• Bioinformatics Workflow for Co...

*Result*: Bioinformatics Workflow for Co-Transcriptome Analysis of Plant-Bacterial Interactions.

Title:
Bioinformatics Workflow for Co-Transcriptome Analysis of Plant-Bacterial Interactions.
Authors:
Tang Y; National Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Hubei Key Laboratory of Plant Pathology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China., Tsuda K; National Key Laboratory of Agricultural Microbiology, Hubei Hongshan Laboratory, Hubei Key Laboratory of Plant Pathology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China. tsuda@mail.hzau.edu.cn.
Source:
Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2026; Vol. 3012, pp. 181-223.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
Imprint Name(s):
Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press
MeSH Terms:
Computational Biology*/methods , Plants*/microbiology , Plants*/genetics , Gene Expression Profiling*/methods , Bacteria*/genetics , Host-Pathogen Interactions*/genetics , Transcriptome*, Symbiosis/genetics ; Workflow
References:
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. (PMID: 10.1038/nrg2484190156602949280)
Nakagami S, Wang Z, Han X, Tsuda K (2024) Regulation of bacterial growth and behavior by host plant. Annu Rev Phytopathol 62:69–96. (PMID: 10.1146/annurev-phyto-010824-02335938857544)
Nobori T, Velásquez AC, Wu J, Kvitko BH, Kremer JM, Wang Y, He SY, Tsuda K (2018) Transcriptome landscape of a bacterial pathogen under plant immunity. Proc Natl Acad Sci USA 115:E3055–E3064. (PMID: 10.1073/pnas.1800529115295310385879711)
Entila F, Han X, Mine A, Schulze-Lefert P, Tsuda K (2024) Commensal lifestyle regulated by a negative feedback loop between Arabidopsis ROS and the bacterial T2SS. Nat Commun 15:456. (PMID: 10.1038/s41467-024-44724-23821233210784570)
Nobori T, Wang Y, Wu J, Stolze SC, Tsuda Y, Finkemeier I, Nakagami H, Tsuda K (2020) Multidimensional gene regulatory landscape of a bacterial pathogen in plants. Nat Plants 6:883–896. (PMID: 10.1038/s41477-020-0690-732541952)
Nobori T, Cao Y, Entila F, Dahms E, Tsuda Y, Garrido-Oter R, Tsuda K (2022) Dissecting the cotranscriptome landscape of plants and their microbiota. EMBO Rep 23:e55380. (PMID: 10.15252/embr.202255380362196909724666)
Nobori T, Tsuda K (2018) In planta transcriptome analysis of pseudomonas syringae. Bio Protoco 8:e2987–e2987. (PMID: 10.21769/BioProtoc.2987)
Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E, Rott M, Dombrowski N, Münch PC, Spaepen S, Remus-Emsermann M, Hüttel B, McHardy AC, Julia A, Vorholt JA, Schulze-Lefert P (2015) Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528:364–369. (PMID: 10.1038/nature1619226633631)
Kim D, Paggi JM, Park C, Bennett C, Salzberg SL (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37:907–915. (PMID: 10.1038/s41587-019-0201-4313758077605509)
Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169. (PMID: 10.1093/bioinformatics/btu63825260700)
Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve years of SAMtools and BCFtools. GigaScience 10:giab008. (PMID: 10.1093/gigascience/giab008335908617931819)
Yu G (2024) Thirteen years of clusterProfiler. Innovation 5:100722. (PMID: 3952996011551487)
Chen Y, Chen L, Lun ATL, Baldoni P, Smyth GK (2025) edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Res 53:gkaf018. (PMID: 10.1093/nar/gkaf0183984445311754124)
Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York. (PMID: 10.1007/978-3-319-24277-4)
Slowikowski K (2024) Ggrepel: automatically position non-overlapping text labels with ‘ggplot2’. https://ggrepel.slowkow.com/.
Kolde R (2019) Pheatmap: pretty Heatmaps. R package version 1.0.12. https://CRAN.R-project.org/package=pheatmap.
Storey JD, Bass AJ, Dabney A, Robinson D (2025) qvalue: Q-value estimation for false discovery rate control. R package version 2.40.0. https://bioconductor.org/packages/qvalue.
Neuwirth E (2022) RColorBrewer: ColorBrewer palettes. R package version 1.1-3. https://CRAN.R-project.org/package=RColorBrewer.
Levy A, Salas Gonzalez I, Mittelviefhaus M, Clingenpeel S, Herrera Paredes S, Miao J, Wang K, Devescovi G, Stillman K, Monteiro F, Alvarez BR, Lundberg DS, Lu TY, Lebeis S, Jin Z, McDonald M, Klein AP, Meghan E Feltcher ME, Rio TG, Grant SR, Doty SL, Ley RE, Zhao B, Venturi V, Pelletier DA, Vorholt JA, Tringe SG, Woyke T, Dangl JL (2018) Genomic features of bacterial adaptation to plants. Nat Genet 50:138–150. (PMID: 10.1038/s41588-017-0012-9)
Contributed Indexing:
Keywords: Co-transcriptomics; Host-pathogen interactions; In-planta bacterial transcriptome; Plant–bacterial interactions; RNA-seq pipeline
Entry Date(s):
Date Created: 20260205 Date Completed: 20260205 Latest Revision: 20260205
Update Code:
20260206
DOI:
10.1007/978-1-0716-5138-4_13
PMID:
41642524
Database:
MEDLINE

*Further Information*

*Transcriptomic profiling of plant-bacterial interactions provides critical insights into the molecular mechanisms underlying parasitism, commensalism, and mutualism. RNA sequencing (RNA-seq) enables the simultaneous analysis of plant and bacterial transcriptomes during colonization; however, integrated computational workflows specifically tailored for co-transcriptome analysis remain limited. Here, we present a step-by-step bioinformatics pipeline for analyzing co-transcriptome landscapes in plant-bacterial interactions. This workflow includes: (1) quality control and processing of raw RNA-seq data from both plant host and in-planta bacterial populations; (2) statistical analyses for differential gene expression; (3) prediction of orthologous bacterial genes and functional annotation of bacterial transcripts using the KEGG database; (4) integration and comparative analysis across multiple bacterial strains; and (5) correlation-based analysis of transcriptional dynamics between plants and bacteria. Designed for researchers with basic familiarity with command-line tools and R programming, this pipeline enables comprehensive analysis of plant-bacterial transcriptional interplay and facilitates hypothesis generation in both pathogenic and symbiotic contexts.
(© 2026. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)*