Response and degradation of indole-3-aceticacid by the plant growth-promoting rhizobacteria Variovorax boronicumulans

Abstract

Abstract

Plant growth-promoting rhizobacteria (PGPR) of the genus Variovorax facilitate plant growth through beneficial microbe-plant interplay. Unlike most PGPRs, Variovorax boronicumulans CGMCC 4969 utilizes indole-3-acetonitrile as a precursor to generate indole-3-acetic acid (IAA), which is subsequently metabolized by itself. In this study, IAA enhanced the growth of V. boronicumulans CGMCC 4969 in minimal salt medium (MSM), whereas it inhibited bacterial growth when glucose was added to the MSM broth. IAA was rapidly degraded within 12 h in MSM broth despite glucose appeared or not. Notably, in LB broth, the cell growth was significantly inhibited by IAA concentration beyond 1 mmol/L, while the IAA degradation capability of CGMCC 4969 was significantly increased following exposure to IAA-dosed LB medium. V. boronicumulans CGMCC 4969 degraded IAA to yield a new intermediate 3-hydroxy-anthranilate. An iad gene cluster was identified in V. boronicumulans CGMCC 4969, and co-expression of the iadD and iadE genes endows Escherichia coli with the capacity to degrade IAA. This degradation efficiency is augmented when the iadC gene is expressed simultaneously. Subsequent proteomics and bioinformatics analyses highlighted that the addition of IAA induced a significant up-regulation of ABC transporter proteins, in particular IadK3 and IadK2. Interestingly, there was also a significant increase in protein expression associated with group-sensing metabolism. Collectively, this research helps our understanding of the intricate regulatory mechanisms of IAA within Variovorax own metabolism and expands our knowledge of its complex role in plant-microbe interactions.

Key points

• The iad gene cluster degraded IAA to a previously uncharacterized intermediate.

• Adding IAA during the cell culture period enhances IAA-degrading activity.

• Proteomics defined the adaptive response to IAA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00253-026-13705-4.

Introduction

Plant growth and health are intricately influenced by the dynamic microbial communities both within the phytosphere and in the surrounding soil (Lundberg et al. 2012; Bulgarelli et al. 2012). These microbes engage in a complex network of chemical signaling through cooperative and competitive interactions, which can influence plant development both positively and negatively (Rangel et al. 2021; Huang et al. 2019; Finkel et al. 2019; Duke 2018; Chagas et al. 2018; Bastías et al. 2022; Abdul Hamid and Nadarajah 2022). Beneficial interactions include the production of phytohormones such as indole-3-acetic acid (IAA) by PGPR, which at appropriate levels can stimulate plant growth. These stable microbial communities often facilitate the colonization of plants. Bacteria depend on plants for growth, and the chemical signals secreted or degraded by microorganisms can indirectly affect plant physiology and pathology.

IAA is the most common and abundant phytohormone synthesized by plants, controlling almost all aspects of plant growth and development (Zhao 2010; Leyser 2017; Reed 2001). It plays a dual role, significantly promoting the longitudinal growth of Arabidopsis roots (Evans et al. 1994). However, the excessive IAA can inhibit the elongation of organs. In addition to the plant’s ability to synthesize its own auxin, researchers have discovered strains of bacteria that are capable of synthesizing IAA in the vicinity of the plant (Cherif-Silini et al. 2019). Excessive levels of IAA synthesizing bacterial community can produce root growth inhibition (RGI), necessitating the presence of bacteria that can utilize IAA to maintain normal plant growth (Finkel et al. 2020). Some PGPR, such as Pseudomonas (Sun et al. 2018), Arthrobacter(Mino 1970), and Bradyrhizobium (Donati et al. 2013), have been found to degrade IAA, playing a crucial role in regulating IAA levels in host plants. A study has also uncovered that the Variovorax can counteract RGI triggered by Arthrobacter (Finkel et al. 2020). By restoring chemical balance, Variovorax equips plants to continue their regular growth despite the intricate and potentially hostile chemical milieu.

