An integrated network of Arabidopsis growth regulators and its use for gene prioritization

Hormonal Regulation and Transcriptomic Insights into Flower Development in Hydrangea paniculata 'Vanilla Strawberry'

Understanding the molecular mechanisms that regulate flower growth, development, and opening is of paramount importance, yet these processes remain less explored at the genetic level. Flower development in Hydrangea paniculata 'Vanilla Strawberry' is finely tuned through hormonal signals, yet the genetic underpinnings are not well defined. This study addresses the gap by examining the influence of gibberellic acid (GA3), salicylic acid (SA), and ethylene (ETH) on the flowering traits and underlying molecular responses. Treatment with 100 mg/L SA significantly improved chlorophyll content and bolstered the accumulation of soluble sugars and proteins, advancing the flowering onset by 6 days and lengthening the flowering period by 11 days. Concurrently, this treatment enhanced inflorescence dimensions, increasing length, width, and petal area by 22.76%, 26.74%, and 27.45%, respectively. Contrastingly, 100 mg/L GA3 expanded inflorescence size but postponed flowering initiation and decreased inflorescence count. Higher concentrations of SA and GA3, as well as any concentration of ETH, resulted in delayed flowering and inferior inflorescence attributes. A physiological analysis over 50 days revealed that these regulators variably affected sugar and protein levels and modified antioxidant enzyme activities. An RNA-seq analysis during floral development highlighted significant transcriptomic reprogramming, with SA treatment downregulating Myb transcription factors, implicating them in the modulation of flowering timing and stress adaptation. These findings illuminate the complex interplay between hormonal treatments, gene expression, and flowering phenotypes in Hydrangea paniculata, offering valuable perspectives for ornamental horticulture optimization.

Angiogenesis goes computational - The future way forward to discover new angiogenic targets?

Multi-omics technologies are being increasingly utilized in angiogenesis research. Yet, computational methods have not been widely used for angiogenic target discovery and prioritization in this field, partly because (wet-lab) vascular biologists are insufficiently familiar with computational biology tools and the opportunities they may offer. With this review, written for vascular biologists who lack expertise in computational methods, we aspire to break boundaries between both fields and to illustrate the potential of these tools for future angiogenic target discovery. We provide a comprehensive survey of currently available computational approaches that may be useful in prioritizing candidate genes, predicting associated mechanisms, and identifying their specificity to endothelial cell subtypes. We specifically highlight tools that use flexible, machine learning frameworks for large-scale data integration and gene prioritization. For each purpose-oriented category of tools, we describe underlying conceptual principles, highlight interesting applications and discuss limitations. Finally, we will discuss challenges and recommend some guidelines which can help to optimize the process of accurate target discovery.

The Impact of Heat Stress on Morpho-Physiological Response and Expression of Specific Genes in the Heat Stress-Responsive Transcriptional Regulatory Network in Brassica oleracea

Knowledge of heat-tolerant/sensitive cultivars based on morpho-physiological indicators and an understanding of the action and interaction of different genes in the molecular network are critical for genetic improvement. To screen these indicators, the physiological performance of two different varieties of white and red cabbages (B. oleracea var. capitate f. alba and f. rubra, respectively) under heat stress (HS) and non-stress (NS) was evaluated. Cultivars that showed considerable cell membrane thermostability and less reduction in chlorophyll content with better head formation were categorized as the heat-tolerant cultivars (HTC), while those with reduction in stomatal conductance, higher reduction incurred in chlorophyll and damage to thylakoid membranes are categorized as the heat-sensitive cultivars (HSC). Expression profiling of key genes in the HS response network, including BoHSP70 (HEAT SHOCK PROTEIN 70), BoSCL13 (SCARECROW-LIKE 13) and BoDPB3-1 (transcriptional regulator DNA POLYMERASE II SUBUNIT B3-1 (DPB3-1))/NUCLEAR FACTOR Y SUBUNIT C10 (NF-YC10), were evaluated in all cultivars under HS compared to NS plants, which showed their potential as molecular indicators to differentiate HTC from HSC. Based on the results, the morphophysiological and molecular indicators are applicable to cabbage cultivars for differentiating HTC from HSC, and potential target genes for genome editing were identified for enhancing food security in the warmer regions of the world.

Unsupervised Gene Network Inference with Decision Trees and Random Forests

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference.

Network Modeling Unravels Mechanisms of Crosstalk between Ethylene and Salicylate Signaling in Potato

To develop novel crop breeding strategies, it is crucial to understand the mechanisms underlying the interaction between plants and their pathogens. Network modeling represents a powerful tool that can unravel properties of complex biological systems. In this study, we aimed to use network modeling to better understand immune signaling in potato (Solanum tuberosum). For this, we first built on a reliable Arabidopsis (Arabidopsis thaliana) immune signaling model, extending it with the information from diverse publicly available resources. Next, we translated the resulting prior knowledge network (20,012 nodes and 70,091 connections) to potato and superimposed it with an ensemble network inferred from time-resolved transcriptomics data for potato. We used different network modeling approaches to generate specific hypotheses of potato immune signaling mechanisms. An interesting finding was the identification of a string of molecular events illuminating the ethylene pathway modulation of the salicylic acid pathway through Nonexpressor of PR Genes1 gene expression. Functional validations confirmed this modulation, thus supporting the potential of our integrative network modeling approach for unraveling molecular mechanisms in complex systems. In addition, this approach can ultimately result in improved breeding strategies for potato and other sensitive crops.

BIG LEAF is a regulator of organ size and adventitious root formation in poplar

Here we report the discovery through activation tagging and subsequent characterization of the BIG LEAF (BL) gene from poplar. In poplar, BL regulates leaf size via positively affecting cell proliferation. Up and downregulation of the gene led to increased and decreased leaf size, respectively, and these phenotypes corresponded to increased and decreased cell numbers. BL function encompasses the early stages of leaf development as native BL expression was specific to the shoot apical meristem and leaf primordia and was absent from the later stages of leaf development and other organs. Consistently, BL downregulation reduced leaf size at the earliest stages of leaf development. Ectopic expression in mature leaves resulted in continued growth most probably via sustained cell proliferation and thus the increased leaf size. In contrast to the positive effect on leaf growth, ectopic BL expression in stems interfered with and significantly reduced stem thickening, suggesting that BL is a highly specific activator of growth. In addition, stem cuttings from BL overexpressing plants developed roots, whereas the wild type was difficult to root, demonstrating that BL is a positive regulator of adventitious rooting. Large transcriptomic changes in plants that overexpressed BL indicated that BL may have a broad integrative role, encompassing many genes linked to organ growth. We conclude that BL plays a fundamental role in control of leaf size and thus may be a useful tool for modifying plant biomass productivity and adventitious rooting.

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus, a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Toward this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization), in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The "leave-one-out" cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2). Moreover, RAP ranked 53.68% (204/380) flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.