ArticleBMC bioinformatics2016
Network and biosignature analysis for the integration of transcriptomic and metabolomic data to characterize leaf senescence process in sunflower.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 39 citations in OpenAlex.
- A transcriptome-wide analysis provides novel insights into how Metabacillus indicus promotes coral larvae metamorphosis and settlement.BMC genomics · 2024Article
- Phytohormones and related genes function as physiological and molecular switches regulating water stress response in the sunflower.Physiology and molecular biology of plants : an international journal of functional plant biology · 2024Article
- Co-regulatory network analysis of the main secondary metabolite (SM) biosynthesis in Crocus sativus L.Scientific reports · 2024Article
- Aging and obesity prime the methylome and transcriptome of adipose stem cells for disease and dysfunction.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2023Article
- The Eucalyptus grandis chloroplast proteome: Seasonal variations in leaf development.PloS one · 2022Article
- Genome-wide and comparative phylogenetic analysis of senescence-associated NAC transcription factors in sunflower (Helianthus annuus).BMC genomics · 2021Article
- Dietary switch to Western diet induces hypothalamic adaptation associated with gut microbiota dysbiosis in rats.International journal of obesity (2005) · 2021Article
- Stem girdling affects the onset of autumn senescence in aspen in interaction with metabolic signals.Physiologia plantarum · 2021Article
- Transcriptomic profiling of Solanum peruvianum LA3858 revealed a Mi-3-mediated hypersensitive response to Meloidogyne incognita.BMC genomics · 2020Article
- Leaf Senescence: The Chloroplast Connection Comes of Age.Plants (Basel, Switzerland) · 2019Review
- Exploring gene networks in two sunflower lines with contrasting leaf senescence phenotype using a system biology approach.BMC plant biology · 2019Article
- Transcription Factors Associated with Leaf Senescence in Crops.Plants (Basel, Switzerland) · 2019Review
- Metabolomics of mammalian brain reveals regional differences.BMC systems biology · 2018Article
- Expression of a Plastid-Targeted Flavodoxin Decreases Chloroplast Reactive Oxygen Species Accumulation and Delays Senescence in Aging Tobacco Leaves.Frontiers in plant science · 2018Article
- Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.).Plant molecular biology · 2017Article
- MatureP: prediction of secreted proteins with exclusive information from their mature regions.Scientific reports · 2017Article
- Sunflower Hybrid Breeding: From Markers to Genomic Selection.Frontiers in plant science · 2017Review
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn recent years, high throughput technologies have led to an increase of datasets from omics disciplines allowing the understanding of the complex regulatory networks associated with biological processes. Leaf senescence is a complex mechanism controlled by multiple genetic and environmental variables, which has a strong impact on crop yield. Transcription factors (TFs) are key proteins in the regulation of gene expression, regulating different signaling pathways; their function is crucial for triggering and/or regulating different aspects of the leaf senescence process. The study of TF interactions and their integration with metabolic profiles under different developmental conditions, especially for a non-model organism such as sunflower, will open new insights into the details of gene regulation of leaf senescence.
resultsWeighted Gene Correlation Network Analysis (WGCNA) and BioSignature Discoverer (BioSD, Gnosis Data Analysis, Heraklion, Greece) were used to integrate transcriptomic and metabolomic data. WGCNA allowed the detection of 10 metabolites and 13 TFs whereas BioSD allowed the detection of 1 metabolite and 6 TFs as potential biomarkers. The comparative analysis demonstrated that three transcription factors were detected through both methodologies, highlighting them as potentially robust biomarkers associated with leaf senescence in sunflower.
conclusionsThe complementary use of network and BioSignature Discoverer analysis of transcriptomic and metabolomic data provided a useful tool for identifying candidate genes and metabolites which may have a role during the triggering and development of the leaf senescence process. The WGCNA tool allowed us to design and test a hypothetical network in order to infer relationships across selected transcription factor and metabolite candidate biomarkers involved in leaf senescence, whereas BioSignature Discoverer selected transcripts and metabolites which discriminate between different ages of sunflower plants. The methodology presented here would help to elucidate and predict novel networks and potential biomarkers of leaf senescence in sunflower.
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