Variovorax exhibits a strong ability to metabolize a wide range of pollutants (Satola et al. 2012) and is commonly used as a model strain for studying plant-bacteria interactions, particularly as a PGPR (Finkel et al. 2020). The genus Variovorax, commonly encountered in environments burdened with pollutants such as contaminated water and soil, is comprised of bacteria renowned for their metabolic flexibility and remarkable capacity to decompose a plethora of organic toxins (Satola et al. 2012). Our research has observed that Variovorax boronicumulans CGMCC 4969 degraded neonicotinoid pesticides and the biosynthesis of an array of PGPR compounds, including a siderophore, salicylic acid (SA), hydrogen cyanide (HCN), ammonia, and IAA (Liu et al. 2013). Notably, the approach of IAA synthesis by V. boronicumulans CGMCC 4969 is distinct among PGPRs. Rather than employing the tryptophan-dependent pathways, which typically proceed through the indole-3-acetamide (IAM) pathway or the indole-3-pyruvate (IpyA) pathway, this strain utilizes indole-3-acetonitrile (IAN) as a precursor (Sun et al. 2018). The subsequent observations have revealed that the synthesized IAA may undergo degradation between 12 and 24 h, a period during which no breakdown products were detected by HPLC analysis. Furthermore, genome sequencing analyses in V. boronicumulans CGMCC 4969 have not identified the presence of the iac gene cluster, which promotes an aerobic route converting IAA into catechol (Greenhut et al. 2018; Laird et al. 2020; Leveau and Gerards 2008; Lin et al. 2012; Sadauskas et al. 2020; Zúñiga et al. 2013). Researchers have characterized two primary gene clusters responsible for IAA catabolism (Conway et al. 2022; Ma et al. 2023; Li et al. 2022; Proctor 1958; Rigaud and Puppo 1975; Raczkowska-Błach et al. 1995). The iac gene cluster mentioned above, and the iaa gene cluster that facilitates an anaerobic pathway leading to the synthesis of 2-aminobenzoyl-CoA (Ebenau-Jehle et al. 2012). Nonetheless, numerous hypothesized pathways for IAA degradation by bacteria differ from those described for the iac and iaa gene clusters, and it has been demonstrated that IAA catabolism also occurs in bacteria lacking these gene clusters (Sun et al. 2018; Vereecke et al. 2020). The iad gene cluster, responsible for IAA degradation and recently identified by Finkel et al., transforms IAA into o-aminobenzoic acid, with the iadD/E gene playing a crucial role in this process and also involved in signaling disruption (Finkel et al. 2020). As shown in Fig. 1, the structure of the iad gene cluster is markedly distinct from that of the previously characterized gene clusters involved in IAA degradation. Additionally, a recent study has demonstrated that incorporating the iadC/D/E gene cluster into Escherichia coli enables it to degrade IAA, offering insights into the IAA catabolic mechanism in Variovorax (Ma et al. 2023).

Fig. 1.

Comparison of V. boronicumulans CGMCC 4969 with other reported IAA metabolism-related gene cluster. The structure of the V. boronicumulans CGMCC 4969 IAA metabolism gene cluster is similar to that of V. paradoxus CL14, but there is an extra hypothetical protein in the middle whose role in IAA metabolism is not yet clear. The same color in the figure represents the same type of gene. The symbol above the arrow denotes its designation within the gene cluster. For instance, an orange arrow with a K above it specifies that the arrow represents iadK

In recent years, there has been increasing in-depth exploration of the products resulting from the degradation of IAA by PGPR and the related gene cluster. However, the mechanism by which IAA induces action on PGPR-regulated hormone concentration has received little attention. In our study, we investigated the mechanisms involved in the degradation of IAA by the PGPR V. boronicumulans CGMCC 4969, as well as the induction of this degradation pathway by IAA. By leveraging proteomics and bioinformatics analyses, we identified the proteins and related pathways up-regulated and enriched in Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Our research aims to elucidate the regulatory role of IAA itself in IAA metabolism and its multifaceted functions in microbe-plant interactions.

Materials and methods

Chemicals and reagents

IAA and 3-hydroxy-anthranilate (≥ 99% purity) were procured from Sangon Biotech Co. (Shanghai, China). Acetonitrile of high-performance liquid chromatography (HPLC) grade was provided by Tedia Co. (Fairfield, OH). All other reagents were of analytical grade and sourced from Sinopharm Chemical Reagent Co. (Shanghai,China).

