Nodule Inception Is Not Required for Arbuscular Mycorrhizal Colonization of Medicago truncatula

Most legumes can engage in symbiosis with N-fixing bacteria called rhizobia. This symbiosis, called nodulation, evolved from the more widespread symbiosis that most land plants form with arbuscular mycorrhiza, which is reflected in a common requirement of certain genes for both these symbioses. One key nodulation gene, Nodule Inception (NIN), has been intensively studied. Mutants in NIN are unable to form nodules, which has made it difficult to identify downstream genes under the control of NIN. The analysis of data from our recent transcriptomics study revealed that some genes with an altered expression of nin during nodulation are upregulated in mycorrhizal roots. In addition, another study reported the decreased colonization of nin roots by arbuscular mycorrhiza. We therefore investigated a role for NIN in mycorrhiza formation. Our time course study, using two nin alleles with differing genetic backgrounds, suggests that that loss of NIN does not affect colonization of Medicago truncatula roots, either in the presence or absence of rhizobia. This, and recent phylogenetic analyses showing that the loss of NIN is correlated with loss of nodulation in the FaFaCuRo clade, but not with the ability to form mycorrhiza, argue against NIN being required for arbuscular mycorrhization in legumes

LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org

Estimating Population Cause-Specific Mortality Fractions from in-Hospital Mortality: Validation of a New Method

Working in Mexico and using vital registration data, Chris Murray and colleagues achieved encouraging results with a new method to estimate population cause-specific mortality fractions

Legumes display common and host-specific responses to the rhizobial cellulase CelC2 during primary symbiotic infection.

Primary infection of legumes by rhizobia involves the controlled localized enzymatic breakdown of cell walls at root hair tips. Previous studies determined the role of rhizobial CelC2 cellulase in different steps of the symbiotic interaction Rhizobium leguminosarum-Trifolium repens. Recent findings also showed that CelC2 influences early signalling events in the Ensifer meliloti-Medicago truncatula interaction. Here, we have monitored the root hair phenotypes of two legume plants, T. repens and M. sativa, upon inoculation with strains of their cognate and non-cognate rhizobial species, R. leguminosarum bv trifolii and E. meliloti, (over)expressing the CelC2 coding gene, celC. Regardless of the host, CelC2 specifically elicited ‘hole-on-the-tip’ events (Hot phenotype) in the root hair apex, consistent with the role of this endoglucanase in eroding the noncrystalline cellulose found in polarly growing cell walls. Overproduction of CelC2 also increased root hair tip redirections (RaT phenotype) events in both cognate and non-cognate hosts. Interestingly, heterologous celC expression also induced non-canonical alterations in ROS (Reactive Oxygen Species) homeostasis at root hair tips of Trifolium and Medicago. These results suggest the concurrence of shared unspecific and host-related plant responses to CelC2 during early steps of symbiotic rhizobial infection. Our data thus identify CelC2 cellulase as an important determinant of events underlying early infection of the legume host by rhizobia

During postnatal development endogenous neurosteroids influence GABA-ergic neurotransmission of mouse cortical neurons

AbstractAs neuronal development progresses, GABAergic synaptic transmission undergoes a defined program of reconfiguration. For example, GABAA receptor (GABAAR)-mediated synaptic currents, (miniature inhibitory postsynaptic currents; mIPSCs), which initially exhibit a relatively slow decay phase, become progressively reduced in duration, thereby supporting the temporal resolution required for mature network activity. Here we report that during postnatal development of cortical layer 2/3 pyramidal neurons, GABAAR-mediated phasic inhibition is influenced by a resident neurosteroid tone, which wanes in the second postnatal week, resulting in the brief phasic events characteristic of mature neuronal signalling. Treatment of cortical slices with the immediate precursor of 5α-pregnan-3α-ol-20-one (5α3α), the GABAAR-inactive 5α-dihydroprogesterone, (5α-DHP), greatly prolonged the mIPSCs of P20 pyramidal neurons, demonstrating these more mature neurons retain the capacity to synthesize GABAAR-active neurosteroids, but now lack the endogenous steroid substrate. Previously, such developmental plasticity of phasic inhibition was ascribed to the expression of synaptic GABAARs incorporating the α1 subunit. However, the duration of mIPSCs recorded from L2/3 cortical neurons derived from α1 subunit deleted mice, were similarly under the developmental influence of a neurosteroid tone. In addition to principal cells, synaptic GABAARs of L2/3 interneurons were modulated by native neurosteroids in a development-dependent manner. In summary, local neurosteroids influence synaptic transmission during a crucial period of cortical neurodevelopment, findings which may be of importance for establishing normal network connectivity

The Lagrange Equilibrium Points L_4 and L_5 in a Black Hole Binary System

We calculate the location and stability of the L_4 and L_5 Lagrange equilibrium points in the circular restricted three-body problem as the binary system evolves via gravitational radiation losses. Relative to the purely Newtonian case, we find that the L_4 equilibrium point moves towards the secondary mass and becomes slightly less stable, while the L_5 point moves away from the secondary and gains in stability. We discuss a number of astrophysical applications of these results, in particular as a mechanism for producing electromagnetic counterparts to gravitational-wave signals.Comment: 10 pages, 4 figures, submitted to ApJ; comments welcom

