The centromere is the region on a chromosome where the kinetochore assembles and spindle microtubules attach during mitosis and meiosis. In the vast majority of eukaryotes, the centromere position is determined epigenetically by the presence of the centromere-specific histone H3 variant CENH3. In species with monocentric chromosomes, CENH3 is confined to a single chromosomal region corresponding to the primary constriction on metaphase chromosomes. By contrast, in holocentrics, CENH3 (and thus centromere activity) is distributed along the entire chromosome length. Here, we report a unique pattern of CENH3 distribution in the holocentric plant Cuscuta europaea. This species expressed two major variants of CENH3, both of which were deposited into one to three discrete regions per chromosome, whereas the rest of the chromatin appeared to be devoid of CENH3. The two CENH3 variants fully co-localized, and their immunodetection signals overlapped with the positions of DAPI-positive heterochromatic bands containing the highly amplified satellite repeat CUS-TR24. This CENH3 distribution pattern contrasted with the distribution of the mitotic spindle microtubules, which attached at uniform density along the entire chromosome length. This distribution of spindle attachment sites proves the holocentric nature of C. europaea chromosomes and also suggests that, in this species, CENH3 either lost its function or acts in parallel to an additional CENH3-free mechanism of kinetochore positioning. Copyright © 2020 Oliveira, Neumann, Jang, Klemme, Schubert, Koblížková, Houben and Macas.Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN has three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields. Copyright © 2020 Khaki, Wang and Archontoulis.Plants host a mesmerizing diversity of microbes inside and around their roots, known as the microbiome. The microbiome is composed mostly of fungi, bacteria, oomycetes, and archaea that can be either pathogenic or beneficial for plant health and fitness. To grow healthy, plants need to surveil soil niches around the roots for the detection of pathogenic microbes, and in parallel maximize the services of beneficial microbes in nutrients uptake and growth promotion. Plants employ a palette of mechanisms to modulate their microbiome including structural modifications, the exudation of secondary metabolites and the coordinated action of different defence responses. Here, we review the current understanding on the composition and activity of the root microbiome and how different plant molecules can shape the structure of the root-associated microbial communities. Examples are given on interactions that occur in the rhizosphere between plants and soilborne fungi. We also present some well-established examples of microbiome harnessing to highlight how plants can maximize their fitness by selecting their microbiome. Understanding how plants manipulate their microbiome can aid in the design of next-generation microbial inoculants for targeted disease suppression and enhanced plant growth. Copyright © 2020 Pascale, Proietti, Pantelides and Stringlis.DNA binding with one finger (Dof) proteins are plant-specific transcription factors with important and diverse functions in seed germination, flowering time, and biotic and abiotic stresses. In this study, haplotype-based association analysis was conducted between heading date and 30 Dof family genes in a worldwide germplasm collection. Of these, 22 Dof genes were associated with heading date. Multiple comparisons among haplotypes revealed their diverse functions in promoting and suppressing heading date under short-day (SD) and long-day (LD) conditions. They cumulatively made a considerable contribution to the missing heritability of heading date. A set of knockout mutants of 30 Dof genes generated by CRISPR/Cas9-mediated genome editing technology showed that 11 and 9 Dof genes regulated heading date under LD and SD, respectively. https://www.selleckchem.com/products/lji308.html Phenotype measurement of mutants showed that these 11 and 9 Dof genes slightly regulated heading with effects of 2-5 days under LD and SD, respectively. Both mutant and natural variation assays indicated functional redundancy in regulating heading date among Dof family genes. Nucleotide diversity analysis suggested that most Dof genes have been subjected to selection during domestication and improvement. Beyond heading date, this set of mutants is also a good resource for evaluating the function of Dof genes in regulating stress tolerance and seed germination. Copyright © 2020 Huang, Han, Cheng, Luo, Bai and Xing.Petal morphogenesis has a profound influence on the quality of ornamental flowers. Most current research on petal development focuses on the early developmental stage, and little is known about the late developmental stage. Previously, it was reported that the GEG gene [a gerbera homolog of the gibberellin-stimulated transcript 1 (GAST1) from tomato] negatively regulates ray petal growth during the late stage of development by inhibiting longitudinal cell expansion. To explore the molecular mechanisms of the role of GEG in petal growth inhibition, an ethylene insensitive 3-like 1 (EIL1) protein was identified from a Gerbera hybrida cDNA library by yeast one-hybrid screening. Direct binding between GhEIL1 and the GEG promoter was confirmed by electrophoretic mobility shift and dual-luciferase assays. The expression profiles of GhEIL1 and GEG were correlated during petal development, while a transient transformation assay suggested that GhEIL1 regulates GEG expression and may be involved in the inhibition of ray petal elongation and cell elongation.