zirconium beads (BioSpec Products, Bartlesville, OK). Samples were incubated for 20 min at 37°C. After incubation, 85 μL of 10% SDS solution and 40 μL Proteinase K (15 mg/mL; Sigma, St. Louis, MO) were added to samples followed by another incubation period at 60°C for 15 min. Phenol:chloroform:isoamyl alcohol (25:24:1; 500 μL) was added and the samples were homogenized in a MiniBeadbeater-8 (BioSpec Products) at maximum speed for 2 min. Samples were placed on ice before separating layers by centrifugation at 10,000 × g for 5 min at 4°C. The top layer was extracted twice with phenol:chloroform:isoamyl alcohol (25:24:1) and twice with chlorophorm:isoamyl alcohol; DNA was recovered by standard ethanol precipitation. Pellets were dried for 30 min at room temperature. The DNA pellet was resuspended in 100 μL of Tris/HCl buffer (10 mM; pH 8.0). The DNA samples were stored at –20°C for subsequent analyses.

Polymerase Chain Reaction-Denaturing Gradient Gel Electrophoresis Analysis

Microbial profiling was performed by using denaturing gradient-gel electrophoresis (DGGE), PCR-based technique to determine major shifts in microbial composition. Polymerase chain reaction procedures were performed on the extracted DNA using bacterial-specific primers to the conserved regions flanking the variable V3 region of 16s ribosomal DNA (rDNA). Each PCR mixture contained 20 pmol of primers PRBA338fGC (5′CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGG143 ACTCCTACGGGAGGCAGCAG′3) and PRUN518r (5′-ATTACCGCGGCTGCTGG-3′; Ovreas et al., 1997).

Denaturing gradient gel electrophoresis was performed as described by Walter et al. (2000) using a DCode universal mutation detection system (Bio-Rad, Hercules, CA). Visualization of the DNA bands in the DGGE gels was performed by standard ethidium bromide staining and photographed using the InGenius gel documentation system (Syngene, Frederick, MD). The DGGE images were analyzed using bionumerics software (BioNumerics version 5.0; Applied Maths, Kortrijk, Belgium). Distance matrices were generated by manually assigning bands and normalizing banding patterns used to generate distance matrices. Matrices were used to calculate the Pearson product moment correlation coefficient for all pairwise combinations of patterns. Pairwise combinations compare profiles based on the entire densitometric curve, accounting for both band position and intensity. Using the BioNumerics software, the DGGE fingerprints were transformed to peak profiles and intensities of individual bands were determined as a percent peak surface area relative to the surface area of the entire molecular fingerprint of the sample. To determine similarity of the gut microbiota within parity, individual piglet DGGE profiles were compared by Pearson’s pairwise comparison, thus obtaining similarity coefficients. Biodiversity of the fecal microbial populations in progeny derived from P1 vs. P4 dams were determined by analyzing the DGGE profiles with Shannon’s and Simpson’s ecological indexes of diversity as described by Scanlan et al. (2006). Briefly, Shannon’s diversity index was calculated using the formula shown below,in which pi represents the proportions of a species i present in a sample (determined as the proportion of the band intensity with respect to the intensity of the entire fingerprint) of n different species (number of bands in the profile). Simpson’s diversity index was calculated with this formula in which ni represents the number of organisms belonging to species i (determined as proportion of the band intensity with respect to the intensity of the entire fingerprint) and N, the total number of organisms in the microbial population.

Statistical Analysis

All data were analyzed as a randomized complete design (Cochran and Cox, 1992). Each dam and litter within a parity was considered an experimental unit. The model for litter performance data included parity in the model with dam nested in parity included in a random statement. Piglet BW was analyzed using day as a repeated measure with parity, day, and their interaction included in the model, including dam nested in parity × day. Similarity and diversity microbial indexes included parity using dam as a random effect. Differences by day were not included for microbial indexes as DGGE was run by day and severe gel bias would distort a day effect. Sow serum and milk analysis included parity, day, and their interaction in the model using day as a repeated measure. The model for progeny immunoglobulins (IgA and IgG) included parity, day, and their interaction with dam nested in parity × day included as a random statement and day considered a repeated measure. Analysis of variance was performed for all analyses using the MIXED procedure (SAS Inst. Inc., Cary, NC). Least square means were calculated for each independent variable.


