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Xue C, Wen Y, Sheng S, Gao Y, Zhang Y, Chen T, Peng J, Cao S. Hormonal Regulation and Transcriptomic Insights into Flower Development in Hydrangea paniculata 'Vanilla Strawberry'. PLANTS (BASEL, SWITZERLAND) 2024; 13:486. [PMID: 38498457 PMCID: PMC10893276 DOI: 10.3390/plants13040486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 03/20/2024]
Abstract
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.
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Affiliation(s)
- Chao Xue
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
| | - Yuxing Wen
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
| | - Song Sheng
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
- Yuelushan Laboratory, Hunan Agricultural University, Qiushi Building, Furong District, Changsha 410128, China
- The Belt and Road International Union Research Center for Tropical Arid Non-Wood Forest in Hunan Province, 498 South Shaoshan Road, Changsha 410004, China
| | - Yu Gao
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
| | - Yaoyi Zhang
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
| | - Tingfeng Chen
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
| | - Jiqing Peng
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
- Yuelushan Laboratory, Hunan Agricultural University, Qiushi Building, Furong District, Changsha 410128, China
- The Belt and Road International Union Research Center for Tropical Arid Non-Wood Forest in Hunan Province, 498 South Shaoshan Road, Changsha 410004, China
| | - Shoujin Cao
- College of Forestry, Central South University of Forestry & Technology, 498 South Shaoshan Road, Changsha 410004, China; (C.X.); (Y.W.); (S.S.); (Y.G.); (Y.Z.); (T.C.)
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Subramanian A, Zakeri P, Mousa M, Alnaqbi H, Alshamsi FY, Bettoni L, Damiani E, Alsafar H, Saeys Y, Carmeliet P. Angiogenesis goes computational - The future way forward to discover new angiogenic targets? Comput Struct Biotechnol J 2022; 20:5235-5255. [PMID: 36187917 PMCID: PMC9508490 DOI: 10.1016/j.csbj.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 09/09/2022] [Accepted: 09/09/2022] [Indexed: 11/26/2022] Open
Abstract
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.
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Affiliation(s)
- Abhishek Subramanian
- Laboratory of Angiogenesis & Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Pooya Zakeri
- Laboratory of Angiogenesis & Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Centre for Brain and Disease Research, Flanders Institute for Biotechnology (VIB), Leuven, Belgium
- Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Mira Mousa
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Halima Alnaqbi
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Fatima Yousif Alshamsi
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Leo Bettoni
- Laboratory of Angiogenesis & Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ernesto Damiani
- Robotics and Intelligent Systems Institute, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Peter Carmeliet
- Laboratory of Angiogenesis & Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory of Angiogenesis & Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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Moradpour M, Abdullah SNA, Namasivayam P. 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. PLANTS (BASEL, SWITZERLAND) 2021; 10:1064. [PMID: 34073267 PMCID: PMC8230129 DOI: 10.3390/plants10061064] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/07/2021] [Accepted: 04/13/2021] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Mahdi Moradpour
- Laboratory of Agronomy and Sustainable Crop Protection, Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400 UPM, Selangor, Malaysia;
| | - Siti Nor Akmar Abdullah
- Laboratory of Agronomy and Sustainable Crop Protection, Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400 UPM, Selangor, Malaysia;
- Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400 UPM, Selangor, Malaysia
| | - Parameswari Namasivayam
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Science, University Putra Malaysia, Serdang 43400 UPM, Selangor, Malaysia;
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Huynh-Thu VA, Geurts P. Unsupervised Gene Network Inference with Decision Trees and Random Forests. Methods Mol Biol 2019; 1883:195-215. [PMID: 30547401 DOI: 10.1007/978-1-4939-8882-2_8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
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.
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Affiliation(s)
- Vân Anh Huynh-Thu
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium.
| | - Pierre Geurts
- Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
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Ramšak Ž, Coll A, Stare T, Tzfadia O, Baebler Š, Van de Peer Y, Gruden K. Network Modeling Unravels Mechanisms of Crosstalk between Ethylene and Salicylate Signaling in Potato. PLANT PHYSIOLOGY 2018; 178:488-499. [PMID: 29934298 PMCID: PMC6130022 DOI: 10.1104/pp.18.00450] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 06/09/2018] [Indexed: 05/25/2023]
Abstract
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.
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Affiliation(s)
- Živa Ramšak
- National Institute of Biology, Department of Biotechnology and Systems Biology, 1000 Ljubljana, Slovenia
| | - Anna Coll
- National Institute of Biology, Department of Biotechnology and Systems Biology, 1000 Ljubljana, Slovenia
| | - Tjaša Stare
- National Institute of Biology, Department of Biotechnology and Systems Biology, 1000 Ljubljana, Slovenia
| | - Oren Tzfadia
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Špela Baebler
- National Institute of Biology, Department of Biotechnology and Systems Biology, 1000 Ljubljana, Slovenia
| | - Yves Van de Peer
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Genomics Research Institute, University of Pretoria, Pretoria 0028, South Africa
| | - Kristina Gruden
- National Institute of Biology, Department of Biotechnology and Systems Biology, 1000 Ljubljana, Slovenia
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Yordanov YS, Ma C, Yordanova E, Meilan R, Strauss SH, Busov VB. BIG LEAF is a regulator of organ size and adventitious root formation in poplar. PLoS One 2017; 12:e0180527. [PMID: 28686626 PMCID: PMC5501567 DOI: 10.1371/journal.pone.0180527] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 06/17/2017] [Indexed: 01/08/2023] Open
Abstract
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.
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Affiliation(s)
- Yordan S. Yordanov
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, United States of America
- Department of Biological Sciences, Eastern Illinois University, Charleston, Illinois, United States of America
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, United States of America
| | - Elena Yordanova
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, United States of America
- Department of Biological Sciences, Eastern Illinois University, Charleston, Illinois, United States of America
| | - Richard Meilan
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana, United States of America
| | - Steven H. Strauss
- Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, United States of America
| | - Victor B. Busov
- School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, United States of America
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Zhai J, Tang Y, Yuan H, Wang L, Shang H, Ma C. A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function. FRONTIERS IN PLANT SCIENCE 2016; 7:1914. [PMID: 28018423 PMCID: PMC5156684 DOI: 10.3389/fpls.2016.01914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 12/02/2016] [Indexed: 05/10/2023]
Abstract
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.
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