Interrelations between the rumen microbiota and production, behavioral, rumen fermentation, metabolic, and immunological attributes of dairy cows.

Discipline: fermentation/digestion; Key words: rumen microbiota, feed efficiency, behavior, PCA

The cow as a ruminant, has a symbiotic relationship with her rumen microbes. In the past it was accepted that the microbial composition is largely determined by the feed composition, as well as the feed intake pattern of the host (cow). The exception was the rumen protozoal population which was to some extent host specific. With the advent of DNA fingerprinting and sequencing techniques, the specificity was also found for some  other rumen organisms. The current understanding of the rumen microbial dynamics is that the rumen microbes consist of core and variable groups. The core group is similar across a wide geographical range and consists of different genera that increase or decrease in their abundance according to the diet fed. They therefore constitute a key element in the survival of ruminants by allowing fast and appropriate adaptation to new diets. In contrast, the variable or individual microbes are a result of animal variation in behavioural and genetic attributes, as well as environmental influences. Being the case, different studies have shown interrelations between animal production variables, such as feed efficiency, milk production and composition, and the rumen microbial population. The underlying dynamics between cause and effect is, however, still not clear. Therefore, the aim of the study of Dr M. Schären and colleagues was to investigate the associations between the rumen microbial population and a large set of variables describing the production, as well as the metabolic and immunological state of dairy cows in early lactation, plus behavioural attributes, in an attempt to describe possible functional interrelations and pathways. Their study was published in the Journal of  Dairy Science, Volume 101 of 2018, page 4615 to 4637; the title being: Interrelations between the rumen microbiota and production, behavioral, rumen fermentation, metabolic, and immunological attributes of dairy cows.

The authors endeavoured to describe the possible functional interrelations and pathways by  using a large set of data describing the production, the metabolic and immunological state, as well as the rumen microbial population and fermentation characteristics of dairy cows in early lactation. They hypothesized that the feed intake-associated behaviour may influence the ruminal fermentation pattern, and a set of variables describing these individual animal attributes was included. Principal component analysis as well as Spearman’s rank correlations were conducted, using a total of 265 variables. The attained plots describe several well-known associations between metabolic, immunological and production traits.

The main drivers of variance within the data set included milk production and efficiency as well as rumen fermentation and microbial diversity attributes, whereas behavioural, metabolic and immunological variables did not exhibit any strong interrelations with the other variables. The previously well-documented strong correlation of production traits with distinct types was confirmed. This mainly included a negative correlation of operational taxonomic units ascribed to the Prevotella genus with milk and fat yield and feed efficiency. However, a central role for the animals’ feed intake behaviour in this context by influencing the ruminal fermentation pattern could not be confirmed, which suggests that another undescribed driving force causes the distinct differences in the rumen microbial population between efficient and inefficient animals.                                                                                             

It was concluded that, to further investigate this driving force, studies including more sophisticated methods to describe phenotypical traits of the host (e.g., rumen physiology, metabolic and genetic aspects) as well as the rumen microbial population (e.g., metagenome, metatranscriptome, metaproteome and metabolome analysis) are needed.