RESULTS

The effects of dam parity on litter and pig measurements are presented in Table 1. There were no effects of dam parity on number (pigs/litter) of total born, born live, stillbirths, mummified fetuses, prewean mortality, or the number of pigs weaned. However, significant effects of dam parity were observed with respect to average litter BW and average piglet BW. Specifically, average litter BW was decreased in P1 litters at d 7 (25.7 vs. 30.0 kg; P < 0.03) and tended to be decreased at d 14 (44.4 vs. 51.5 kg; P < 0.06) compared with P4 litters. In addition, P1 progeny had decreased (P < 0.01) average piglet BW at birth (d 0; 1.32 vs. 1.56 kg), d 7 (2.53 vs. 3.00), d 14 (4.36 vs. 5.08), and at weaning (d 19; 5.39 vs. 6.06) compared with P4 progeny.


View Full Table | Close Full ViewTable 1.

The effect of dam parity (parity 1 vs. parity 4) on litter and pig measurements

 
Parity
Item 1 SEM 4 SEM P-value
No. of sows 16 24
No. of pigs per litter
    Total born 12.79 0.86 12.79 0.77 0.999
    Born live 12.00 0.84 11.50 0.74 0.660
    Stillbirths 0.63 0.23 1.13 0.21 0.125
    Mummified fetuses 0.16 0.09 0.21 0.08 0.682
    Mortality (preweaning) 2.68 0.56 1.83 0.50 0.260
    Weaned 10.16 0.44 10.13 0.39 0.956
Avg. piglet BW, kg
    Birth (d 0) 1.32 0.04 1.56 0.04 <0.001
    d 7 2.53 0.11 3.00 0.09 0.002
    d 14 4.36 0.13 5.08 0.12 <0.001
    Weaning (d 19) 5.39 0.17 6.06 0.15 0.005
    BW gain (0 to 19 d) 4.10 0.16 4.44 0.15 0.107

Circulating concentrations of IgG and IgA in P1 and P4 dams during gestation (d 90 and 114) and at parturition (d 0) are presented in Fig. 1. No parity × time interaction was observed and there were no main effects of dam parity on circulating IgG or IgA in dams. Circulating serum IgG concentrations decreased (P < 0.05) from d 90 to d 114 of gestation with no statistical difference between d 114 of gestation and d 0 of lactation. Circulating IgA concentrations were not affected by day.

Figure 1.
Figure 1.

Circulating concentrations of IgG (top panel) and IgA (bottom panel) in parity 1 (P1) and parity 4 (P4) dams. Immunoglobulin concentrations were evaluated in serum obtained at d 90 and 114 of gestation and immediately after parturition (d 0). Each bar represents the least-squares mean (±SEM) of 19 and 24 observations for P1 and P4 dams, respectively. Bars with different letters (a,b) differed (P < 0.05)

 

Concentrations of IgG and IgA in colostrum/milk samples obtained during lactation (d 0, 7, and 14) are represented in Fig. 2. No parity × time interactions were observed for IgG or IgA with respect to colostrum/milk sample analyses. Concentrations of IgA tended to be greater (P = 0.09; 7.98 vs. 5.08 mg/mL) in P4 compared with P1 dams. In addition, both IgG (P < 0.001) and IgA (P < 0.0001) concentrations were greatest on d 0 and decreased on d 7 and 14 of lactation.

Figure 2.
Figure 2.

Concentrations of IgG (top panel) and IgA (bottom panel) in d 0 (colostrum), d 7, and d 14 milk samples obtained from parity 1 (P1) and parity 4 (P4) dams. Each bar represents the least-squares mean (±SEM) of 19 and 24 observations for P1 and P4 dams, respectively. Bars with different letters (a,b) differed (P < 0.05)

 

Circulating concentrations of IgG and IgA in serum obtained from the progeny of P1 and P4 dams are presented in Fig. 3. No significant parity × time interactions were observed for either circulating IgG or IgA concentrations in P1 or P4 progeny. Serum IgG and IgA concentrations were not affected by dam parity. Both IgG (P < 0.0001) and IgA (P < 0.0001) concentrations in serum samples were greatest at d 1. Concentrations of IgG and IgA on d 7 were decreased from d 1 but similar to concentrations on d 14.

