Supplemental tables and figures for metabolic resilience in goats: Insights from nutritional challenge and milk metabolomic analysis
Abstract
SUPPLEMENTARY TABLES AND FIGURES FOR THE FOLLOWING PAPER PUBLISHED IN THE JOURNAL OF DAIRY SCENCE Resilience in goats: Insights from nutritional challenge and milk metabolomic analysis This study aimed to investigate the metabolic mechanisms underlying resilience and to identify potential biomarkers of resilience and longevity by comparing divergent functional longevity lines and phenotypic resilience groups in dairy goats. A total of 70 Alpine goats, selected for longevity and phenotypic resilience, were subjected to a two-day underfeeding challenge. Milk samples were collected before and at the end of the challenge and analyzed using untargeted metabolomics. Univariate and multivariate analyses revealed distinct milk metabolome changes between the divergent longevity lines and between phenotypic resilience groups during the challenge, while no differences were observed at pre-challenge. During the challenge, phenotype resilience groups differed in amino acid metabolism, with the low resilience group exhibiting greater reductions in branched-chain amino acids (leucine, valine, isoleucine) and tryptophan compared to the high resilience group, supporting the previously observed decrease in total amino groups under nutritional stress. During the challenge, goats from higher functional longevity lines showed greater reductions in milk C4-DC carnitine during the challenge, and this reduction was negatively correlated with their sires' estimated breeding values for functional longevity. These findings support a biological link between metabolic resilience and functional longevity, potentially through mitochondrial efficiency. These results highlight novel milk-based biomarkers that could contribute to improved selection and management strategies aimed at enhancing resilience in dairy goats.
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SUPPLEMENTARY TABLES AND FIGURES FOR METABOLIC RESILIENCE IN DAIRY GOATS: INSIGHTS FROM NUTRITIONAL CHALLENGE AND MILK UNTARGETED METABOLOMICS C. Brosse1, J. Pires1, S. Emery1, C. Migne², M. Brandolini-Bunlon², S. Durand², E. Pujos-Guillot², N. C. Friggens3,4, R. Rupp5*, M. Bonnet1* 1INRAE, UMRH, Route de Theix, 63122 Saint-Genès-Champanelle, France, ²GenPhySE - INRAE - Université de Toulouse - ENVT, 24 Chemin de Borde-Rouge, F31326 Castanet-Tolosan, France 3INRAE, PEGASE, 65 rue de Saint-Brieuc, 35042 Rennes, France, 4INRAE, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, 8 avenue Blaise Pascal, 63178 Aubière, France 5 UMR 0791 Modélisation Systémique Appliquée aux Ruminants, INRAE, AgroParisTech, Université Paris-Saclay, 75005 Paris, France *R. Rupp and M. Bonnet share senior authorship.
Interpretive Summary : Frequency of nutritional challenges is expected to increase with climate change, and dairy goats will need to manage these challenges to maintain health and productivity. We studied how goats with different longevity and resilience responded to an acute underfeeding period. Using milk metabolomics, we found that modifications of milk concentration in certain molecules in response to a nutritional challenge were linked to resilience and longevity genetic background. These results suggest that milk content in certain metabolites can reveal how well goats handle nutritional stress and may help identify resilient animals. Table S1 : Summary of sire EBV for functional longevity and the number of daughters included in the study EBV for functional longevity Number of offspring Sire 1 -269 2 Sire 2 -216 1 Sire 3 -204 7 Sire 4 -194 3 Sire 5 -186 2 Sire 6 -133 5 Sire 7 -69 4 Sire 8 -1 2 Sire 9 34 6 Sire 10 68 1 Sire 11 98 2 Sire 12 99 4 Sire 13 145 1 Sire 14 203 6 Sire 15 242 3 Sire 16 242 4 Sire 17 273 9 Sire 18 311 5 Sire 19 365 1 Sire 20 NA 1 Sire 21 NA 1 Estimated breeding values (EBV) for functional longevity and the number of offspring per sire included in the study. The EBV indicates genetic potential for longevity excluding productivity-related culling. Two sires have no available EBV data (NA).
