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Abstract Nucleotide-binding site (NBS) domain genes are one of the superfamily of resistance genes involved in plant responses to pathogens. The current study identified 12,820 NBS-domain-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes are classified into 168 classes with several novel domain architecture patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR, etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBSetc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG₀, OG₁, OG_2,etc.) and unique (highly specific to species; OG₈₀, OG_82,etc.) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG₂, OG_6,and OG₁₅in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to cotton leaf curl disease (CLCuD). The genetic variation between susceptible (Coker 312) and tolerant (Mac7)Gossypium hirsutumaccessions identified several unique variants inNBSgenes of Mac7 (6583 variants) and Coker312 (5173 variants). The protein–ligand and proteins-protein interaction showed a strong interaction of some putativeNBSproteins with ADP/ATP and different core proteins of the cotton leaf curl disease virus. The silencing ofGaNBS(OG₂) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful in understanding the plant adaptation mechanism. 

Summary IRE1, BI-1, and bZIP60 monitor compatible plant–potexvirus interactions though recognition of the viral TGB3 protein. This study was undertaken to elucidate the roles of three IRE1 isoforms, the bZIP60U and bZIP60S, and BI-1 roles in genetic reprogramming of cells during potexvirus infection. Experiments were performed using Arabidopsis thaliana knockout lines and Plantago asiatica mosaic virus infectious clone tagged with the green fluorescent protein gene (PlAMV-GFP). There were more PlAMV-GFP infection foci in ire1a/b, ire1c, bzip60, and bi-1 knockout than wild-type (WT) plants. Cell-to-cell movement and systemic RNA levels were greater bzip60 and bi-1 than in WT plants. Overall, these data indicate an increased susceptibility to virus infection. Transgenic overexpression of AtIRE1b or StbZIP60 in ire1a/b or bzip60 mutant background reduced virus infection foci, while StbZIP60 expression influences virus movement. Transgenic overexpression of StbZIP60 also confers endoplasmic reticulum (ER) stress resistance following tunicamycin treatment. We also show bZIP60U and TGB3 interact at the ER. This is the first demonstration of a potato bZIP transcription factor complementing genetic defects in Arabidopsis. Evidence indicates that the three IRE1 isoforms regulate the initial stages of virus replication and gene expression, while bZIP60 and BI-1 contribute separately to virus cell-to-cell and systemic movement. 

Duckweed (Lemnaceae) rises as a crucial model system due to its unique characteristics and wide-ranging utility. The significance of physiological research and phytoremediation highlights the intricate potential of duckweed in the current era of plant biology. Special attention to duckweed has been brought due to its distinctive features of nutrient uptake, ion transport dynamics, detoxification, intricate signaling, and stress tolerance. In addition, duckweed can alleviate environmental pollutants and enhance sustainability by participating in bioremediation processes and wastewater treatment. Furthermore, insights into the genomic complexity of Lemnaceae species and the flourishing field of transgenic development highlight the opportunities for genetic manipulation and biotechnological innovations. Novel methods for the germplasm conservation of duckweed can be adopted to preserve genetic diversity for future research endeavors and breeding programs. This review centers around prospects in duckweed research promoting interdisciplinary collaborations and technological advancements to drive its full potential as a model organism. 

To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation. 

Orphan Genes (OGs) are a mysterious class of genes that have recently gained significant attention. Despite lacking a clear evolutionary history, they are found in nearly all living organisms, from bacteria to humans, and they play important roles in diverse biological processes. The discovery of OGs was first made through comparative genomics followed by the identification of unique genes across different species. OGs tend to be more prevalent in species with larger genomes, such as plants and animals, and their evolutionary origins remain unclear but potentially arise from gene duplication, horizontal gene transfer (HGT), or de novo origination. Although their precise function is not well understood, OGs have been implicated in crucial biological processes such as development, metabolism, and stress responses. To better understand their significance, researchers are using a variety of approaches, including transcriptomics, functional genomics, and molecular biology. This review offers a comprehensive overview of the current knowledge of OGs in all domains of life, highlighting the possible role of dark transcriptomics in their evolution. More research is needed to fully comprehend the role of OGs in biology and their impact on various biological processes. 

Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein–protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant–pathogen interactions and related functions and pathways compared to the other algorithms.