Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments.

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment-which has low overall diversity and productivity despite high variation in each-belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River-where plant diversity and productivity were consistently higher-belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.

Selective microbial genomic DNA isolation using restriction endonucleases.

To improve the metagenomic analysis of complex microbiomes, we have repurposed restriction endonucleases as methyl specific DNA binding proteins. As an example, we use DpnI immobilized on magnetic beads. The ten minute extraction technique allows specific binding of genomes containing the DpnI Gm6ATC motif common in the genomic DNA of many bacteria including γ-proteobacteria. Using synthetic genome mixtures, we demonstrate 80% recovery of Escherichia coli genomic DNA even when only femtogram quantities are spiked into 10 µg of human DNA background. Binding is very specific with less than 0.5% of human DNA bound. Next Generation Sequencing of input and enriched synthetic mixtures results in over 100-fold enrichment of target genomes relative to human and plant DNA. We also show comparable enrichment when sequencing complex microbiomes such as those from creek water and human saliva. The technique can be broadened to other restriction enzymes allowing for the selective enrichment of trace and unculturable organisms from complex microbiomes and the stratification of organisms according to restriction enzyme enrichment.

Circulating tumor cells are transcriptionally similar to the primary tumor in a murine prostate model.

The abundance of circulating tumor cells (CTC) indicates patient prognosis. Molecular characterization of CTCs may add additional information about a patient's disease. However, currently available methods are limited by contamination with blood cells. We describe a study using a modified CTC-chip to capture CTCs from an orthotopic xenograft model. Using laser capture microscopy to collect CTCs from the chip, we compared transcripts from purified CTCs with those from primary and metastatic tissue. Transcriptional profiles showed strong concordance among primary, metastatic, and CTC sources. Moreover, cells captured on the chip were viable and could be expanded in culture. We conclude that the CTC-chip is a useful tool to further characterize animal models of cancer and that viable CTCs can be isolated and show transcriptional similarity to solid tumors.