A plant's diet, surviving in a variable nutrient environment.

As primary producers, plants rely on a large aboveground surface area to collect carbon dioxide and sunlight and a large underground surface area to collect the water and mineral nutrients needed to support their growth and development. Accessibility of the essential nutrients nitrogen (N) and phosphorus (P) in the soil is affected by many factors that create a variable spatiotemporal landscape of their availability both at the local and global scale. Plants optimize uptake of the N and P available through modifications to their growth and development and engagement with microorganisms that facilitate their capture. The sensing of these nutrients, as well as the perception of overall nutrient status, shapes the plant's response to its nutrient environment, coordinating its development with microbial engagement to optimize N and P capture and regulate overall plant growth

Automated Bayesian model development for frequency detection in biological time series

<p>Abstract</p> <p>Background</p> <p>A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies.</p> <p>Results</p> <p>In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing.</p> <p>Conclusions</p> <p>Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.</p

Genetic strategies for improving crop yields.

The current trajectory for crop yields is insufficient to nourish the world's population by 20501. Greater and more consistent crop production must be achieved against a backdrop of climatic stress that limits yields, owing to shifts in pests and pathogens, precipitation, heat-waves and other weather extremes. Here we consider the potential of plant sciences to address post-Green Revolution challenges in agriculture and explore emerging strategies for enhancing sustainable crop production and resilience in a changing climate. Accelerated crop improvement must leverage naturally evolved traits and transformative engineering driven by mechanistic understanding, to yield the resilient production systems that are needed to ensure future harvests

Highly syntenic regions in the genomes of soybean, Medicago truncatula, and Arabidopsis thaliana

BACKGROUND: Recent genome sequencing enables mega-base scale comparisons between related genomes. Comparisons between animals, plants, fungi, and bacteria demonstrate extensive synteny tempered by rearrangements. Within the legume plant family, glimpses of synteny have also been observed. Characterizing syntenic relationships in legumes is important in transferring knowledge from model legumes to crops that are important sources of protein, fixed nitrogen, and health-promoting compounds. RESULTS: We have uncovered two large soybean regions exhibiting synteny with M. truncatula and with a network of segmentally duplicated regions in Arabidopsis. In all, syntenic regions comprise over 500 predicted genes spanning 3 Mb. Up to 75% of soybean genes are colinear with M. truncatula, including one region in which 33 of 35 soybean predicted genes with database support are colinear to M. truncatula. In some regions, 60% of soybean genes share colinearity with a network of A. thaliana duplications. One region is especially interesting because this 500 kbp segment of soybean is syntenic to two paralogous regions in M. truncatula on different chromosomes. Phylogenetic analysis of individual genes within these regions demonstrates that one is orthologous to the soybean region, with which it also shows substantially denser synteny and significantly lower levels of synonymous nucleotide substitutions. The other M. truncatula region is inferred to be paralogous, presumably resulting from a duplication event preceding speciation. CONCLUSION: The presence of well-defined M. truncatula segments showing orthologous and paralogous relationships with soybean allows us to explore the evolution of contiguous genomic regions in the context of ancient genome duplication and speciation events

A combination of chitooligosaccharide and lipochitooligosaccharide recognition promotes arbuscular mycorrhizal associations in Medicago truncatula.

Plants associate with beneficial arbuscular mycorrhizal fungi facilitating nutrient acquisition. Arbuscular mycorrhizal fungi produce chitooligosaccharides (COs) and lipo-chitooligosaccharides (LCOs), that promote symbiosis signalling with resultant oscillations in nuclear-associated calcium. The activation of symbiosis signalling must be balanced with activation of immunity signalling, which in fungal interactions is promoted by COs resulting from the chitinaceous fungal cell wall. Here we demonstrate that COs ranging from CO4-CO8 can induce symbiosis signalling in Medicago truncatula. CO perception is a function of the receptor-like kinases MtCERK1 and LYR4, that activate both immunity and symbiosis signalling. A combination of LCOs and COs act synergistically to enhance symbiosis signalling and suppress immunity signalling and receptors involved in both CO and LCO perception are necessary for mycorrhizal establishment. We conclude that LCOs, when present in a mix with COs, drive a symbiotic outcome and this mix of signals is essential for arbuscular mycorrhizal establishment

Rhizopine biosensors for plant-dependent control of bacterial gene expression

Engineering signalling between plants and microbes could be exploited to establish host-specificity between plant-growth-promoting bacteria and target crops in the environment. We previously engineered rhizopine-signalling circuitry facilitating exclusive signalling between rhizopine-producing (RhiP) plants and model bacterial strains. Here, we conduct an in-depth analysis of rhizopine-inducible expression in bacteria. We characterize two rhizopine-inducible promoters and explore the bacterial host-range of rhizopine biosensor plasmids. By tuning the expression of rhizopine uptake genes, we also construct a new biosensor plasmid pSIR05 that has minimal impact on host cell growth in vitro and exhibits markedly improved stability of expression in situ on RhiP barley roots compared to the previously described biosensor plasmid pSIR02. We demonstrate that a sub-population of Azorhizobium caulinodans cells carrying pSIR05 can sense rhizopine and activate gene expression when colonizing RhiP barley roots. However, these bacteria were mildly defective for colonization of RhiP barley roots compared to the wild-type parent strain. This work provides advancement towards establishing more robust plant-dependent control of bacterial gene expression and highlights the key challenges remaining to achieve this goal

Efficiently recording and processing data from arbuscular mycorrhizal colonization assays using AMScorer and AMReader.

Arbuscular mycorrhizal (AM) fungi engage with land plants in a widespread, mutualistic endosymbiosis which provides their hosts with increased access to nutrients and enhanced biotic and abiotic stress resistance. The potential for reducing fertiliser use and improving crop resilience has resulted in rapidly increasing scientific interest. Microscopic quantification of the level of AM colonization is of fundamental importance to this research, however the methods for recording and processing these data are time-consuming and tedious. In order to streamline these processes, we have developed AMScorer, an easy-to-use Excel spreadsheet, which enables the user to record data rapidly during from microscopy-based assays, and instantly performs the subsequent data processing steps. In our hands, AMScorer has more than halved the time required for data collection compared to paper-based methods. Subsequently, we developed AMReader, a user-friendly R package, which enables easy visualization and statistical analyses of data from AMScorer. These tools require only limited skills in Excel and R, and can accelerate research into AM symbioses, help researchers with variable resources to conduct research, and facilitate the storage and sharing of data from AM colonization assays. They are available for download at https://github.com/EJarrattBarnham/AMReader, along with an extensive user manual

Nodulation and nitrogen fixation in Medicago truncatula strongly alters the abundance of its root microbiota and subtly affects its structure.

Funder: University of Massachusetts Amherst; Id: http://dx.doi.org/10.13039/100008975The plant common symbiosis signalling (SYM) pathway has shared function between interactions with rhizobia and arbuscular mycorrhizal fungi, the two most important symbiotic interactions between plants and microorganisms that are crucial in plant and agricultural yields. Here, we determine the role of the plant SYM pathway in the structure and abundance of the microbiota in the model legume Medicago truncatula and whether this is controlled by the nitrogen or phosphorus status of the plant. We show that SYM mutants (dmi3) differ substantially from the wild type (WT) in the absolute abundance of the root microbiota, especially under nitrogen limitation. Changes in the structure of the microbiota were less pronounced and depended on both plant genotype and nutrient status. Thus, the SYM pathway has a major impact on microbial abundance in M. truncatula and also subtly alters the composition of the microbiota