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Gene expression profiling of tomato roots interacting with Pseudomonas fluorescens unravels the molecular reprogramming that occurs during the early phases of colonization

  • Riccardo ScottiEmail author
  • Nunzio D’AgostinoEmail author
  • Massimo Zaccardelli


The concerns of people over the wide use of chemicals are determining an increasing attention to more eco-friendly management practices in agriculture. Plant growth promoting rhizobacteria (PGPR) are emerging as important beneficial microbial inoculants because of their capability to promote plant growth and improve plant protection. We performed RNASeq to analyze gene expression profiling at 24, 48 and 72 h post inoculation (hpi) by Pseudomonas fluorescens strain CREA 16 (Pf-16), a PGPR previously isolated from Pisum sativum rhizosphere and with proven growth promotion activity on tomato plants, to unravel the extent of transcriptome reprogramming of tomato roots during the colonization. Pf-16 induces transcriptional reprogramming mainly at 24 and 72 hpi particularly affecting the down-regulation of genes. During the investigated time span, two phases can be clearly distinguished. In the first phase, within the first 48 h, Pf-16 strongly represses R-genes, calcium/calmodulin-mediated signaling and various transcription factors involved in the salicylic and jasmonic acid metabolism. Even more, Pf-16 blocks ethylene biosynthesis/signaling and protease-dependent mechanisms. At a later stage, from 48/72 h onwards, an intimate relationship between tomato roots and Pf-16 is established as a result of cell wall modifications. Finally, Pf-16 triggers the up-regulation of genes associated with plant growth promotion. Based on the main findings derived from this research, a model that gathers and describes the molecular events of outmost importance during tomato roots-Pf-16 interaction is proposed.


Solanum lycopersicum Plant-PGPR interaction Beneficial microorganisms RNASeq transcriptome 



We thank Timothy Jenkins for English language polishing. This work was carried out in the frame of the “GenoPom-pro - Integrating post-genomic platforms to enhance the tomato production chain” project (PON02_00395_3082360) and is supported by the e Italian Ministry of Education, University and Research.

Supplementary material

13199_2019_611_MOESM1_ESM.pdf (111 kb)
Figure S1 Overall experimental design and analysis scheme. Sampling was carried out in triplicate at 24, 48 and 72 h post inoculation. (PDF 110 kb)
13199_2019_611_MOESM2_ESM.pdf (125 kb)
Figure S2 Box plots of the distribution of read counts before and after TMM normalization. Biological replicates of 6 samples (control and Pseudomonas fluorescens treated samples at 24, 48 and 72 h post inoculation (hpi)) are represented with the same colour. (PDF 125 kb)
13199_2019_611_MOESM3_ESM.pdf (360 kb)
Figure S3 Scatter plots and Pearson’s correlation coefficient (r2). Relationships among expression estimates in three biological replicates of control (a) and Pseudomonas fluorescens treated (b) samples at 24, 48 and 72 h post inoculation (hpi). (PDF 360 kb)
13199_2019_611_MOESM4_ESM.pdf (46 kb)
Figure S4 Area-proportional Venn diagrams displaying the overlap of DEGs independently called by DESeq2 and edgeR. Only DEGs called by both methods were used to describe the transcriptional reprogramming of tomato roots after interaction with Pseudomonas fluorescens strain CREA-C16. (PDF 45 kb)
13199_2019_611_MOESM5_ESM.pdf (23 kb)
Figure S5 Comparison between RNAseq (bars) and qRT-PCR (dotted lines) expression data of 8 selected DEGs, and Person’s correlation coefficients. Data are mean of three biological replicates at 24, 48, and 72 h post-inoculation (hpi). Bars represent standard deviation between biological replicates. Pearson’s correlation coefficient (r2) shows the relationships among expression estimates by RNASeq and qRT-PCR. (PDF 22 kb)
13199_2019_611_MOESM6_ESM.pdf (121 kb)
Figure S6 Distribution of DEGs in MAPMAN functional categories (BINs) at different time points (24, 48 and 72 hpi) duringPseudomonas fluorescens-tomato root interaction. Blue and green boxes correspond to up-regulated and down-regulated genes, respectively. Numbers refer to BIN designations as defined in MAPMAN: (1) Photosystem; (2) Major carbohydrates metabolism; (3) Minor carbohydrates metabolism; (4) Glycolysis; (5) Fermentation; (6) Gluconeogenesis/glyoxylate cycle; (7) OPP cycle; (8) TCA/organic transformation; (9) Mitochondrial electron transport/ATP synthesis; (10) Cell wall; (11) Lipid metabolism; (12) N-metabolism; (13) Amino acid metabolism; (14) S-assimilation; (15) Metal handling; (16) Secondary metabolism; (17) Hormone metabolism; (18) Co-factor and vitamine metabolism; (19) Tetrapyrrole synthesis; (20) Stress; (21) Redox; (22) Polyamine metabolism; (23) Nucleotide metabolism; (24) Biodegradation of xenobiotics; (25) C1-metabolism; (26) Miscellaneous enzyme families; (27) RNA; (28) DNA; (29) Protein; (30) Signalling; (31) Cell; (32) Micro RNA, natural antisense; (33) Development; (34) Transport; (35) Not assigned. (PDF 120 kb)
13199_2019_611_MOESM7_ESM.xlsx (113 kb)
Table S1 Complete list of DEGs in at least one out of three comparisons. For each gene at different time points (namely 24, 48, and 72 h post inoculation (hpi)), it is reported the expression level estimate (Log2 fold-change) and a statistical significance value as calculate by edgeR (FDR) and DESeq2 (padj), respectively. Expression and statistical significance values that meet threshold criteria (−2 ≤ Log2 Fold-change ≥2 and FDR/padj <0.05) are highlighted in grey. The iTAG 2.40 functional annotation, the SOTA cluster membership and the MAPMAN ontologies separated by semicolon are reported for each gene. (XLSX 112 kb)
13199_2019_611_MOESM8_ESM.xlsx (11 kb)
Table S2 Enriched gene ontology terms for molecular function. List of GO terms associated with DEGs per cluster, based on Fisher’s exact p value >0.01. (XLSX 11 kb)


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.CREA Research Centre for Vegetable and Ornamental CropsPontecagnano FaianoItaly
  2. 2.Department of Agricultural SciencesUniversity of Naples Federico IIPorticiItaly

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