Illuminating the dynamic rare biosphere of the Greenland Ice Sheet's Dark Zone.

Greenland's Dark Zone is the largest contiguous region of bare terrestrial ice in the Northern Hemisphere and microbial processes play an important role in driving its darkening and thereby amplifying melt and runoff from the ice sheet. However, the dynamics of these microbiota have not been fully identified. Here, we present joint 16S rRNA gene and 16S rRNA (cDNA) comparison of input (snow), storage (cryoconite) and output (supraglacial stream water) habitats across the Dark Zone over the melt season. We reveal that all three Dark Zone communities have a preponderance of rare taxa exhibiting high protein synthesis potential (PSP). Furthermore, taxa with high PSP represent highly connected 'bottlenecks' within community structure, consistent with their roles as metabolic hubs. Finally, low abundance-high PSP taxa affiliated with Methylobacterium within snow and stream water suggest a novel role for Methylobacterium in the carbon cycle of Greenlandic snowpacks, and importantly, the export of potentially active methylotrophs to the bed of the Greenland Ice Sheet. By comparing the dynamics of bulk and potentially active microbiota in the Dark Zone of the Greenland Ice Sheet, we provide novel insights into the mechanisms and impacts of the microbial colonization of this critical region of our melting planet.

Implementation of Genomic Prediction in Lolium perenne (L.) Breeding Populations.

Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance, and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.

Draft Genomes, Phylogenetic Reconstruction, and Comparative Genomics of Two Novel Cohabiting Bacterial Symbionts Isolated from Frankliniella occidentalis.

Obligate bacterial symbionts are widespread in many invertebrates, where they are often confined to specialized host cells and are transmitted directly from mother to progeny. Increasing numbers of these bacteria are being characterized but questions remain about their population structure and evolution. Here we take a comparative genomics approach to investigate two prominent bacterial symbionts (BFo1 and BFo2) isolated from geographically separated populations of western flower thrips, Frankliniella occidentalis. Our multifaceted approach to classifying these symbionts includes concatenated multilocus sequence analysis (MLSA) phylogenies, ribosomal multilocus sequence typing (rMLST), construction of whole-genome phylogenies, and in-depth genomic comparisons. We showed that the BFo1 genome clusters more closely to species in the genus Erwinia, and is a putative close relative to Erwinia aphidicola. BFo1 is also likely to have shared a common ancestor with Erwinia pyrifoliae/Erwinia amylovora and the nonpathogenic Erwinia tasmaniensis and genetic traits similar to Erwinia billingiae. The BFo1 genome contained virulence factors found in the genus Erwinia but represented a divergent lineage. In contrast, we showed that BFo2 belongs within the Enterobacteriales but does not group closely with any currently known bacterial species. Concatenated MLSA phylogenies indicate that it may have shared a common ancestor to the Erwinia and Pantoea genera, and based on the clustering of rMLST genes, it was most closely related to Pantoea ananatis but represented a divergent lineage. We reconstructed a core genome of a putative common ancestor of Erwinia and Pantoea and compared this with the genomes of BFo bacteria. BFo2 possessed none of the virulence determinants that were omnipresent in the Erwinia and Pantoea genera. Taken together, these data are consistent with BFo2 representing a highly novel species that maybe related to known Pantoea.