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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #402441
Research Project: Increasing Accuracy of Genomic Prediction, Developing Algorithms, Selecting Markers, and Evaluating New Traits to Improve Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: Colostrum microbiome as a predictor of future milk solids production

Author
item JEWELL, SYDNEY - Cornell University
item KRISHNAMOORTHY, SRIKANTH - Cornell University
item Miles, Asha
item HUSON, HEATHER - Cornell University

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/6/2023
Publication Date: 6/25/2023
Citation: Jewell, S.E., Krishnamoorthy, S., Miles, A.M., Huson, H.J. 2023. Colostrum microbiome as a predictor of future milk solids production [abstract]. Journal of Dairy Science. 106(Suppl. 1):406-407(abstr. 1646W).

Interpretive Summary:

Technical Abstract: The objective of this study was to assess if colostrum microbiome populations differ among cows that go on to produce high and low concentrations of milk solids in lactation with the hope this could be used as a predictor of future production. Colostrum and 5 additional milk samples throughout lactation were collected from 275 Holstein cows from two NY commercial herds for microbiome sequencing and milk solids analysis. The average fat, protein, and total solids percent composition across these samples (excluding colostrum) was calculated for each animal. The top and bottom 25% of milk fat (HFAT and LFAT), protein (HPROT and LPROT) and total solids (HSOL and LSOL) producing cows were identified. The average milk fat composition across all cows was 4.8% with HFAT and LFAT averaging 9.3% and 1.7%, respectively. The average milk protein composition across all cows was 3.2% with HPROT and LPROT averaging 3.5% and 2.9%, respectively. The average total solids composition across all cows was 13.8% with HSOL and LSOL averaging 18.1% and 10.8%, respectively. For the microbiome analysis, metagenomic DNA was extracted from each colostrum sample and the V4 region of the 16s rRNA gene was amplified and sequenced according to Earth Microbiome Project protocols. Amplicon sequence variants (ASVs) were identified using DADA2, and taxonomic classification were performed by QIIME 2 using SILVA v.138 database at 99% similarity. Difference in ASVs between high and low production groups were noted. Regardless of group, the four dominant phyla were Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria. Alpha diversity was calculated based on Shannon diversity, Shannon evenness, Abundance-based coverage estimators, and Faith’s phylogenetic diversity indices. Significantly higher (P < 0.05) alpha diversity was observed in LFAT and LSOL relative to HFAT and HSOL, respectively. Beta diversity was estimated based on Bray-Curtis, weighted UniFrac, unweighted UniFrac, and Jaccard dissimilarity matrices. Pairwise PERMANOVA testing revealed a statistically significant difference (P < 0.05) in beta diversity between LFAT and HFAT and LSOL and HSOL groups.