Characterization and comparison of the microbiomes and resistomes of colostrum from selectively treated dry cows.
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Professionals in animal agriculture promote prudent use of antimicrobials to address public and animal health concerns, such as reduction of antimicrobial residues and antimicrobial resistance (AMR) in products. Few studies evaluate the effect of selective dry-cow therapy on preservation of the milk microbiome or the profile of AMR genes (the resistome) present at freshening. Our objectives were to characterize and compare the microbiomes and resistomes in the colostrum of cows with low somatic cell count that were treated or not treated with intramammary cephapirin benzathine at dry-off. From a larger parent study, cows on a New York dairy farm eligible for dry-off and with histories of somatic cell counts 200,000 cells/mL were enrolled to this study (n = 307). Cows were randomly assigned to receive an intramammary antimicrobial and external teat sealant (ABXTS) or sealant only (TS) at dry-off. Composite colostrum samples taken within 4 h of freshening, and quarter milk samples taken at 1 to 7 d in milk were subjected to aerobic culture. The DNA extraction was performed on colostrum from cows with culture-negative samples (ABXTS = 43; TS = 33). The DNA from cows of the same treatment group and parity were pooled (26 pools; ABXTS = 12; TS = 14) for 16S rRNA metagenomic sequencing. Separately, the resistome was captured using a custom RNA bait library for target-enriched sequencing. Sequencing reads were aligned to taxonomic and AMR databases to characterize the microbiome and resistome, respectively. The R statistical program was used to tabulate abundances and to analyze differences in diversity measures and in composition between treatment groups. In the microbiome, the most abundant phyla were Firmicutes (68%), Proteobacteria (23%), Actinobacteria (4%), and Bacteroidetes (3%). Shannon and richness diversity means were 0.93 and 14.7 for ABXTS and 0.94 and 13.1 for TS, respectively. Using analysis of similarities (ANOSIM), overall microbiome composition was found to be similar between treatment groups at the phylum (ANOSIM R = 0.005), class (ANOSIM R = 0.04), and order (ANOSIM R = -0.04) levels. In the resistome, we identified AMR gene accessions associated with 14 unique mechanisms of resistance across 9 different drug classes in 14 samples (TS = 9, ABXTS = 5). The majority of reads aligned to gene accessions that confer resistance to aminoglycoside (TS = ABXTS each 35% abundance), tetracycline (TS = 22%, ABXTS = 54%), and -lactam classes (TS = 15%, ABXTS = 12%). Shannon diversity means for AMR class and mechanism, respectively, were 0.66 and 0.69 for TS and 0.19 and 0.19 for ABXTS. Resistome richness diversity means for class and mechanism were 3.1 and 3.4 for TS and 1.4 and 1.4 for ABXTS. Finally, resistome composition was similar between groups at the class (ANOSIM R = -0.20) and mechanism levels (ANOSIM R = 0.01). Although no critical differences were found between treatment groups regarding their microbiome or resistome composition in this study, a larger sample size, deeper sequencing, and additional methodology is needed to identify more subtle differences, such as between lower-abundance features.