Figure 3.
Figure 3.

Circulating concentrations of IgG (top panel) and IgA (bottom panel) in serum obtained from the progeny of sows (First parity and fourth parity; P1 and P4, respectively). Immunoglobulin concentrations were evaluated in serum obtained at 1, 7, and 14 d postpartum. Each bar represents the least-squares mean (±SEM) of the progeny of 19 and 24 observations for P1 and P4 dams, respectively. Bars with different letters (a,b) differed (P < 0.05)

 

The results of the analyses from fecal microbial profiling are presented in Table 2 (similarity indexes) and Table 3 (diversity indexes). A representative dendogram derived from DGGE is presented in Fig. 4. No effect of dam parity was observed on d 1 for microbial similarity (Table 2) but increased (P < 0.001; 52.4 vs. 34.1) on d 7 and decreased (P = 0.03; 34.8 vs. 44.2) on d 14 in P1 progeny compared with P4 progeny. Differences in microbial diversity were determined using the Shannon’s and Simpson’s indexes (Table 3). The Shannon’s index was increased (P < 0.03; 2.80 vs. 2.40) and the Simpson’s index tended (P = 0.07; 0.08 vs. 0.16) to be decreased for progeny derived from P1 compared with P4 dams on d 7.


View Full Table | Close Full ViewTable 2.

The effect of dam parity (parity 1 vs. parity 4) on similarity indexes1 of microbial populations in piglets (3 pigs per litter and 3 litters per parity)1

 
Day Parity 1 Parity 4 SEM P-value
d 1 39.64 44.22 4.33 0.46
d 7 52.44 34.13 4.40 0.01
d 14 34.75 44.23 3.04 0.03
1Similarity indexes are calculated using the BioNumerics software version 5.0 (Applied Maths, Kortrijk, Belgium). The denaturing gradient gel electrophoresis fingerprints were transformed to peak profiles. Intensities of individual bands were determined as a percent peak surface area relative to the surface area of the entire molecular fingerprint of the sample.

View Full Table | Close Full ViewTable 3.

The effect of dam parity (parity 1 vs. parity 4) on diversity indexes1 of microbial populations in piglets (3 pigs per litter and 3 litters per parity)1

 
Diversity coefficient
Day Parity 1 SEM Parity 4 SEM P-value
Shannon
d 0 1.85 0.28 1.20 0.28 0.128
d 7 2.80 0.11 2.35 0.14 0.033
d 14 2.65 0.20 2.42 0.20 0.457
Simpson
d 0 0.30 0.16 0.37 0.16 0.777
d 7 0.08 0.02 0.16 0.03 0.072
d 14 0.09 0.03 0.13 0.03 0.312
1Diversity indexes were calculated by comparing molecular fingerprints of DNA. A greater Shannon’s diversity index represents more diversity. A lower Simpson’s diversity index represents greater diversity.
Figure 4.
Figure 4.

Panel A is a representative dendrogram derived from denaturing gradient gel electrophoresis (DGGE) analysis of the fecal bacterial community of piglets derived from parity 1 (P1; n = 9 piglets; 3 piglets/dam) and parity 4 (P4; n = 9 piglets; 3 piglets/dam) dams on d 7 postfarrowing. The Unweighted Pair Group Method with Arithmetic Mean (UPGMA)-type dendograms were constructed based on the similarity matrix resulting from Pearson’s pairwise comparisons of DGGE fingerprints. The legend on the right side of the figure indicates samples obtained from individual pigs within parity. The black squares represent P1 piglets and the gray squares represent P4 piglets. The scaled bar above the figure in Panel A (left) indicates the percentage similarity coefficients.

 

DISCUSSION

It is well documented that litter and subsequent growth performance is decreased in primiparous compared with multiparous dams and their progeny (Wilson and Johnson, 1980; Mahan, 1991, 1994, 1998; Kemme et al., 1997; Averette et al., 1999; Mahan et al., 2000; Bates et al., 2003; Peters and Mahan, 2008). One hypothesis for explaining why this occurs is that health status and therefore performance is decreased in progeny from primiparous sows but improves with increasing parity. However, there is little peer-reviewed, published evidence directly evaluating the effect of dam parity on progeny health status. Therefore, our objective was to evaluate the effects of dam parity on litter performance and to evaluate (as indicators of health status) the effects of dam parity on passive transfer of immunity as well as progeny microbial ecology.