Strain and plasmid

V. boronicumulans CGMCC 4969, isolated from soil, was deposited in the China General Microbiological Culture Collection Center (CGMCC, Beijing, China) and cataloged under accession number 4969. The expression vector used was pET 28a(+) (Novagen, Germany), and the expression strain employed was E. coli Rosetta (DE3) (Novagen Inc., Madison, WI, USA).

IAA degradation by V. boronicumulans CGMCC 4969 and metabolite identification

Resting cell transformation

V. boronicumulans CGMCC 4969 was removed from the −80 ℃ culture tube and streaked onto a solid LB plate (10 g/L peptone, 5 g/L yeast extract, 10 g/L NaCl, 20 g/L agar), followed by incubation at 30 ℃ in a constant-temperature incubator for 48 h until single colonies developed. Upon the formation of single colonies, they were selected and inoculated into triangular flasks containing 30 mL of liquid LB medium, and then incubated in a shaker at 30 ℃ with agitation at 220 rpm for 18 h. The optical density at 600 nm (OD₆₀₀) of the bacterial solution was measured and adjusted to 2. The bacteria were pelleted by centrifugation at 8000 × g for 5 min. After two washes with PBS buffer solution (pH 7.5, 15.036 g/L Na₂HPO₄·12H₂O, 1.248 g/L NaH₂PO₄), the bacteria were resuspended in a 50 mL centrifuge tube with 5 mL of IAA conversion solution. These samples were centrifuged at 13,000 × g for 5 min; the resulting supernatant was diluted twofold and filtered through a 0.22 μm microporous membrane before HPLC analysis.

Growth cell transformation

The initial steps of the experiment mirrored those of the resting cell transformation experiments above. Subsequently, cells were aspirated from the minipreps using a 2% inoculum volume and incubated in 50 mL minimal salt medium (MSM), which was prepared with a pH of 7.0 and contained 1.36 g/L KH₂PO₄, 2.13 g/L Na₂HPO₄, 0.50 g/L MgSO₄·7H₂O, and 1% metal solution. The metal solution comprised 0.40 g CaCl₂·2H₂O, 0.30 g H₃BO₃, 0.04 g CuSO₄·5H₂O, 0.10 g KI, 0.20 g FeSO₄·7H₂O, 0.40 g MnSO₄·7H₂O, 0.20 g NaMoO₄·2H₂O, and 10 mL of concentrated hydrochloric acid in 1 L of deionized water. The MSM medium was supplemented with pre-configured IAA and glucose solutions to attain a final concentration of 20 mg/L IAA and 2 g/L glucose. Four groups were established for the experiment: the first group received IAA, the second group received only glucose, the third group received both glucose and IAA, and the control group received no additional substances in the MSM medium. Three replicates were prepared for each group. Following inoculation, the triangular flasks were incubated at 30 ℃ and 200 rpm, with periodic sampling.

Mass spectrometry analysis of its intermediates

The metabolites were analyzed using HPLC with an Agilent 1260 system equipped with an HC-C18 column (4.6 × 250 mm). The column was maintained at a temperature of 30 ℃, and the UV detector operated at a wavelength of 225 nm. A flow rate of 1 mL/min was employed, with the mobile phases consisting of 40% double-distilled water with 0.1‰ (v/v) acetic acid and 60% chromatographic grade acetonitrile.

Liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) analysis of metabolites was utilized with an ultra-high-pressure liquid chromatography (UHPLC) tandem quadrupole mass spectrometry (MS) coupler: the Agilent 1290 HPLC–DAD and 6460 HPLC-q3MS/MS system (USA) equipped with an electrospray ionization (ESI) source. The mobile phase composition was consistent with that used for HPLC analysis, and the flow rate was maintained at 0.6 mL/min.

