Genomic and genetic analyses of diversity and plant interactions of Pseudomonas fluorescens

Background: Pseudomonas fluorescens are common soil bacteria that can improve plant health through nutrient cycling, pathogen antagonism and induction of plant defenses. The genome sequences of strains SBW25 and Pf0-1 were determined and compared to each other and with P. fluorescens Pf-5. A functional genomic in vivo expression technology (IVET) screen provided insight into genes used by P. fluorescens in its natural environment and an improved understanding of the ecological significance of diversity within this species. Results: Comparisons of three P. fluorescens genomes (SBW25, Pf0-1, Pf-5) revealed considerable divergence: 61% of genes are shared, the majority located near the replication origin. Phylogenetic and average amino acid identity analyses showed a low overall relationship. A functional screen of SBW25 defined 125 plant-induced genes including a range of functions specific to the plant environment. Orthologues of 83 of these exist in Pf0-1 and Pf-5, with 73 shared by both strains. The P. fluorescens genomes carry numerous complex repetitive DNA sequences, some resembling Miniature Inverted-repeat Transposable Elements (MITEs). In SBW25, repeat density and distribution revealed 'repeat deserts' lacking repeats, covering approximately 40% of the genome. Conclusions: P. fluorescens genomes are highly diverse. Strain-specific regions around the replication terminus suggest genome compartmentalization. The genomic heterogeneity among the three strains is reminiscent of a species complex rather than a single species. That 42% of plant-inducible genes were not shared by all strains reinforces this conclusion and shows that ecological success requires specialized and core functions. The diversity also indicates the significant size of genetic information within the Pseudomonas pan genome.

A comparison of the Thlaspi caerulescens and Thlaspi arvense shoot transcriptomes

Whole-genome transcriptome profiling is revealing how biological systems are regulated at the transcriptional level. This study reports the development of a robust method to profile and compare the transcriptomes of two nonmodel plant species, Thlaspi caerulescens, a zinc (Zn) hyperaccumulator, and Thlaspi arvense, a nonhyperaccumulator, using Affymetrix Arabidopsis thaliana ATH1-121501 GeneChip (R) arrays (Affymetrix, Santa Clara, CA, USA). Transcript abundance was quantified in the shoots of agar- and compost-grown plants of both species. Analyses were optimized using a genomic DNA (gDNA)-based probe-selection strategy based on the hybridization efficiency of Thlaspi gDNA with corresponding A. thaliana probes. In silico alignments of GeneChip (R) probes with Thlaspi gene sequences, and quantitative real-time PCR, confirmed the validity of this approach. Approximately 5000 genes were differentially expressed in the shoots of T. caerulescens compared with T. arvense, including genes involved in Zn transport and compartmentalization. Future functional analyses of genes identified as differentially expressed in the shoots of these closely related species will improve our understanding of the molecular mechanisms of Zn hyperaccumulation.

Assessing the sensitivities of a distributed snow model to forcing data resolution

Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.