ArticleScientific reports2025
Integrating multi-omic data to identify key genes and pathways involved in rice bacterial leaf streak disease.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- System level identification and multidimensional analysis of hub genes reveal complex regulatory networks underlying human iPSC differentiation into motor neurons.Biochemistry and biophysics reports · 2026Article
- The Rice-Xoc Arms Race: Molecular Mechanisms and Genomic Strategies Against Bacterial Leaf Streak.Rice (New York, N.Y.) · 2026Review
- Integrative Systems-Level Transcriptomic Network Analysis Identifies Candidate Genes Associated with Biofilm Formation and Virulence inInternational journal of molecular sciences · 2026Article
- Transcriptomic and alternative splicing reprogramming by low-dose gamma irradiation in tomato under ToBRFV infection.Biochemistry and biophysics reports · 2026Article
- Beyond Resistance Genes: Silencing Susceptibility.International journal of molecular sciences · 2026Review
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Authors and funding
4 authors.
Funding
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Abstract
Bacterial leaf streak disease (BLS) is caused by Xanthomonas oryzae pv. oryzicola (Xoc), which represents a significant threat to rice (Oryza sativa) crops. The objective of the present work is to identify the genetic regions and signaling pathways that contribute to the infection process caused by Xoc. The study employs RNA-Seq data to investigate gene co-expression networks, with a particular emphasis on essential genes involved in ribosome production. Furthermore, the research investigates codon usage patterns in the context of gene expression and translation processes. The findings highlight the importance of the apoplastic pathway, which plays a crucial role in the infection of plants through the breach of the epidermis in their aerial structures. Bioinformatics tools, including STRING, KEGG, Tomtom, and GoMo, were employed to conduct differential gene expression analyses, pathway enrichment studies, and network construction, thereby facilitating the identification of essential genes involved in stress response and disease resistance. Network cluster analysis corroborated the findings of the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) survey. In total, 16 hub genes were identified through integrated PPI network and CytoHubba analysis, including RPL3B (log2FC = 4.69) and P0650C03.25 (log2FC = 4.4), both involved in ribosomal biogenesis. GO and KEGG pathway enrichment revealed significant activation of ribosome biogenesis, secondary metabolite biosynthesis, and stress-related transcription factors. Promoter motif analysis identified conserved elements associated with transcriptional regulation and water stress responses. Furthermore, key miRNAs (e.g., miR5075 and miR1848) targeting hub genes were predicted. Codon usage analysis showed that several hub genes exhibited high CAI and low ENC, indicating codon optimization under bacterial infection. These findings offer candidate targets for improving rice resistance to BLS. The objective is to furnish insights that can inform the development of efficacious preventive strategies.
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