Impaired induction of the Jasmonate Pathway in the rice mutant hebiba

The elongation of rice (Oryza sativa) coleoptiles is inhibited by light, and this photoinhibition was used to screen for mutants with impaired light response. In one of the isolated mutants, hebiba, coleoptile elongation was stimulated in the presence of red light, but inhibited in the dark. Light responses of endogenous indolyl-3-acetic acid and abscisic acid were identical between the wild type and the mutant. In contrast, the wild type showed a dramatic increase of jasmonate heralded by corresponding increases in the content of its precursor o-phytodienoic acid, whereas both compounds were not detectable in the mutant. The jasmonate response to wounding was also blocked in the mutant. The mutant phenotype was rescued by addition of exogenous methyl jasmonate and o-phytodienoic acid. Moreover, the expression of O. sativa 12-oxophytodienoic acid reductase, an early gene of jasmonic acid-synthesis, is induced by red light in the wild type, but not in the mutant. This evidence suggests a novel role for jasmonates in the light response of growth, and we discuss a cross-talk between jasmonate and auxin signaling. In addition, hebiba represents the first rice mutant in which the induction of the jasmonate pathway is impaired providing a valuable tool to study the role of jasmonates in Graminean development

Phantom epistasis in genomic selection: on the predictive ability of epistatic models

Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density (“Phantom Epistasis”). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.Fil: Schrauf, Matías Florián. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Cátedra de Mejoramiento Genético Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martini, Johannes W.R.. Centro Internacional de Mejoramiento de Maíz y Trigo; MéxicoFil: Simianer, Henner. Universität Göttingen; AlemaniaFil: de los Campos, Gustavo. Michigan State University; Estados UnidosFil: Cantet, Rodolfo Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Unidad Ejecutora de Investigaciones en Producción Animal. Universidad de Buenos Aires. Facultad de Ciencias Veterinarias. Unidad Ejecutora de Investigaciones en Producción Animal; ArgentinaFil: Freudenthal, Jan. Universität Würzburg; AlemaniaFil: Korte, Arthur. Universität Würzburg; AlemaniaFil: Munilla Leguizamon, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Animal. Cátedra de Mejoramiento Genético Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites

cis‐prenyltransferase 3 and α/β‐hydrolase are new determinants of dolichol accumulation in Arabidopsis

Dolichols (Dols), ubiquitous components of living organisms, are indispensable for cell survival. In plants, as well as other eukaryotes, Dols are crucial for posttranslational protein glycosylation, aberration of which leads to fatal metabolic disorders in humans and male sterility in plants. Until now, the mechanisms underlying Dol accumulation remain elusive. In this study, we have analysed the natural variation of the accumulation of Dols and six other isoprenoids among more than 120 Arabidopsis thaliana accessions. Subsequently, by combining QTL and GWAS approaches, we have identified several candidate genes involved in the accumulation of Dols, polyprenols, plastoquinone and phytosterols. The role of two genes implicated in the accumulation of major Dols in Arabidopsis—the AT2G17570 gene encoding a long searched for cis‐prenyltransferase (CPT3) and the AT1G52460 gene encoding an α/β‐hydrolase—is experimentally confirmed. These data will help to generate Dol‐enriched plants which might serve as a remedy for Dol‐deficiency in humans

Emerging roles of the mitogen and stress activated kinases MSK1 and MSK2

Mitogen- and stress-activated kinases (MSK) 1 and 2 are nuclear proteins activated downstream of the ERK1/2 or p38 MAPK pathways. MSKs phosphorylate multiple substrates, including CREB and Histone H3, and their major role is the regulation of specific subsets of Immediate Early genes (IEG). While MSKs are expressed in multiple tissues, their levels are high in immune and neuronal cells and it is in these systems most is known about their function. In immunity, MSKs have predominantly anti-inflammatory roles and help regulate production of the anti-inflammatory cytokine IL-10. In the CNS they are implicated in neuronal proliferation and synaptic plasticity. In this review we will focus on recent advances in understanding the roles of MSKs in the innate immune system and neuronal function

Preoperative anaemia and outcome after elective cardiac surgery:a Dutch national registry analysis