Effect of IAA on the cell growth and degradation of IAA by the resting cell of V. boronicumulans CGMCC 4969

The cell culture process and collection methods were described previously in Sect. 2.3. The bacteria were subsequently transferred to LB medium supplemented with varying concentrations of IAA: 0, 0.25, 0.5, 1, 2.5, and 5 mmol/L. The inoculum used was 1%, and the bacteria were incubated for 18 h. After measuring OD₆₀₀ of the bacterial incubation broth, a specific volume was extracted and the absorbance was adjusted to 2. The bacteria were then centrifuged at 8000 × g for 5 min, collected, rinsed twice with PBS buffer solution, and suspended in 50 mL centrifuge tubes containing 5 mL of a 200 mg/L IAA converting solution. The samples were incubated in a shaker at 30 ℃ and 220 rpm, with samples collected every 0.5 h. Prior to HPLC analysis, the samples were spun at 12,000 × g for 5 min, and the supernatant was diluted two-fold and filtered through a 0.22 μm microporous membrane.

Cloning and expression of key genes

Genomic DNA extraction of V. boronicumulans CGMCC 4969 was performed using the MiniBEST Bacterial Genomic DNA Extraction Kit (version 3.0) (TaKaRa, Beijing, China). Primers for amplifying the key genes were designed using Primer Premier software (version 5.0). The primer pairs (F1/R1) with EcoRI/XhoI restriction endonuclease sites and pET28a(+) homology arm sequences were synthesized by Sangon Biotechnology (Shanghai, China) (Table 1).

Table 1.

Primers used in clonal expression experiments

Target	Primer	Sequence(5′—3′)	Tm (℃)	Amplicon size (bp)
iadE	49-F	acagcaaatgggtcgcggatccgaattcATGGCCGCCGCCGATATC	66.1	489
	49-R	atctcagtggtggtggtggtggtgctcgagTCAGATGAAAAGCTGCACGGC	63.6
iadD	50-F	acagcaaatgggtcgcggatccgaattcATGCCTTCGTACCGAGACAACC	64.8	1314
	50-R	atctcagtggtggtggtggtggtgctcgagTTACATCGTCTCCGTCATGCGCT	66.6
iadC	51-F	acagcaaatgggtcgcggatccgaattcCCTGAAGGGACCCGAACCAT	63.3	1022
	51-R	atctcagtggtggtggtggtggtgctcgagTTCCTGGCTGGCTTTTCTCA	60.7

The numbers in the primer names correspond to the final two digits of the gene’s corresponding protein ID number in NCBI. iadE, ATA56149.1; iadD, ATA56150.1; iadC, ATA56151.1

Each polymerase chain reactions (PCR) were performed in 25 μL reaction volumes containing 1 μL primers F and R (10 μmol/L), 1 μL genomic DNA (151 ng/μL), 12.5 μL 2 × Phanta® Max Master Mix (Vazyme, Nanjing, China), and 10 μL of sterile water. The PCR program was set as follows: initial denaturation at 95 ℃ for 3 min, followed by 40 cycles of denaturation at 95 ℃ for 15 s, annealing at a temperature determined as the average of the melting temperature (Tm) values of primers F and R for 15 min, and extension at 72 ℃ for a duration based on the length of the gene (60 s/kb). Lastly, there was a final extension at 72 ℃ for 5 min.

The ClonExpress® II One-Step Cloning Kit (Vazyme, Nanjing, China) was utilized to ligate the amplified key genes into pET28a(+) plasmid. Induction of key gene expression in E. coli Rosetta was achieved using isopropyl β-D-thiogalactopyranoside with a content of 0.2 mmol/L.

Proteomics analysis

Proteomics sample preparation

V. boronicumulans CGMCC 4969, which had been cultured on an LB plate for 2 days, was inoculated in two groups: one group in 100 mL of LB medium containing IAA at a final concentration of 1 mmol/L, and the other group without IAA. The cultures were incubated for 18 h. After incubation, the samples were transferred to centrifuge tubes and centrifuged at 5000 × g for 10 min at 4 ℃. The resulting bacterial pellet (1 × 10⁸ bacteria per tube) was collected, and the supernatant was discarded. The pellet was washed three times with pre-chilled PBS, with each wash performed at 4 ℃, and meticulously resuspended. Following the washing steps, the bacterial suspension was centrifuged again at 5000 × g for 10 min, and the supernatant was discarded.

