Endophyte Microbiome Diversity in Micropropagated Atriplex canescens and Atriplex torreyi var griffithsii

Microbial diversity associated with micropropagated Atriplex species was assessed using microscopy, isolate culturing, and sequencing. Light, electron, and confocal microscopy revealed microbial cells in aseptically regenerated leaves and roots. Clone libraries and tag-encoded FLX amplicon pyrosequencing (TEFAP) analysis amplified sequences from callus homologous to diverse fungal and bacterial taxa. Culturing isolated some seed borne endophyte taxa which could be readily propagated apart from the host. Microbial cells were observed within biofilm-like residues associated with plant cell surfaces and intercellular spaces. Various universal primers amplified both plant and microbial sequences, with different primers revealing different patterns of fungal diversity. Bacterial and fungal TEFAP followed by alignment with sequences from curated databases revealed 7 bacterial and 17 ascomycete taxa in A. canescens, and 5 bacterial taxa in A. torreyi. Additional diversity was observed among isolates and clone libraries. Micropropagated Atriplex retains a complex, intimately associated microbiome which includes diverse strains well poised to interact in manners that influence host physiology. Microbiome analysis was facilitated by high throughput sequencing methods, but primer biases continue to limit recovery of diverse sequences from even moderately complex communities

Selective progressive response of soil microbial community to wild oat roots

Roots moving through soil enact physical and chemical changes that differentiate rhizosphere from bulk soil, and the effects of these changes on soil microorganisms have long been a topic of interest. Use of a high-density 16S rRNA microarray (PhyloChip) for bacterial and archaeal community analysis has allowed definition of the populations that respond to the root within the complex grassland soil community; this research accompanies previously reported compositional changes, including increases in chitinase and protease specific activity, cell numbers and quorum sensing signal. PhyloChip results showed a significant change in 7% of the total rhizosphere microbial community (147 of 1917 taxa); the 7% response value was confirmed by16S rRNA T-RFLP analysis. This PhyloChip-defined dynamic subset was comprised of taxa in 17 of the 44 phyla detected in all soil samples. Expected rhizosphere-competent phyla, such as Proteobacteria and Firmicutes, were well represented, as were less-well-documented rhizosphere colonizers including Actinobacteria, Verrucomicrobia and Nitrospira. Richness of Bacteroidetes and Actinobacteria decreased in soil near the root tip compared to bulk soil, but then increased in older root zones. Quantitative PCR revealed {beta}-Proteobacteria and Actinobacteria present at about 10{sup 8} copies of 16S rRNA genes g{sup -1} soil, with Nitrospira having about 10{sup 5} copies g{sup -1} soil. This report demonstrates that changes in a relatively small subset of the soil microbial community are sufficient to produce substantial changes in function in progressively more mature rhizosphere zones

Experimental demonstration of chaotic instability in biological nitrification

Biological nitrification (that is, NH3 -> NO2- -> NO3-) is a key reaction in the global nitrogen cycle (N-cycle); however, it is also known anecdotally to be unpredictable and sometimes fails inexplicably. Understanding the basis of unpredictability in nitrification is critical because the loss or impairment of this function might influence the balance of nitrogen in the environment and also has biotechnological implications. One explanation for unpredictability is the presence of chaotic behavior; however, proving such behavior from experimental data is not trivial, especially in a complex microbial community. Here, we show that chaotic behavior is central to stability in nitrification because of a fragile mutualistic relationship between ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), the two major guilds in nitrification. Three parallel chemostats containing mixed microbial communities were fed complex media for 207 days, and nitrification performance, and abundances of AOB, NOB, total bacteria and protozoa were quantified over time. Lyapunov exponent calculations, supported by surrogate data and other tests, showed that all guilds were sensitive to initial conditions, suggesting broad chaotic behavior. However, NOB were most unstable among guilds and displayed a different general pattern of instability. Further, NOB variability was maximized when AOB were most unstable, which resulted in erratic nitrification including significant NO2- accumulation. We conclude that nitrification is prone to chaotic behavior because of a fragile AOB-NOB mutualism, which must be considered in all systems that depend on this critical reaction

Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. However, a landscape perspective is needed to understand the relative importance of local and regional factors and land management for the microbial communities and the ecosystem services they provide. In the most comprehensive analysis of spatial patterns of microbial communities to date, we investigated the distribution of functional microbial communities involved in N-cycling and of the total bacterial and crenarchaeal communities over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 × 16 km2 sampling grid. At each sampling site, the abundance of total bacteria, crenarchaea, nitrate reducers, denitrifiers- and ammonia oxidizers were estimated by quantitative PCR and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time, and soil physico-chemical properties to the spatial distribution of the different communities were analyzed by canonical variation partitioning. Our results indicate that 43–85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of microbial communities at the landscape scale. The present study highlights the potential of a spatially explicit approach for microbial ecology to identify the overarching factors driving the spatial heterogeneity of microbial communities even at the landscape scale