Table S2 : Least squares means (LSmeans) and changes during a 2-day underfeeding challenge for zootechnical milk production parameters, 13 milk metabolites, and 1 enzyme in 70 Alpine goats. A Type III ANOVA was used to compare resilience groups (LOW_RES and NORM_RES), adjusting for litter size (1 vs. 2+) and experimental cohorts (3 farm/year). Measurements were taken at the start (36 ± 5 days in milk) and at the end (38 ± 5 days in milk) of the challenge (2 days with access to straw only) during morning milkings. Comparisons between LOW_RES and NORM_RES groups are presented for pre-challenge (D0) concentrations and changes during the challenge (D2–D0). Pre-challenge (D0) Challange (D2-D0) LSmeans of LOW_RES (SE ) LSmeans of NORM_RES (SE ) P-value LSmeans of LOW_RES (SE) LSmeans of NORM_RES (SE) P-value BOHB (microM) 28.58 (1.48) 27.85 (0.89) 0.67 20.69 (5.17) 17.37 (3.13) 0.58 Chol (microM) 261.14 (13.06) 258.63 (7.9) 0.87 440.70 (32.76) 366.60 (19.82) 0.05 Choline (microM) 1.32 (0.07) 1.22 (0.04) 0.19 2.35 (0.16) 2.07 (0.10) 0.14 Gal (microM) 63.08 (4.75) 66.76 (2.87) 0.50 29.23 (5.71) 31.06 (3.45) 0.78 Glu (microM) 218.62 (19.73) 220.37 (11.93) 0.94 -66.67 (18.59) -75.67 (11.24) 0.67 Glu6P (microM) 61.18 (2.26) 64.74 (1.37) 0.17 -34.26 (3.24) -20.20 (1.96) < 0.01 Glutamate (microM) 177.37 (16.67) 209.62 (10.08) 0.10 -115.74 (15.07) -110.54 (9.12) 0.76 isocitrate (microM) 181.14 (13.86) 188.41 (8.38) 0.65 82.31 (13.57) 50.78 (8.21) 0.05 LDH (UI) 9.8 (0.9) 11.54 (0.55) 0.1 48.72 (4.32) 49.54 (2.61) 0.87 Malate (microM) 82.89 (8.11) 95.73 (4.91) 0.17 -57.74 (7.65) -60.95 (4.63) 0.72 NH2 (glutamate micro eqv) 1567.52 (85.63) 1677.41 (51.79) 0.27 -439.33 (75.32) -179.24 (45.56) < 0.01 TAG (mM) 56.21 (3.17) 52.37 (1.92) 0.29 63.15 (5.09) 56.24 (3.08) 0.24 Urate (microM) 45.75 (6.33) 49.07 (3.83) 0.65 44.63 (9.54) 47.11 (5.77) 0.82 Urea (mM) 6.75 (0.28) 6.37 (0.17) 0.24 -1.90 (0.32) -1.01 (0.20) 0.02 MY (kg/d) 3.04 (0.12) 2.91 (0.07) 0.34 -1.81 (0.10) -1.63 (0.06) 0.13 MFC (g/kg) 44.54 (1.21) 42.01 (0.73) 0.07 39.78 (1.95) 34.69 (1.18) 0.03 MFP (g/kg) 32.66 (0.52) 33.39 (0.32) 0.23 3.75 (0.73) 1.20 (0.44) < 0.01 SCS 5.16 (0.39) 4.88 (0.24) 0.52 1.55 (0.53) 0.13 (0.32) 0.02 Glucose-6-phosphate (Glu6P), glucose (Glu), galactose (Gal), β-hydroxy-butyrate (BOHB), isocitrate, glutamate, NH2, urea, Cholinee, malate, urate, triacylglycerols (TAG), cholesterol (Chol) and lactate dehydrogenase enzyme (LDH). Milk performance: daily milk yield (MY), ratio of fat content to protein content (F:P ratio), fat content (MFC), protein content (MPC), somatic cells score (SCS).