Extraction and digestion of protein

The buffer SDT (pH 7.6) containing 4% SDS, 100 mmol/L Tris-HCl, and 1 mmol/L DTT was utilized for sample lysis and protein extraction. Using a BCA Protein Assay Kit (Bio-Rad, USA), the amount of protein was measured. According to Matthias Mann’s filter-assisted sample preparation (FASP) method (Wiśniewski et al. 2009), trypsin was used to degrade proteins. The peptides were desalted using C18 Cartridge (Empore SPE Cartridges C18 (standard density), bed I.D. 7 mm, volume 3 mL, Sigma), and after the peptides were lyophilized, 40 L of 0.1% formic acid solution was added for reconstitution.

Labeling

Each sample’s 100 g peptide mixture was labeled with iTRAQ reagent (Applied Biosystems) according to the manufacturer’s instructions. Each sample’s 100 g peptide mixture was labeled using TMT reagent per the manufacturer’s instructions (Thermo Fisher).

High pH reversed-phase fractionation

The labeled peptides were fractionated using the High pH Reversed-Phase Peptide Fractionation Kit (Thermo Fisher). The dried peptide mixture, reconstituted and acidified with a 0.1% TFA solution, was applied onto an equilibrated, high-pH, reversed-phase fractionation spin column. Under aqueous conditions, peptides bonded to hydrophobic resin were desalted by washing the column with water and centrifuging at low speed. Subsequently, a step gradient of increasing acetonitrile concentrations in a volatile high-pH elution solution was employed to elute bound peptides into 10 distinct fractions, which were then collected by centrifugation. The collected fractions were desalted using C18 Cartridges and concentrated by vacuum centrifugation.

LC-MS/MS data acquisition

Each sample was separated using an HPLC liquid phase system, Easy nLC, at a nanoliter flow rate. Buffer A consisted of 0.1% formic acid in water, while buffer B was 0.1% formic acid in 84% acetonitrile. The chromatographic column was equilibrated with 95% of buffer A. Subsequently, the samples were separated on an EASY-Spray analytical column (10 cm × 75 μm I.D. 3 μm, C18-A2, Thermo Fisher) at a flow rate of 300 nL/min and analyzed using a Q-Exactive mass spectrometer following chromatographic separation.

Protein identification and quantification

For identification and quantitation analysis, the MS raw data for each sample were searched using the MASCOT engine (Matrix Science, London, UK, version 2.2), which is integrated into the Proteome Discoverer 1.4 software.

Bioinformatic analysis

Annotation and enrichment analysis

To identify similar protein sequences, the NCBI BLAST+ client software (ncbi-blast-2.2.28 +-win32.exe) and InterProScan were utilized to perform a local search on the protein sequences of the differentially expressed proteins. Following this, gene ontology (GO) terms were assigned, and the sequences were annotated using Blast2GO software. The results of the GO annotation were visualized using scripts written in the R programming language.

In line with the annotation protocol, the proteins under investigation underwent a BLAST search against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://geneontology.org/). The aim of this search was to identify the proteins’ KEGG orthology, which were then linked to relevant pathways in the KEGG database.

Enrichment analysis was performed using Fisher’s exact test, with the entire set of quantified proteins serving as the background dataset. Subsequently, the Benjamini-Hochberg correction method was employed to adjust the obtained p-values for multiple testing. Only functional categories and pathways with p-values less than 0.05 were considered statistically significant.

PPI analysis

Protein-protein interaction (PPI) data were sourced from the IntAct molecular interaction database (http://www.ebi.ac.uk/intact/) using the gene symbols or through the use of STRING software (http://string-db.org/). The results were obtained in XGMML format and imported into Cytoscape software (version 3.2.1) for visualization and additional analysis of functional PPI networks. Furthermore, the degree of each protein was computed to assess its significance within the PPI network.

Statistical methods

Data analysis was performed using GraphPad Prism (version 9.0). To compare the growth of cells cultured under different conditions, the experimental design incorporated two independent variables: time and treatment. Descriptive statistics were utilized to summarize continuous data, presenting as mean ± standard error of the mean (SEM). A two-way analysis of variance (ANOVA) was conducted to evaluate the effects of treatment and time on cell growth.

Prior to the application of two-way ANOVA, assumptions of normality and homogeneity of variances were tested to ensure the validity of the statistical inferences. The level of significance for all tests was set at α = 0.05. Results yielding p-values less than 0.05 were considered statistically significant.

