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ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Cell Wall Biology and Utilization Research » Research » Publications at this Location » Publication #379722
Research Project: Investigating Microbial, Digestive, and Animal Factors to Increase Dairy Cow Performance and Nutrient Use Efficiency

Location: Cell Wall Biology and Utilization Research

Title: Big data from small cells: Metagenome assembly of ruminant microbial communities

Author
item Bickhart, Derek

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/18/2020
Publication Date: 11/18/2020
Citation: Bickhart, D.M. 2020. Big data from small cells: Metagenome assembly of ruminant microbial communities. Meeting Abstract. US Dairy Forage Research Center Webinar Series. November 18, 2020. Virtual.

Interpretive Summary:

Technical Abstract: The famous dairyman, W.D. Hoard, was paraphrased in a 1918 edition of “Hoard’s Dairyman” as saying that the inside of a dairy cow was the darkest place on earth. Over one hundred years since that quote, the microbial populations of the ruminant gastrointestinal tract remain largely unclassified and functionally mysterious. We have made many critical discoveries in the microbiology of these systems that are relevant to dairy and beef production; however, these systems are incredibly complex. To pierce this complexity and discover useful insights, we used the latest in DNA sequencing technologies to create genetic maps of microbes in the ruminant gut. These new maps grant us insights into the biology of the ruminant gut that were nearly impossible to identify previously. They also provide us the means to develop cost effective techniques to rapidly assess the rumen microbes of an individual cow. By taking advantage of the fact that they “ruminate” and chew the contents of their stomachs, we can assess a cow’s current microbial profile by taking an oral swab prior to feeding. At a cost of less than $5 per sample, it will soon be possible for us to analyze the microbial contents of an entire herd of cows in a cost-effective way. Ultimately, we hope that these discoveries will result in diagnostic tools that can be used on commercial dairies to identify poor performing or sick cows and to inform the farmer as to which treatment will be most effective for those animals. By sifting through big data from these small microbial cells, it may be possible to improve the production efficiency of cattle beyond their genetic predisposition.