Consistent with our hypothesis, there were observed effects of dam parity on litter growth performance including decreased average piglet BW in P1 compared with P4 litters. However, contrary to our hypothesis, we did not observe differences in litter performance that may be indicative of health status (i.e., total number of pigs that died before weaning and number of pigs weaned). As mentioned previously, several studies have demonstrated decreased litter and growth performance in primiparous compared with multiparous dams. However, in many of these experiments performance has been evaluated using the same dams over successive parities whereas we compared sows farrowing at the same time. In addition, the greatest differences in performance may occur between P1 and P2 or P3 dams (Mahan, 1994, 1998; Neil, 1999). Both of these factors, in addition to the fact that low numbers of dams (19 P1 and 24 P4) were used in the current study, may contribute to the absence of additional effects of dam parity on litter performance measurements that are indicative of health status (e.g., mortality).

The pig is agammaglobulinemic at birth and totally dependent on colostral immunoglobulins for initial immune protection (Blecha, 2001) and maternal immunoglobulins reach minimal concentrations in the system of the piglet between 2 and 4 wk of age (Wagstrom et al., 2000). Although neonatal pigs are capable of mounting an immune response, the piglet does not begin to actively synthesize immunoglobulins until between 2 and 5 wk of age. Therefore, there is a potential gap in immune protection between the time that passive immunity acquired from the dam wanes and the time that active immunity begins. Colostrum plays a very important role in piglet growth and health status due to its nutritional and immunological factors (Le Dividich et al., 2005) and low IgG absorption has been correlated with piglet death (Klobasa et al., 1981).

Preliminary research evaluating the effects of dam parity on passive immunity lead us to hypothesize that passive immunity may be affected by dam parity as differences in immunoglobulin (G and A) concentrations in dams (serum and colostrum/milk) and their progeny were observed (Burkey et al., 2007). In this much larger experiment, no significant parity × time interactions was observed with respect to immunoglobulin concentrations in dams (serum, colostrum, or milk) or their progeny (serum). However, main effects of dam parity were observed with IgA concentrations in colostrum/milk samples during lactation. Our concentrations of immunoglobulin are consistent with previous experiments (Bourne, 1977; Inoue et al., 1980; Inoue, 1981a,b; Jackson et al., 1995; Rooke and Bland, 2002).

The mechanism or mechanisms by which the maternally derived antibodies afford protection against disease in neonatal pigs is unclear. It has been suggested that composition of the intestinal microflora may be linked to immune system development (Round and Mazmanian, 2009) as well as health and growth performance (Thompson et al., 2008). Studies on humans and mice have demonstrated that nonpathogenic bacteria directly influence the intestinal epithelium to limit immune activation (Hooper and Gordon, 2001). Not only that, but evidence suggests the acquisition of commensal bacteria during the postnatal period is required for developing tolerance to luminal antigens (Hooper and Gordon, 2001). Our group (Tran et al., 2011) and others (reviewed by Richards et al., 2005) have used an electrophoretic fingerprinting technique (DGGE) to evaluate GIT microbial communities as an indicator of health status in pigs.

Denaturing gradient gel electrophoresis is a technique capable of discriminating among bacterial species and possibly among strains of the same species and allows for the detection of shifts within the dominant microbial communities over time and/or after different treatments (Thompson et al., 2008). Similarity coefficients of fecal microbiota, which are obtained through pairwise comparisons, indicate how similar 1 fecal microbial population is to another microbial population (Berg, 1996; Guarner and Malagelada, 2003). Despite the term, diversity indexes are not the opposite of the similarity indexes. Diversity indexes are a measurement of how diverse the microbial population is in an individual piglet (Berg, 1996; Guarner and Malagelada, 2003). Microbial diversity indexes can be inferred from DGGE fingerprints using Shannon’s and Simpson’s indexes. The Shannon’s index is a measure of the probability that any 2 organisms will be the same phylotype; therefore, a greater Shannon’s index signifies a more diverse microbial population. The Simpson’s diversity index measures the species evenness by taking into account the number of species as well as band intensity, so a lower Simpson’s index indicates a greater microbial diversity or lower species evenness.

