Comparative genomic approaches for investigating evolution in the microbial and viral populations of the rumen

The rumen is home to a diverse population of microorganisms encompassing all three domains of life: Bacteria, Archaea, and Eukarya. Viruses have also been documented to be present in large numbers; however, little is currently known about their role in the dynamics of the rumen ecosystem. This research aimed to use a comparative genomics approach in order to assess the potential evolutionary mechanisms at work in the rumen environment. We proposed to do this by first assessing the diversity and potential for horizontal gene transfer (HGT) of multiple strains of the cellulolytic rumen bacterium, Ruminococcus flavefaciens, and then by conducting a survey of rumen viral metagenome (virome) and subsequent comparison of the virome and microbiome sequences to ascertain if there was genetic information shared between these populations. We hypothesize that the bacteriophages play an integral role in the community dynamics of the rumen, as well as driving the evolution of the rumen microbiome through HGT. In our analysis of the Ruminococcus flavefaciens genomes, there were several mobile elements and clustered regularly interspaced short palindromic repeat (CRISPR) sequences detected, both of which indicate interactions with bacteriophages. The rumen virome sequences revealed a great deal of diversity in the viral populations. Additionally, the microbial and viral populations appeared to be closely associated; the dominant viral types were those that infect the dominant microbial phyla. The correlation between the distribution of taxa in the microbiome and virome sequences as well as the presence of CRISPR loci in the R. flavefaciens genomes, suggested that there is a “kill-the-winner” community dynamic between the viral and microbial populations in the rumen. Additionally, upon comparison of the rumen microbiome and rumen virome sequences, we found that there are many sequence similarities between these populations indicating a potential for phage-mediated HGT. These results suggest that the phages represent a gene pool in the rumen that could potentially contain genes that are important for adaptation and survival in the rumen environment, as well as serving as a molecular ‘fingerprint’ of the rumen ecosystem.

Development of error correction techniques for nitrate-N load estimation models

Excess nutrient loads carried by streams and rivers are a great concern for environmental resource managers. In agricultural regions, excess loads are transported downstream to receiving water bodies, potentially causing algal blooms, which could lead to numerous ecological problems. To better understand nutrient load transport, and to develop appropriate water management plans, it is important to have accurate estimates of annual nutrient loads. This study used a Monte Carlo sub-sampling method and error-corrected statistical models to estimate annual nitrate-N loads from two watersheds in central Illinois. The performance of three load estimation methods (the seven-parameter log-linear model, the ratio estimator, and the flow-weighted averaging estimator) applied at one-, two-, four-, six-, and eight-week sampling frequencies were compared. Five error correction techniques; the existing composite method, and four new error correction techniques developed in this study; were applied to each combination of sampling frequency and load estimation method. On average, the most accurate error reduction technique, (proportional rectangular) resulted in 15% and 30% more accurate load estimates when compared to the most accurate uncorrected load estimation method (ratio estimator) for the two watersheds. Using error correction methods, it is possible to design more cost-effective monitoring plans by achieving the same load estimation accuracy with fewer observations. Finally, the optimum combinations of monitoring threshold and sampling frequency that minimizes the number of samples required to achieve specified levels of accuracy in load estimation were determined. For one- to three-weeks sampling frequencies, combined threshold/fixed-interval monitoring approaches produced the best outcomes, while fixed-interval-only approaches produced the most accurate results for four- to eight-weeks sampling frequencies.