Population Structure in the Model Grass Brachypodium distachyon Is Highly Correlated with Flowering Differences across Broad Geographic Areas

The small, annual grass Brachypodium distachyon (L.) Beauv., a close relative of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), is a powerful model system for cereals and bioenergy grasses. Genome-wide association studies (GWAS) of natural variation can elucidate the genetic basis of complex traits but have been so far limited in B. distachyon by the lack of large numbers of well-characterized and sufficiently diverse accessions. Here, we report on genotyping-by-sequencing (GBS) of 84 B. distachyon, seven B. hybridum, and three B. stacei accessions with diverse geographic origins including Albania, Armenia, Georgia, Italy, Spain, and Turkey. Over 90,000 high-quality single-nucleotide polymorphisms (SNPs) distributed across the Bd21 reference genome were identified. Our results confirm the hybrid nature of the B. hybridum genome, which appears as a mosaic of B. distachyon-like and B. stacei-like sequences. Analysis of more than 50,000 SNPs for the B. distachyon accessions revealed three distinct, genetically defined populations. Surprisingly, these genomic profiles are associated with differences in flowering time rather than with broad geographic origin. High levels of differentiation in loci associated with floral development support the differences in flowering phenology between B. distachyon populations. Genome-wide association studies combining genotypic and phenotypic data also suggest the presence of one or more photoperiodism, circadian clock, and vernalization genes in loci associated with flowering time variation within B. distachyon populations. Our characterization elucidates genes underlying population differences, expands the germplasm resources available for Brachypodium, and illustrates the feasibility and limitations of GWAS in this model grass

Research to action to address inequities: the experience of the Cape Town Equity Gauge

© 2008 Scott et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Deep learning for clustering of multivariate clinical patient trajectories with missing values

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general

Molecular Evolution of the Rice Blast Resistance Gene Pi-ta in Invasive Weedy Rice in the USA

The Pi-ta gene in rice has been effectively used to control rice blast disease caused by Magnaporthe oryzae worldwide. Despite a number of studies that reported the Pi-ta gene in domesticated rice and wild species, little is known about how the Pi-ta gene has evolved in US weedy rice, a major weed of rice. To investigate the genome organization of the Pi-ta gene in weedy rice and its relationship to gene flow between cultivated and weedy rice in the US, we analyzed nucleotide sequence variation at the Pi-ta gene and its surrounding 2 Mb region in 156 weedy, domesticated and wild rice relatives. We found that the region at and around the Pi-ta gene shows very low genetic diversity in US weedy rice. The patterns of molecular diversity in weeds are more similar to cultivated rice (indica and aus), which have never been cultivated in the US, rather than the wild rice species, Oryza rufipogon. In addition, the resistant Pi-ta allele (Pi-ta) found in the majority of US weedy rice belongs to the weedy group strawhull awnless (SH), suggesting a single source of origin for Pi-ta. Weeds with Pi-ta were resistant to two M. oryzae races, IC17 and IB49, except for three accessions, suggesting that component(s) required for the Pi-ta mediated resistance may be missing in these accessions. Signatures of flanking sequences of the Pi-ta gene and SSR markers on chromosome 12 suggest that the susceptible pi-ta allele (pi-ta), not Pi-ta, has been introgressed from cultivated to weedy rice by out-crossing

Assessing chemical mechanisms underlying the effects of sunflower pollen on a gut pathogen in bumble bees

Many pollinator species are declining due to a variety of interacting stressors including pathogens, sparking interest in understanding factors that could mitigate these outcomes. Diet can affect host-pathogen interactions by changing nutritional reserves or providing bioactive secondary chemicals. Recent work found that sunflower pollen (Helianthus annuus) dramatically reduced cell counts of the gut pathogen Crithidia bombi in bumble bee workers (Bombus impatiens), but the mechanism underlying this effect is unknown. Here we analyzed methanolic extracts of sunflower pollen by LC-MS and identified triscoumaroyl spermidines as the major secondary metabolite components, along with a flavonoid quercetin-3-O-hexoside and a quercetin-3-O-(6-O-malonyl)-hexoside. We then tested the effect of triscoumaroyl spermidine and rutin (as a proxy for quercetin glycosides) on Crithidia infection in B. impatiens, compared to buckwheat pollen (Fagopyrum esculentum) as a negative control and sunflower pollen as a positive control. In addition, we tested the effect of nine fatty acids from sunflower pollen individually and in combination using similar methods. Although sunflower pollen consistently reduced Crithidia relative to control pollen, none of the compounds we tested had significant effects. In addition, diet treatments did not affect mortality, or sucrose or pollen consumption. Thus, the mechanisms underlying the medicinal effect of sunflower are still unknown; future work could use bioactivity-guided fractionation to more efficiently target compounds of interest, and explore non-chemical mechanisms. Ultimately, identifying the mechanism underlying the effect of sunflower pollen on pathogens will open up new avenues for managing bee health

Assessing the impact of wolves on ungulate prey

Simple models may be useful in evaluating definitions of population limitation and regulation, and in considering the impact of wolves (Canis lupus) on ungulate prey. Examination of three types of models that have recently been used to assess wolf-ungulate interactions indicates that the total response model is inappropriate and that models generating substantial oscillations may not be realistic. Models based on ratio dependence appear to be more useful and are explored further. Because reintroduction of wolves into areas with abundant prey may yield new insights into the controversial issue of population regulation by predators, the available data are used to consider some needs for research on the impact of wolves on Yellowstone elk (Cervus elaphus) herds, and for a further assessment of ratio dependence