Network analysis to evaluate complexities in relationships among fermentation variables measured within continuous culture experiments.

The objective of this study was to leverage a frequentist (ELN) and Bayesian learning (BLN) network analyses to summarize quantitative associations among variables measured in 4 previously published dual-flow continuous culture fermentation experiments. Experiments were originally designed to evaluate effects of nitrate, defaunation, yeast, and/or physiological shifts associated with pH or solids passage rates on rumen conditions. Measurements from these experiments that were used as nodes within the networks included concentrations of individual volatile fatty acids, mM and nitrate, NO3-,%; outflows of non-ammonia nitrogen (NAN, g/d), bacterial N (BN, g/d), residual N (RN, g/d), and ammonia N (NH3-N, mg/dL); degradability of neutral detergent fiber (NDFd, %) and degradability of organic matter (OMd, %); dry matter intake (DMI, kg/d); urea in buffer (%); fluid passage rate (FF, L/d); total protozoa count (PZ, cells/mL); and methane production (CH4, mmol/d). A frequentist network (ELN) derived using a graphical LASSO (least absolute shrinkage and selection operator) technique with tuning parameters selected by Extended Bayesian Information Criteria (EBIC) and a BLN were constructed from these data. The illustrated associations in the ELN were unidirectional yet assisted in identifying prominent relationships within the rumen that were largely consistent with current understanding of fermentation mechanisms. Another advantage of the ELN approach was that it focused on understanding the role of individual nodes within the network. Such understanding may be critical in exploring candidates for biomarkers, indicator variables, model targets, or other measurement-focused explorations. As an example, acetate was highly central in the network suggesting it may be a strong candidate as a rumen biomarker. Alternatively, the major advantage of the BLN was its unique ability to imply causal directionality in relationships. Because the BLN identified directional, cascading relationships, this analytics approach was uniquely suited to exploring the edges within the network as a strategy to direct future work researching mechanisms of fermentation. For example, in the BLN acetate responded to treatment conditions such as the source of N used and the quantity of substrate provided, while acetate drove changes in the protozoal populations, non-NH3-N and residual N flows. In conclusion, the analyses exhibit complementary strengths in supporting inference on the connectedness and directionality of quantitative associations among fermentation variables that may be useful in driving future studies.

This study leveraged frequentist (ELN) and Bayesian networks (BLN) to evaluate the potential of network analysis to explore complex rumen environments with interconnected quantitative associations. The approaches were selected based on their capacity for holistic exploration of all possible quantitative associations among variables, including opportunities to explore the potential strength and directionality of those associations. Data from 4 continuous culture experiments, involving 18 rumen variables [major and minor volatile fatty acid (VFA), degradability variables and nitrogen related variables], were used for network derivation. Variables within a network are denoted as nodes and relationships between two nodes are referred to as edges. The different networking approaches had different strengths for biological interpretation. Although the ELN approach was useful for exploring the role and importance of specific variables in the network, the BLN had more relevance in selecting edges or relationships linking those variables. These strengths are complementary and make a case for joint exploration of datasets using both approaches. Many of the biological inferences derived from the networks are well-acknowledged within the literature, acetate, valerate, isobutyrate, and isovalerate were important nodes within both networks, and important edges focused on the driving role of N dynamics within the rumen. Overall, these analyses demonstrated potential to illustrate associations and directionality of quantitative associations among fermentation variables. These associations can be used to direct future studies based on more comprehensive datasets.

Pelleted-hay alfalfa feed increases sheep wether weight gain and rumen bacterial richness over loose-hay alfalfa feed.

Diet composed of smaller particles can improve feed intake, digestibility, and animal growth or health, but in ruminant species can reduce rumination and buffering-the loss of which may inhibit fermentation and digestibility. However, the explicit effect of particle size on the rumen microbiota remains untested, despite their crucial role in digestion. We evaluated the effects of reduced particle size on rumen microbiota by feeding long-stem (loose) alfalfa hay compared to a ground and pelleted version of the same alfalfa in yearling sheep wethers during a two-week experimental period. In situ digestibility of the pelleted diet was greater at 48 h compared with loose hay; however, distribution of residual fecal particle sizes in sheep did not differ between the dietary treatments at any time point (day 7 or 14). Both average daily gain and feed efficiency were greater for the wethers consuming the pelleted diet. Observed bacterial richness was very low at the end of the adaptation period and increased over the course of the study, suggesting the rumen bacterial community was still in flux after two weeks of adaptation. The pelleted-hay diet group had a greater increase in bacterial richness, including common fibrolytic rumen inhabitants. The pelleted diet was positively associated with several Succiniclasticum, a Prevotella, and uncultured taxa in the Ruminococcaceae and Rickenellaceae families and Bacteroidales order. Pelleting an alfalfa hay diet for sheep does shift the rumen microbiome, though the interplay of diet particle size, retention and gastrointestinal transit time, microbial fermentative and hydrolytic activity, and host growth or health is still largely unexplored.