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Deciphering Plant Molecular Data Using Computational Methods
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Deciphering Plant Molecular Data Using Computational Methods

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Molecular Biology".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3448

Special Issue Editor

Dr. Rajeev K. Azad

E-Mail Website
Guest Editor
1. Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
2. BioDiscovery Institute, University of North Texas, Denton, TX 76203, USA
3. Department of Mathematics, University of North Texas, Denton, TX 76203, USA
Interests: plants bioinformatics; computational genomics, genome evolution, pathogenomics, metagenomics; gene prediction, structural variation detection, disease gene identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High-throughput sequencing technologies continue to generate vast amounts of plant molecular data, advancing the frontier of plant biology research and making significant contributions to the understanding of plants. Computational analysis has become an indispensable tool in unraveling complex plant molecular networks.

This Special Issue aims to provide a platform for researchers to share the latest findings and advancements in the use of computational methods for unraveling the mysteries of plant molecular networks. We welcome original research articles, critical review papers and perspectives that delve into various aspects of this field, including the genomics, transcriptomics, proteomics, metabolomics and epigenomics of plants. Additionally, we encourage contributions on data processing, analysis and visualization techniques that pave the way for novel insights into plant molecular mechanisms.

Dr. Rajeev K. Azad
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • plant molecular networks
  • plant biology
  • computational data
  • genomics
  • transcriptomics
  • proteomics
  • metabolomics

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Published Papers (3 papers)

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Research

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20 pages, 2978 KiB  
Article
Response of Arabidopsis thaliana to Flooding with Physical Flow
by Momoko Kaji, Kazuma Katano, Taufika Islam Anee, Hiroshi Nitta, Ryotaro Yamaji, Rio Shimizu, Shunsuke Shigaki, Hiroyuki Suzuki and Nobuhiro Suzuki
Plants 2024, 13(24), 3508; https://doi.org/10.3390/plants13243508 - 16 Dec 2024
Viewed by 499
Abstract
Flooding causes severe yield losses worldwide, making it urgent to enhance crop tolerance to this stress. Since natural flooding often involves physical flow, we hypothesized that the effects of submergence on plants could change when combined with physical flow. In this study, we [...] Read more.
Flooding causes severe yield losses worldwide, making it urgent to enhance crop tolerance to this stress. Since natural flooding often involves physical flow, we hypothesized that the effects of submergence on plants could change when combined with physical flow. In this study, we analyzed the growth and transcriptome of Arabidopsis thaliana exposed to submergence or flooding with physical flow. Plants exposed to flooding with physical flow had smaller rosette diameters, especially at faster flow rates. Transcriptome analysis revealed that “defense response” transcripts were highly up-regulated in response to flooding with physical flow. In addition, up-regulation of transcripts encoding ROS-producing enzymes, SA synthesis, JA synthesis, and ethylene signaling was more pronounced under flooding with physical flow when compared to submergence. Although H2O2 accumulation changed in response to submergence or flooding with physical flow, it did not lead to lipid peroxidation, suggesting a role for ROS as signaling molecules under these conditions. Multiple regression analysis indicated possible links between rosette diameter under flooding with physical flow and the expression of Rbohs and SA synthesis transcripts. These findings suggest that pathogen defense responses, regulated by SA and ROS signaling, play crucial roles in plant responses to flooding with physical flow. Full article
(This article belongs to the Special Issue Deciphering Plant Molecular Data Using Computational Methods)
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15 pages, 4155 KiB  
Article
Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images
by Sofia A. Bengoa Luoni, Riccardo Ricci, Melanie A. Corzo, Genc Hoxha, Farid Melgani and Paula Fernandez
Plants 2024, 13(14), 1998; https://doi.org/10.3390/plants13141998 - 22 Jul 2024
Cited by 1 | Viewed by 1072
Abstract
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of [...] Read more.
Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase. Full article
(This article belongs to the Special Issue Deciphering Plant Molecular Data Using Computational Methods)
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Review

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14 pages, 1814 KiB  
Review
Polyploids of Brassicaceae: Genomic Insights and Assembly Strategies
by Donghyun Jeon and Changsoo Kim
Plants 2024, 13(15), 2087; https://doi.org/10.3390/plants13152087 - 27 Jul 2024
Viewed by 1259
Abstract
The Brassicaceae family is distinguished by its inclusion of high-value crops such as cabbage, broccoli, mustard, and wasabi, all noted for their glucosinolates. In this family, many polyploidy species are distributed and shaped by numerous whole-genome duplications, independent genome doublings, and hybridization events. [...] Read more.
The Brassicaceae family is distinguished by its inclusion of high-value crops such as cabbage, broccoli, mustard, and wasabi, all noted for their glucosinolates. In this family, many polyploidy species are distributed and shaped by numerous whole-genome duplications, independent genome doublings, and hybridization events. The evolutionary trajectory of the family is marked by enhanced diversification and lineage splitting after paleo- and meso-polyploidization, with discernible remnants of whole-genome duplications within their genomes. The recent neopolyploidization events notably increased the proportion of polyploid species within the family. Although sequencing efforts for the Brassicaceae genome have been robust, accurately distinguishing sub-genomes remains a significant challenge, frequently complicating the assembly process. Assembly strategies include comparative analyses with ancestral species and examining k-mers, long terminal repeat retrotransposons, and pollen sequencing. This review comprehensively explores the unique genomic characteristics of the Brassicaceae family, with a particular emphasis on polyploidization events and the latest strategies for sequencing and assembly. This review will significantly improve our understanding of polyploidy in the Brassicaceae family and assist in future genome assembly methods. Full article
(This article belongs to the Special Issue Deciphering Plant Molecular Data Using Computational Methods)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

A guide to metabolic network modeling for plant biology

Xiaolan Rao and Wei Liu

Abstract

Plants produce a wide range of compounds that play diverse roles in plant growth and development, and in response to abiotic and biotic stresses. Understanding metabolic pathway fluxes in plants is essential in guiding strategies to direct metabolism for crop improvement, plant natural product industry, and human nutrition and health. In the past decade, metabolic network modeling has become a predominant tool for integration, quantitation, and prediction of the spatial and temporal distribution of metabolic flows. In this review, we provide a practical protocol for mathematical modeling of metabolic networks, including steady-state modeling (flux balance analysis (FBA) and metabolic flux analysis (MFA)) and dynamic modeling. The practical application of mathematical modeling will provide significant insights in the structure and regulation of plant metabolic networks.

 

2. Gene network analysis of Citrus Huanglongbing (HLB): Spatiotemporal Insights Across Species and Tissues.

 Machado, R. ; Moschen, S. ; Conti, G.; González, S.A. ; Rivarola, M.; Gómez, C. ; Hopp, H.E.  &  Fernández, P.

Huanglongbing (HLB), caused mainly by Candidatus Liberibacter asiaticus (CLas), is a devastating disease which threatens citrus production worldwide, manifesting as leaf greening, fruit deformation and yield losses. This study generated a comprehensive co-expression network analysis using RNA-seq data from 17 public datasets and applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with citrus species, tissue types and days post infection (DPI).

Key modules were identified among different conditions.  We found a significative gene enrichment involved in stress responses, metabolic processes, ribosomal protein synthesis; chloroplast and plastid functions; cellular structure; intracellular organization, and stress responses. These findings provide a molecular framework for understanding HLB pathogenesis and host tolerance.

By elucidating module-specific functions and their correlation with species- and tissue-specific responses, this study provides a robust foundation for identifying key genetic targets. These insights facilitate breeding programs focused on developing HLB-tolerant citrus cultivars, contributing to the long-term sustainability and resilience of global citrus production.

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