Moving forward in circles: challenges and opportunities in modelling population cycles

Population cycling is a widespread phenomenon, observed across a multitude of taxa in both laboratory and natural conditions. Historically, the theory associated with population cycles was tightly linked to pairwise consumer–resource interactions and studied via deterministic models, but current empirical and theoretical research reveals a much richer basis for ecological cycles. Stochasticity and seasonality can modulate or create cyclic behaviour in non-intuitive ways, the high-dimensionality in ecological systems can profoundly influence cycling, and so can demographic structure and eco-evolutionary dynamics. An inclusive theory for population cycles, ranging from ecosystem-level to demographic modelling, grounded in observational or experimental data, is therefore necessary to better understand observed cyclical patterns. In turn, by gaining better insight into the drivers of population cycles, we can begin to understand the causes of cycle gain and loss, how biodiversity interacts with population cycling, and how to effectively manage wildly fluctuating populations, all of which are growing domains of ecological research

LegumeGRN: a gene regulatory network prediction server for functional and comparative studies

Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i.e., Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments. Users are able to select which experiments, genes and algorithms they will consider to perform their GRN analysis. To achieve this flexibility and improve prediction performance, we have implemented multiple mainstream GRN prediction algorithms including co-expression, Graphical Gaussian Models (GGMs), Context Likelihood of Relatedness (CLR), and parallelized versions of TIGRESS and GENIE3. Besides these existing algorithms, we also proposed a parallel Bayesian network learning algorithm, which can infer causal relationships (i.e., directionality of interaction) and scale up to several thousands of genes. Moreover, this web server also provides tools to allow integrative and comparative analysis between predicted GRNs obtained from different algorithms or experiments, as well as comparisons between legume species. The web site is available at http://legumegrn.noble.org.Oklahoma Center for The Advancement of Science and Technology: (OCAST Grant No. PSB11-031)

Chasing the identification of ASCA Galactic Objects (ChIcAGO): An X-ray survey of unidentified sources in the galactic plane. I : Source sample and initial results

We present the Chasing the Identification of ASCA Galactic Objects (ChIcAGO) survey, which is designed to identify the unknown X-ray sources discovered during the ASCA Galactic Plane Survey (AGPS). Little is known about most of the AGPS sources, especially those that emit primarily in hard X-rays (2-10 keV) within the Fx 10-13 to 10-11 erg cm -2 s-1 X-ray flux range. In ChIcAGO, the subarcsecond localization capabilities of Chandra have been combined with a detailed multiwavelength follow-up program, with the ultimate goal of classifying the >100 unidentified sources in the AGPS. Overall to date, 93 unidentified AGPS sources have been observed with Chandra as part of the ChIcAGO survey. A total of 253 X-ray point sources have been detected in these Chandra observations within 3′ of the original ASCA positions. We have identified infrared and optical counterparts to the majority of these sources, using both new observations and catalogs from existing Galactic plane surveys. X-ray and infrared population statistics for the X-ray point sources detected in the Chandra observations reveal that the primary populations of Galactic plane X-ray sources that emit in the Fx 10-13 to 10-11 erg cm -2 s-1 flux range are active stellar coronae, massive stars with strong stellar winds that are possibly in colliding wind binaries, X-ray binaries, and magnetars. There is also another primary population that is still unidentified but, on the basis of its X-ray and infrared properties, likely comprises partly Galactic sources and partly active galactic nuclei.Peer reviewedSubmitted Versio

MtLAX2, a functional homologue of the Arabidopsis auxin influx transporter AUX1, is required for nodule organogenesis

Most legume plants can form nodules, specialized lateral organs that form on roots, and house nitrogen-fixing bacteria collectively called rhizobia. The uptake of the phytohormone auxin into cells is known to be crucial for development of lateral roots. To test the role of auxin influx in nodulation we used the auxin influx inhibitors 1-naphthoxyacetic acid (1-NOA) and 2-NOA, which we found reduced nodulation of Medicago truncatula. This suggested the possible involvement of the AUX/LAX family of auxin influx transporters in nodulation. Gene expression studies identified MtLAX2, a paralogue of Arabidopsis (Arabidopsis thaliana) AUX1, as being induced at early stages of nodule development. MtLAX2 is expressed in nodule primordia, the vasculature of developing nodules, and at the apex of mature nodules. The MtLAX2 promoter contains several auxin response elements, and treatment with indole-acetic acid strongly induces MtLAX2 expression in roots. mtlax2 mutants displayed root phenotypes similar to Arabidopsis aux1 mutants, including altered root gravitropism, fewer lateral roots, shorter root hairs, and auxin resistance. In addition, the activity of the synthetic DR5-GUS auxin reporter was strongly reduced in mtlax2 roots. Following inoculation with rhizobia, mtlax2 roots developed fewer nodules, had decreased DR5-GUS activity associated with infection sites, and had decreased expression of the early auxin responsive gene ARF16a. Our data indicate that MtLAX2 is a functional analog of Arabidopsis AUX1 and is required for the accumulation of auxin during nodule formation in tissues underlying sites of rhizobial infection