Maternal Programming of the Piglet Microbiome from Birth to Weaning Kayla Law1, Brigit Lozinski1, Ivanellis Torres1, Adrienne Hillbrands1,2, Emma Nelson1,2, Lee Johnston1,2 and Andres Gomez1

1. Department of Animal Science, University of Minnesota, St Paul, MN, 2. University of Minnesota, West Central Research and Outreach Center, Morris, MN

Introduction Results

Conclusions

• Maternal programming refers to the maternal factors and perinatal interventions that can predetermine offspring growth and development.

• In humans, maternal programming profoundly affects the neonatal microbiome, with potential impacts in microbiome development and health. This concept has yet to be well explored in swine systems, especially in the context of microbiome seeding and modulation.

• The objective of this study was to determine whether sanitation level in the birth (farrowing) environment affects piglets' gut and nasal microbiomes and growth performance.

• We hypothesized that piglets born in an environment exposed to a commercial disinfectant would exhibit distinct microbiomes, lower bacterial diversity and lower weight from birth to weaning, compared to piglets born in an environment devoid of disinfection.

(a)

• Use of disinfectant in the farrowing environment significantly altered nasal and fecal bacterial diversity from birth to weaning, with disinfection linked to lower bacterial diversity at birth.

• The maternal sources seeding the piglets' microbiome had different influences depending on farrowing environment, time-point of development and anatomical site sampled (gut vs nasal).

• The lower bacterial diversity shown by piglets born in a highly disinfected environment was not associated with lower birth and weaning weights, contrary to our hypothesis.

Acknowledgements*Minnesota Supercomputing Institute (MSI) provided all computing resources for the data analyses carried out in this research

MethodsExperimental Design

Data Analysis • DNA was extracted from swabs and the V4 variable region of

16srRNA bacterial gene was sequenced on the MiSeq platform at the UMGC.

• Sequence data was analyzed using DADA2 and Qiime2 through MSI*

• Qiime2 processing requirements were 24 hours of walltime, 5 nodes, 24 ppn, and 62 gb of memory

• Statistical analyses were done using various packages within the R statistical software

• Seeding sources of piglets' microbiomes were calculated using The SourceTracker software through MSI command-line

Department of Animal ScienceCFANS-College of Food, Agriculture, and Natural Resources Sciences

(a)

Figure 5. Birth (BW) and weaning weights (WW) of D and Nde piglets. Piglets born in the (D) environment showed higher BW (p=3.773e-05) and WW (p=5.803e-06) compared with Nde piglets.

(b)d0

(b)

(c)d7

(e)d21(d)d14

(b)d0(a) (c)d7

(d)d14 (e)d21

Figure 4. Microbial seeding sources differ between D and Nde piglets across the 4 sampling points according to SourceTracker. (a-b) Fecal microbiomes for D and Nde piglets respectively. (c-d) Nasal microbiomes for the D and Nde piglets respectively. The highest proportion of piglet samples were traced to unknown origins regardless of sample type or time-point, with unknown sources significantly more influential in fecal samples from D pigs (*p=0.018).

D Nde

Maternal Source

Fecal Oral Udder Vaginal Unknown

100%

75%

50%

25%

0%

(D)BW

1 2

Shan

non'

s H

Figure 1. The microbiome of crates showed distinct patterns after disinfection or power-wash. These distinctions were seen for alpha (a) and beta diversity (b); However, they were not supported statistically: Shannon’s H, p= 0.1143 (a) and PERMANOVA, R2=0.47444, p=0.031(b).

Figure 2. The fecal microbiome of D and Nde piglets across 4 time-points differed significantly. (a) Alpha diversity between treatment groups was significant at d0 (Shannon’s H, p=9.131e-08), but not at d7-d21. b-e) Beta diversity between treatment groups was significant at all four time-points (PERMANOVA, R2=0.06-0.1295, p=0.001).

Figure 3. The nasal microbiome of D and Nde piglets across 4 time-points differed significantly. (b) Alpha diversity between treatment groups was significant at d0 (Shannon’s H, p=6.164e-08) and d21 (Shannon’s H, p=0.03557). b-e) Beta diversity between treatment groups was significant at all four time-points (PERMANOVA, R2=0.13642-0.19623, p=0.001).

Treatment Group

D

(Nde)BW

(D)WW(Nde)WW

2 2 21113 3334 4 44Time-point

*

*

3.8

3.6

2.8

3.2

3.4

3.0Shan

non'

s H

PCo.

2(21

%)

0.1

0.2

-0.1

-0.3

PCo.1(55%)-0.2 0.2 0.4

Nde

*

PCo.1(15%) PCo.1(13%)

PCo.1(15%)PCo.1(22%)

2

3

4

5

-0.2 0.20.0-0.4

PCo.

2(11

%)

-0.3

-0.1

0.1

-0.3

-0.1

0.1

0.3

PCo.

2(10

%)

-0.3 -0.1 0.1 0.3

PCo.

2(16

%)

PCo.

2(10

%)

PCo.

2(15

%)

PCo.1(21%)

0.0

0.2

0.4

-0.2

0.1

-0.1

-0.3

0.0

-0.2

0.3

0.0 0.2 0.4-0.4 -0.2 0.40.20.0

Wei

ght (

lbs)

20

15

10

5

0

* *

-0.4 -0.2 0.2

-0.3 -0.1 0.30.10.1

0.3

-0.3

-0.1

0.1

-0.1

-0.3

-0.1

5.5

Shan

non'

s H

Treatment Group

DNde

4.5

5.0

4.0

3.5

Time-point

Time-point

1 2 3 4

1 2 3 4

0.3

0.1

-0.3

0.1

-0.1

0.3

-0.3

PCo.1(22%)

PCo.

2(10

%)

PCo.

2(12

%)

PCo.

2(21

%)

PCo.1(21%)

PCo.1(24%)

-0.1

-0.3 0.1

0.3

0.2

-0.2

0.0

-0.4

Nde Nde

(a) D Piglet Fecal (b) Nde Piglet Fecal (c) D Piglet Nasal (d) Nde Piglet Nasal

*