Background: Previous studies have shown that preoperative anaemia in patients undergoing cardiac surgery is associated with adverse outcomes. However, most of these studies were retrospective, had a relatively small sample size, and were from a single centre. The aim of this study was to analyse the relationship between the severity of preoperative anaemia and short- and long-term mortality and morbidity in a large multicentre national cohort of patients undergoing cardiac surgery. Methods: A nationwide, prospective, multicentre registry (Netherlands Heart Registration) of patients undergoing elective cardiac surgery between January 2013 and January 2019 was used for this observational study. Anaemia was defined according to the WHO criteria, and the main study endpoint was 120-day mortality. The association was investigated using multivariable logistic regression analysis. Results: In total, 35 484 patients were studied, of whom 6802 (19.2%) were anaemic. Preoperative anaemia was associated with an increased risk of 120-day mortality (adjusted odds ratio [aOR] 1.7; 95% confidence interval [CI]: 1.4–1.9; P<0.001). The risk of 120-day mortality increased with anaemia severity (mild anaemia aOR 1.6; 95% CI: 1.3–1.9; P<0.001; and moderate-to-severe anaemia aOR 1.8; 95% CI: 1.4–2.4; P<0.001). Preoperative anaemia was associated with red blood cell transfusion and postoperative morbidity, the causes of which included renal failure, pneumonia, and myocardial infarction. Conclusions: Preoperative anaemia was associated with mortality and morbidity after cardiac surgery. The risk of adverse outcomes increased with anaemia severity. Preoperative anaemia is a potential target for treatment to improve postoperative outcomes

Why are the δ 13 C org values in Phanerozoic black shales more negative than in modern marine organic matter?

The δ 13 C org values of Phanerozoic black shales average −27‰, whereas those of modern marine organic matter average −20‰. The black shale isotopic values mimic those of continental organic matter, yet their organic geochemical properties mandate that they contain predominantly marine organic matter. Hypotheses that proposed to explain the low δ 13 C values of black shales include diagenetic losses of isotopically heavier organic matter components, releases of isotopically light carbon from methane clathrates or extensive magmatic events, greater photosynthetic discrimination against 13 C during times of higher atmospheric p CO 2 , and greenhouse climate stratification of the surface ocean that magnified photic zone recycling of isotopically light organic matter. Although the last possibility seems contrary to the vertical mixing that leads to the high productivity of modern oceanic upwelling systems, it is consistent with the strongly stratified conditions that accompanied deposition of the organic carbon‐rich Pliocene‐Pleistocene sapropels of the Mediterranean Sea. Because most Phanerozoic black shales contain evidence of photic zone anoxia similar to the sapropels, well‐developed surface stratification of the oceans was likely involved in their formation. Existence of isotopically light land plant organic matter during several episodes of extensive magmatism that accompanied black shale deposition implies massive release of mantle CO 2 that added to the greenhouse conditions that favored oceanic stratification. The 13 C depletion common to most Phanerozoic black shales apparently resulted from a greenhouse climate associated with elevated atmospheric p CO 2 that led to a strongly stratified ocean and photic zone recycling of organic matter in, augmented by magmatic CO 2 releases. Key Points Photic zone recycling of organic carbon is responsible for their low δ 13 C values Black shales deposited during periods of strong surface ocean stratification Periods of greenhouse climate established conditions for black shale depositionPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108327/1/ggge20506.pd

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Using Local Convolutional Neural Networks for Genomic Prediction

The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. In this study, we analyze the use of artificial neural networks (ANN) and, in particular, local convolutional neural networks (LCNN) for genomic prediction, as a region-specific filter corresponds much better with our prior genetic knowledge on the genetic architecture of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000; p = 34,595) and real Arabidopsis data (n = 2,039; p = 180,000) for a variety of traits based on their predictive ability. The baseline LCNN, containing one local convolutional layer (kernel size: 10) and two fully connected layers with 64 nodes each, is outperforming commonly proposed ANNs (multi layer perceptrons and convolutional neural networks) for basically all considered traits. For traits with high heritability and large training population as present in the simulated data, LCNN are even outperforming state-of-the-art methods like genomic best linear unbiased prediction (GBLUP), Bayesian models and extended GBLUP, indicated by an increase in predictive ability of up to 24%. However, for small training populations, these state-of-the-art methods outperform all considered ANNs. Nevertheless, the LCNN still outperforms all other considered ANNs by around 10%. Minor improvements to the tested baseline network architecture of the LCNN were obtained by increasing the kernel size and of reducing the stride, whereas the number of subsequent fully connected layers and their node sizes had neglectable impact. Although gains in predictive ability were obtained for large scale data sets by using LCNNs, the practical use of ANNs comes with additional problems, such as the need of genotyping all considered individuals, the lack of estimation of heritability and reliability. Furthermore, breeding values are additive by design, whereas ANN-based estimates are not. However, ANNs also comes with new opportunities, as networks can easily be extended to account for additional inputs (omics, weather etc.) and outputs (multi-trait models), and computing time increases linearly with the number of individuals. With advances in high-throughput phenotyping and cheaper genotyping, ANNs can become a valid alternative for genomic prediction