Table S3 : Least squares means (LSmeans) and changes during a 2-day underfeeding challenge for zootechnical milk production parameters, 13 milk metabolites, and 1 enzyme in 138 Alpine goats. A Type III ANOVA was used to compare resilience groups (LOW_RES and NORM_RES), adjusting for litter size (1 vs. 2+) and experimental cohorts (3 farm/year). Measurements were taken at the start (36 ± 5 days in milk) and at the end (38 ± 5 days in milk) of the challenge (2 days with access to straw only) during morning milkings. Comparisons between LOW_RES and NORM_RES groups are presented for pre-challenge (D0) concentrations and challenge-induced changes (D2–D0). Table S4 : Correlation between sire EBV for functional longevity (n = 19) and offspring metabolite levels (n = 68) at pre-challenge and their changes during a nutritional challenge, weighted by number of offspring. Metabolite annotations were obtained via MS/MS analysis. Displayed correlations have raw p-values < 0.05; FDR corresponds to BH-corrected p-values. Pre-challenge (D0) Challange (D2-D0) LSmeans of LOW_RES (SE ) LSmeans of NORM_RES (SE ) P-value LSmeans of LOW_RES (SE) LSmeans of NORM_RES (SE) P-value BHB (microM) 28,58 (1,02) 27,85 (0,62) 0,54 20,69 (3,59) 17,37 (2,17) 0,42 Chol (microM) 261,14 (9,06) 258,63 (5,48) 0,81 440,7 (22,73) 366,6 (13,75) 0,01 Cholinee (microM) 1,32 (0,05) 1,22 (0,03) 0,06 2,35 (0,11) 2,07 (0,07) 0,03 Gal (microM) 20,99 (2,61) 23,59 (1,58) 0,39 3,27 (1,85) 0,09 (1,12) 0,14 Glu (microM) 95,31 (9,05) 82,47 (5,47) 0,22 -40,24 (9,55) -33,24 (5,78) 0,52 Glu6P (microM) 20,06 (1,2) 23,57 (0,72) 0,01 -10,69 (1,9) -4,16 (1,15) < 0.01 Glutamate (microM) 177,37 (11,56) 209,62 (6,99) 0,02 -115,74 (10,46) -110,54 (6,33) 0,67 isocitrate (microM) 181,14 (9,61) 188,41 (5,82) 0,51 82,31 (9,42) 50,78 (5,7) < 0.01 LDH (UI) 9,8 (0,63) 11,54 (0,38) 0,02 48,72 (2,99) 49,54 (1,81) 0,81 Malate (microM) 82,89 (5,63) 95,73 (3,4) 0,05 -57,74 (5,31) -60,95 (3,21) 0,6 NH2 (glutamate micro eqv) 1567,52 (59,4) 1677,41 (35,93) 0,11 -439,33 (52,25) -179,24 (31,6) < 0.01 TAG (mM) 56,21 (2,2) 52,37 (1,33) 0,13 63,15 (3,53) 56,24 (2,14) 0,09 Urate (microM) 45,75 (4,39) 49,07 (2,66) 0,51 44,63 (6,62) 47,11 (4) 0,74 Urea (mM) 6,75 (0,19) 6,37 (0,12) 0,09 -1,9 (0,23) -1,01 (0,14) < 0.01 Mass_RT_Formula Annotation Pre-challenge/Chalenge (Tendancy*) Corr w/EBV stdErr P_Value FDR_P_Value M123.0554T1.19 Nicotinamide Change (Decrease) -0.49 0.21 0.03 0.42 M146.1652T0.71 Spermidine Pre-challenge 0.53 0.20 0.02 0.65 M146.1652T0.71 Spermidine Change (Increase) -0.50 0.21 0.03 0.41 M218.1389T3.16 Propionylcarnitine Change (Decrease) -0.40 0.22 0.09 0.53 M262.1286T1.19_1 C4-DC carnitine Change (Decrease) -0.76 0.16 < 0.00 0.01 M480.3088T15.44 PE(0:0/18:1(11Z)) Pre-challenge 0.43 0.22 0.06 0.65 M480.3088T15.44 PE(0:0/18:1(11Z)) Change (Increase) -0.46 0.21 0.04 0.41 *"Tendency" indicates the mean trend during the challenge.
Figure 1 : Correlation between sire EBV for functional longevity (n = 19) and offspring metabolite levels (n = 68) at pre-challenge (D0) and their changes (logFC) during a nutritional challenge, weighted by the number of offspring. Metabolites are described by their mass and retention time obtained via LC-MS, and whether they correspond to baseline (D0) or change (logFC), as indicated by the labels M_XXT_XX_D0 or M_XXT_XX_LogFC. All displayed metabolites show significant or suggestive correlations after FDR correction (FDR < 0.10). The correlation coefficient (r) represents the weighted Spearman correlation computed using the weighted R package.