Results

Biotransformation of IAA by growth cultures of V. boronicumulans CGMCC 4969 and effect of IAA on cell growth

IAA degradation was primarily examined in MSM liquid medium containing 20 mg/L IAA. As shown in Fig. 2, V. boronicumulans CGMCC 4969 rapidly degraded IAA within 12 h through HPLC analysis. In the MSM broth containing only IAA, the degradation rate of IAA was 18.0% during the first 8 h; subsequently, IAA concentration was rapidly decreased within the next 2 h, achieving an IAA degradation rate of 91.6%. In the MSM broth containing IAA and glucose, IAA was hardly degraded during the first 8 h, and then it was sharply decreased in the next 2 h, reaching a degradation rate of 87.3% (Fig. 2).

Fig. 2.

Growth curve and IAA degradation of V. boronicumulans CGMCC 4969 under varying nutrient sources. Different colors represent different groups: black for the control group, red for the group with IAA only, green for the group with both IAA and glucose, and blue for the group with glucose only. The degradation of IAA in the two experimental groups to which IAA was added is shown in the small figure

Cell growth curve analysis showed that the cell density cultivated under the MSM broth supplemented with IAA exhibited a significant increase after 36 h compared to the control group without IAA. At 60 h and 72 h of cultivation, the increases in cell growth reached 24% (^*p < 0.05) and 60% (^**p < 0.01), respectively. Surprisingly, cultivated under the MSM broth supplied with glucose, the additional IAA showed an inhibitory effect on cell growth of V. boronicumulans CGMCC 4969 before reaching the stationary phase. The cell number in MSM broth plus glucose and cultivated for 36 h was 4.2 × 10⁷ CFU/mL, while that supplied with IAA was reduced to 3.7 × 10⁷ CFU/mL; this difference was statistically significant (^*p < 0.05). The above results indicated that IAA exerts a dual role on V. boronicumulans CGMCC 4969. In glucose-containing MSM medium, IAA inhibits the growth of cells in the logarithmic phase. In contrast, in medium without glucose, the degradation of IAA was enhanced, suggesting that IAA likely induces the expression of IAA-degrading enzymes in this strain.

We also examined the inhibitory effect of IAA on cell growth in nutrient-rich LB medium. In LB liquid medium containing IAA at concentrations of 0, 0.25, 0.5, and 1 mmol/L, the OD₆₀₀ of V. boronicumulans CGMCC 4969 cultured for 17 h were 3.56, 3.46, 3.37, and 3.33, while the OD₆₀₀ dramatically decreased to 1.17 and 0.23 (Fig. 3A) when the IAA concentration was increased to 2.5 and 5.0 mmol/L, respectively. The results also indicated that IAA inhibits the growth of V. boronicumulans CGMCC 4969 cultured under nutrient-rich medium.

Fig. 3.

Effect of different IAA concentrations on V. boronicumulans CGMCC 4969 growth, activity, and metabolite analysis. A OD₆₀₀ value of CGMCC 4969 after incubation for 24 h in LB medium supplemented with different concentrations of IAA; B relative activity of IAA degradation in quiescent CGMCC 4969 cells pre-cultured in LB medium supplemented with different concentrations of IAA for 24 h; C kinetic profile of IAA degradation by CGMCC 4969 resting cells; D HPLC chromatogram of the resting cell transformation product of IAA; red: phosphate buffer with 200 mg/L IAA; black: phosphate buffer with 200 mg/L IAA and CGMCC 4969; blue: phosphate buffer with only CGMCC 4969; E LC-MS of the metabolites

Effects of IAA on the IAA-degrading activity of V. boronicumulans CGMCC 4969

The IAA-degrading activity of V. boronicumulans CGMCC 4969 pre-cultured in LB medium with different IAA concentrations was examined. As shown in Fig. 3B, the IAA-degrading activity of the CGMCC 4969 cells pre-cultured with 0.5, 1, and 2.5 mmol/L IAA increased significantly, being 1.7, 3.0, and 2.5-fold higher than the control group, respectively. These results suggest that the addition of IAA in the cell culture period can enhance the IAA-degrading activity of V. boronicumulans CGMCC 4969 and IAA with a concentration of 1 mmol/L results in the highest IAA-degrading activity.

