EARLY BUD-BREAK1 (EBB1) defines a conserved mechanism for control of bud-break in woody perennials

<p>Bud-break is an environmentally and economically important trait in trees, shrubs and vines from temperate latitudes. Poor synchronization of bud-break timing with local climates can lead to frost injuries, susceptibility to pests and pathogens and poor crop yields in fruit trees and vines. The rapid climate changes outpace the adaptive capacities of plants to respond through natural selection. This is particularly true for trees which have long generation cycle and thus the adaptive changes are significantly delayed. Therefore, to devise appropriate breeding and conservation strategies, it is imperative to understand the molecular underpinnings that govern dormancy mechanisms. We have recently identified and characterized the poplar EARLY BUD-BREAK 1 (EBB1) gene. EBB1 is a positive regulator of bud-break and encodes a transcription factor from the AP2/ERF family. Here, using comparative and functional genomics approaches we show that EBB1 function in regulation of bud-break is likely conserved across wide range of woody perennial species with importance to forestry and agriculture.</p

Flowchart illustrating BWERF algorithm for constructing multilayered hierarchical gene regulatory network using expression data of pathway genes and regulatory genes.

<p>A. Input for BWERF included a pathway gene expression matrix and a TFs expression matrix. B. For each pathway gene, recursively constructing of random forest model with backward elimination. C. Aggregation of the importance values of a TF to all pathway genes to produce a unified the importance value of this TF to the pathway. D. The Expectation-maximization (EM) algorithm was implemented to fit a Gaussian mixture model to the importance values. E. The most important TFs were identified and used as a layer. F. By using the new TF layer as bottom layer, we repeated all above procedure to obtain the next layer until the designated number of layer was achieved or the program was terminated due to the lack of significant TFs as input for upper layers.</p

Construction of ML-hGRN for lignocellulosic pathway with a compendium microarray data set (128 chips) from <i>Arabidopsis thaliana</i> roots under salt stress condition.

<p>A. The four-layered hGRN constructed with the BWERF algorithm. B. The four-layered hGRN built from GENIE3. The input files for BWERF and GENIE3 included the expression profiles of 1602 transcription factors and 22 lignocellulosic pathway genes (green nodes at bottom layers). The nodes with red color highlighted in both networks are known regulatory TFs regulating lignocellulosic pathway in existing knowledgebase. The data, edge list as well as gene IDs represented by each symbol can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171532#pone.0171532.s002" target="_blank">S2 File</a>.</p

The precision-recall (PR) and receiver operating characteristic (ROC) curves of BWERF and GENIE3 for three mouse microarray data sets.

<p>The 24 stem cell pluripotency maintenance genes regulated by the 35 transcription factors (TFs) were obtained from mouse ChIP-seq data. A microarray data set of these pathway genes and TFs yielded from a time course in which the pluripotent cells were subjected to undirected differentiation were downloaded from ESCAPE web portal (<a href="http://www.maayanlab.net/ESCAPE/" target="_blank">http://www.maayanlab.net/ESCAPE/</a>). The three data sets were generated by adding the profiles of 100, 200, and 300 noise variables to the profiles of 35 TF genes for <i>in silico</i> experimental validation.</p

Comparison of the ordered important values of top 15 variables generated by GENIE3 (left) and BWERF (right) on a “toys data set”.

<p>Red: true regulators, blue: noise variables. The boxplots were based on 30 runs of GENIE3 and BWERF.</p

AUPR and AUROC values for mouse datasets using BWERF and GENIE3.

<p>AUPR and AUROC values for mouse datasets using BWERF and GENIE3.</p

Additional file 3: FigureS2. of Quantitative trait locus mapping of Populus bark features and stem diameter

Frequency distribution for bark texture, diameter and bark thickness (a, b, and c, respectively) across Oregon and West Virginia sites and various years in Populus Family 52–124. NOTE: All supporting tables, except for Table S3, are in excel format submitted as separate files. (TIFF 209 kb

Additional file 5: Table S3. of Quantitative trait locus mapping of Populus bark features and stem diameter

Number of candidate genes detected across QTL for the three traits. Note: The number of genes for each trait in QTL clusters based on MQM mapping with cofactor selection, sorted by significance and reproducibility. (DOCX 13 kb

A network of genes associated with poplar root development in response to low nitrogen

<p>Deployment of the root system is highly sensitive to the levels and spatial distribution of nutrients like nitrogen. However, the genetic determinants of these sensing and deployment mechanisms are still poorly understood. Previously, using system approaches based on temporal changes in root transcriptome in relation to low nitrogen (LN), we have been able to identify a module that activates root production in poplar in response to LN conditions. Here, using comparative, gene ontology and expression analyses, we provide further evidence that the genes in this module are indeed involved in regulation of root development under LN. Better understanding of these modules will enable approaches for breeding for better nitrogen use efficiency through development of a more sensitive and plastic root system.</p

Additional file 7: Table S5. of Quantitative trait locus mapping of Populus bark features and stem diameter

The 90th percentile candidate genes within the ninety four QTL detected in Populus Family 52–124. Physical localization, annotation and expression profile of gene models in the 90th percentile with high expression within LOD peaks for each QTL interval for all traits. (XLSX 78 kb