(161) Differential Metabolic Programming in Small for Gestational Age (SGA) and Non-SGA Lambs Born to Naturally Nutrient Restricted Ewes in Chilean Patagonia

Camila Sandoval¹; Pablo Alarcón², Francisco Sales¹, Matías Araya³, Rafael Burgos², César Ulloa-Leal⁴; Marcelo Ratto², M. Carey Satterfield⁵

1. INIA Kampenaike, Punta Arenas, Chile.

2. Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Valdivia, Chile.

3. Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile.

4. Escuela de Ciencias Agrícolas y Veterinarias, Universidad de Viña del Mar, Viña del Mar, Chile.

5. Department of Animal Science, Texas A&M University, College Station, TX, USA.

Abstract Text: Maternal nutrient restriction (NR) occurs in Patagonian sheep herds due to poor pasture quality , leading to small for gestational age (SGA) offspring. However, there are NR ewes producing lambs whose birth-weight is unaffected (Non-SGA). Being born as SGA or Non-SGA is a result of differential prenatal programming in response to NR. However, it is unknown if that is reflected on different metabolic signatures that may be the basis for divergent postnatal outcomes in the offspring. Our objective was to evaluate  metabolic signatures associated with the SGA and Non-SGA phenotypes in offspring born to ewes under NR. Experiments were ethically approved (CICUA 01/22). Eighty-six singletone-bearing ewes of similar weight, age, and body condition score were assigned to either a NR (n=68) or Control (CN, n=18) groups. Both groups grazed on natural pasture, representative of Patagonia, throughout the pregnancy (Crude Protein (CP) 6.1%, Metabolizable Energy 1.6 Mcal/Kg, stocking rate 0.9 ewes/hectare; dry matter 525 kg/hectare). The CN group additionally received concentrate supplementation (Suralim®, CP 22%, EM 2.5 Mcal/Kg) to meet their CP requirements according to NRC, from gestational day 70 to term. At parturition, animals were monitored under a 24/7 regime. Immediately after delivery, offspring birth weight (BW, Kg) and biometric measures (Crown-rump length (CRL, cm), Femur length (FL, cm), and biparietal diameter (BD, cm)) were recorded. Blood samples were collected from lambs by jugular venipuncture before colostrum consumption. Lambs born to NR ewes were segregated into quartiles based on BW. Lambs within the upper and lower quartiles formed the Non-SGA (n=17) and SGA (n=17, kg) groups respectively. Lambs in the CN group (n=18, kg) were not separated into quartiles. Data for BW, CRL, FL and BD were evaluated for normality and analyzed via ANOVA. Eight serum samples per group were randomly selected for GC-MS Untargeted Metabolomics. Data was analyzed using MetaboAnalyst v4.0 (https://www.metaboanalyst.ca; Xia Lab, McGill University, Canada) via Principal component analysis (PCA), and validated through PERMANOVA test with F-value < 5.34, R² < 0.348, and p-value < 0.05. Metabolites with significantly different levels (P< 0.05, Mann–Whitney test) were included in pathway topology analyses using the Ovis aries library. Overrepresentation analyses were performed through hypergeometric test. Potential metabolomic pathways were identified with the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg) and the Bovine Metabolome Database (https://www.cowmetdb.ca). Offspring BW differed between CN, Non-SGA, and SGA groups (4.95±0.09^a, 5.31±0.10^b, 3.79±0.10^c, P< 0.0001). The SGA group had smaller CRL (45.30±0.69^a, P< 0.05) than CN (47.60±0.59^b) while Non-SGA was intermediate (46.78±0.67^ab). Femur Length and BD were smaller (P< 0.001) in SGA (10.14±0.4^a ; 5.52±0.41^a) than Non-SGA ( 11.32±0.5^b ; 6.02±0.37^b) and CN (11.46±0.82^b; 5.9±0.34^b) groups respectively. The PCA model was not validated (P >0.05) for the metabolomic comparison between the CN and Non-SGA groups, indicating that the different metabolites seen between groups (galactose metabolism)are result of a random effect. The CN and SGA groups differed (P< 0.01) with galactose and glycerolipid metabolism being the most relevant pathways. There were  differences (P< 0.05) between Non-SGA and SGA, primarily driven by glycerolipid, and glycine, serine and threonine metabolism pathways. Collectively, our results indicate that regardless of being subject to the same prenatal NR, the SGA and Non-SGA phenotypes differ in physical characteristics, but also show different metabolic profiles at birth that may be the basis for a long-lasting programming effect. Fondecyt 11220188. Fondequip EQM130257.