The IAA degradation kinetics of V. boronicumulans CGMCC 4969 (Fig. 3C) indicated that the bacterial cells in LB medium without IAA completely degraded IAA in 3.5 h, with a half-life of 1.44 h. The cells pre-cultured in LB medium with 1 mmol/L IAA completely degraded IAA in 1.5 h, with a half-life of 0.58 h.

Analysis of IAA metabolites and IAA metabolic pathway

As shown in Fig. 3D, a product peak P1 was observed in the degradation of IAA by V. boronicumulans CGMCC 4969, which was absent in the control group without V. boronicumulans CGMCC 4969. The retention times for the IAA and P1 peaks were 3.5 min and 2.9 min, respectively. P1 has the same retention time with the standard 3-hydroxy-anthranilate. The LC-MS analysis of P1 showed that P1 has a parent ion peak at m/z 152 ([M-H]) and two fragment ion peaks at m/z 136 ([M-NH2]) and 108 ([M-CHO2]) (Fig. 3E). Based on HPLC and LC-MS analysis, P1 was identified with 3-hydroxy-anthranilate, a new metabolite of IAA that has not been reported.

3-Hydroxy-anthranilate was also used as a substrate for biotransformation by V. boronicumulans CGMCC 4969. HPLC analysis showed that 3-hydroxy-anthranilate could be degraded and no metabolite was observed. Previous studies proved that IAA was degraded to anthranilate, which subsequently was metabolized to pyrocatechol (Leveau and Gerards 2008). In IAA degradation by V. boronicumulans CGMCC 4969, we propose that IAA may be initially degraded into anthranilate, which subsequently forms 3-hydroxy-anthranilate, and then it is further degraded to pyrocatechol (Fig. S1).

Function of the key iadC/D/E genes in response to IAA

Finkel et al. (2020) reported that V. paradoxus CL14 has a novel IAA-degradation operon iad. This operon also exists in the genome of V. boronicumulans CGMCC 4969 (Fig. 1). The corresponding genes of the iad operon have similarity ranging from 88.43 to 92.52% between V. paradoxus CL14 and V. boronicumulans CGMCC 4969. A distinctive feature of the iad cluster in V. boronicumulans CGMCC 4969 is the presence of a unique gene iadL (encoding penicillin G acylase, Protein ID:ATA57898.1) between iadK3 and iadK2, which is not found in V. paradoxus CL14. Its expression level was upregulated 1.91-fold under IAA induction (Table 2); this genetic element suggests potential structural specialization of the operon. To validate its functionality, we introduced the core genes iadC/D/E (accession number in Genbank database: ATA56151, ATA56150, ATA56149) individually and in combination into E. coli for expression. As shown in Fig. 4, only the simultaneous expression of iadD and iadE genes conferred E. coli Rosetta to rapidly degrade IAA when compared to the control group. Furthermore, the degradation of IAA was further enhanced when iadC was co-expressed with iadD/E. iadD and iadE respectively encode the β-subunit and α-subunit of an aromatic ring-hydroxylating dioxygenase. IadC belongs to a reductase protein containing a 2Fe-2S iron-sulfur domain. IadC and IadD/E composed a two-component dioxygenase system for IAA degradation.

Table 2.

Quantification and differential analysis of proteins associated with the iad gene cluster of V. boronicumulans CGMCC 4969

Gene	Protein ID	Protein	IAA/CK
iadK3	ATA56142.1	Extracellular ligand-binding receptor (Precursor)	4.56
iadL	ATA57898.1	Penicillin G amidase	1.91
iadK2	ATA56143.1	Extracellular ligand-binding receptor (Precursor)	4.50
iadJ	ATA56144.1	Short-chain dehydrogenase/reductase	2.87
iadI	ATA56145.1	Cyclase family protein	2.81
iadH	ATA56146.1	Short-chain dehydrogenase/reductase	3.35
iadG	ATA56147.1	Thiolase	1.61
iadF	ATA56148.1	AMP-dependent synthetase and ligase	2.30
iadE	ATA56149.1	Aromatic-ring-hydroxylating dioxygenase β subunit	1.50
iadD	ATA56150.1	Rieske (2Fe-2S) iron-sulfur domain	2.62
iadC	ATA56151.1	2Fe-2S ferredoxin-type domain	3.80
iadB	ATA56152.1	iorB indolepyruvate ferredoxin oxidoreductase, β subunit	3.50
iadA	ATA56153.1	iorA indolepyruvate ferredoxin oxidoreductase, α subunit	2.55
iadR	ATA56154.1	Transcriptional regulator, MarR family	1.41

Fig. 4.