It has been demonstrated that multiparous sows have a greater colostrum yield (Devillers et al., 2007) compared with primiparous sows. The concentration of specific immunoglobulin varies with colostrum and milk as the concentration of IgG is greatest in colostrum whereas IgA predominates in milk (Wagstrom et al., 2000). Functionally, this is relevant because circulating immunoglobulins (IgG) in the neonate result from that which is provided for in the colostrum whereas milk provides local protection of the GIT via IgA. The tendency for increased IgA in P4 colostrum/milk in the current study may afford P4 progeny a greater level of immune protection, potentially leading to increased health status and growth performance as well as altering GIT microbiota.

In the current study, abrupt and conflicting changes were observed with respect to microbial similarity (i.e., the similarity of microbial populations in P1 piglets compared with P4 piglets). This observation indicates that the microbial populations of individual piglets experience drastic changes in fecal bacteria community composition during the first 2 wk of life. Many factors affect microbial succession, including extrinsic (e.g., environmental and dietary factors) and intrinsic (e.g., host physiology and endogenous nutrients) contributions (reviewed by Mackie et al., 1999). The process of microbial succession will eventually lead to the formation of a bacterial climax community. However, until a climax community is realized, intrinsic and extrinsic factors can lead to microbial community changes (i.e., changes in similarity, diversity, and dominant species) potentially affecting GIT development and health (Preidis and Versalovic, 2009). In addition to the changes in microbial similarity, microbial diversity and microbial richness (number of species) in the current study were generally greater in piglets derived from P1 dams compared with piglets derived from P4 dams. The relationship between microbial diversity and animal health/performance is not entirely clear. Species diversity affects a number of processes in ecological communities (Hooper et al., 2005) and increased microbial diversity has been associated with increased ecosystem stability and resistance to pathogen invasion (Konstantinov et al., 2004). In addition, previous research supports the theory that dietary antibiotics reduce microbial prevalence and diversity (Collier et al., 2003; Dibner and Richards, 2005), which may be a mode of action supporting the positive effects of antibiotics on health and growth performance.

To our knowledge, this is 1 of the first experiments designed to evaluate the combined effects of dam parity on transfer of passive immunity and gut microbial ecology. The level of passive immunity in a given population of piglets varies according to the amount of colostrum ingested and may have direct (i.e., development of active immunity) and indirect (i.e., overall health and performance) effects on progeny. For example, breast milk contains antimicrobial components that influence the microbiota and growth factors that stimulate development and maturation of the intestinal mucosa (Donovan and Odle, 1994; Wold and Hanson, 1994). In addition, it has been demonstrated that secretory IgA is important in the control of pathogenic bacteria (Kelly et al., 2005; Lomax and Calder, 2009). Therefore, the composition of sow milk may have a role in the development and succession of microbial communities. The role of the gut microbiota in health and disease is rapidly emerging (Sekirov et al., 2010). With respect to changes in bacterial populations (similarity and diversity), over time these changes could affect the functions that the microbial community supplies to the host and how the host responds to these changes. For example, changes in the microbial community could shift the production of short-chain fatty acids (i.e., butyrate) that may have an anti-inflammatory effect on the gut. Alternatively, changes in gut microbiota may affect the way in the host tolerates (immune response not initiated) or responds to (immune response initiated with resultant inflammatory events) changes in microbial populations.

Results from the current study demonstrate that litter performance, passive transfer of immunity, and progeny microbial ecology was affected by dam parity. Although there are limitations, DGGE is valuable approach in screening complex ecosystems on a large scale and may be used to gain insight with respect to overall health status. However, the impact of microbial communities on animal health likely needs to be focused on fine-scale diversity (i.e., species and strain level; Thompson et al., 2008). Therefore, further research may be warranted to determine if passive transfer of immunity and microbial ecology reflect overall health status and how the dam is affected throughout her reproductive lifetime.

 

References

Footnotes


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