Degradation kinetics of recombinant E. coli Rosetta strains containing different components of the V. boronicumulans CGMCC 4969 iad gene cluster. The concurrent expression of iadE and iadD genes in E. coli Rosetta resulted in the entire degradation of 200 mg/L IAA within 23 h. Meanwhile, when iadE, iadC, and iadD genes were all expressed at the same time in E. coli Rosetta, they could lead to the breakdown of 200 mg/L IAA within 19 h. In contrast, there was no significant degradation of IAA observed in E. coli Rosetta when iadC, iadD, and iadE expressed separately

Proteomics analysis of V. boronicumulans CGMCC 4969 in response to IAA

Proteomics analysis of V. boronicumulans CGMCC 4969 cultivated in LB medium supplemented with 1 mmol/L IAA and the control without IAA stimulation had been conducted and compared (Fig. S2A). Upon assessing proteins exhibiting more than a 1.2-fold difference in abundance (up-regulated or down-regulated) with a statistical significance of p < 0.05, 248 proteins were discerned in V. boronicumulans CGMCC 4969 cultivated with IAA as compared with the control, encompassing 143 up-regulated proteins and 105 down-regulated proteins (Fig. S2B-D).

iad operon coding proteins implicated in IAA degradation exhibited a substantial upregulation, with the upregulation times ranging from 1.41 to 4.56 (Table 2). Notably, the proteins most significantly elevated in expression were the branched-chain amino acid ABC transporter substrate-binding proteins, encoded by iadK3 and iadK2. In the presence of IAA, the expression levels of the two proteins relative to the control without IAA addition were 4.56 and 4.50-fold, respectively. PPI analysis further identified the branched-chain amino acid ABC transporter substrate-binding protein (IadK2, ATA56143.1) as a core hub protein, interacting with at least 20 other proteins, directly contributing to IAA uptake and transport (Fig. S3).

GO and KEGG enrichment analysis of differentially expressed proteins

Under the specified growth conditions, we conducted an analysis of the GO functions of CGMCC 4969 proteins according to three categories: biological process, molecular function, and cellular component. We quantified the differential proteins at the level of secondary GO functional annotations, as depicted in Fig. S4. Within the molecular function category, there was a noteworthy presence of 216 differentially expressed proteins (DEPs). Within the biological process category, metabolic pathways accounted for 56 DEPs, suggesting that IAA might trigger shifts in various metabolic pathways and activities, thereby enabling CGMCC 4969 to adapt to the changing conditions. In the realm of molecular function, a significant number—121 DEPs—were associated with catalytic activity. This implies that catalytic activity might be one of the key molecular mechanisms for addressing IAA, and these DEPs could partake in numerous vital biochemical reactions. Concerning cellular components, there were 47 DEPs within the cells and cell membranes. This reflects IAA’s potential impact on cell structure and functions such as cell membrane permeability, signal transduction, and material transport. Collectively, these outcomes suggest that bacterial cells may counter and assimilate to IAA by modulating the expression of catalytic enzymes, metabolic pathways, and cell structures.

Upon juxtaposing all the DEPs with the comprehensive GO function annotations derived from experimental identification, significant variances emerge under the two culture conditions. This comparison, as illustrated in the accompanying Fig. 5, highlights notable changes in crucial biological processes, including localization, transport, and the metabolism of aromatic amino acid families. Additionally, there is a conspicuous shift in molecular functions such as redox enzyme and transporter activities. The localization of proteins within the periplasmic space, outer membrane, and envelope also exhibits substantial alterations.

Fig. 5.

Enriched GO terms

KEGG enrichment analysis further confirmed that the DEPs of V. boronicumulans CGMCC 4969 predominantly aggregated in metabolic pathways under both IAA-supplemented LB and untreated LB culture conditions (Fig. 6). Notable enrichment was observed in pathways such as ABC transporters, quorum sensing, cofactor biosynthesis, benzoate degradation, and fatty acid synthesis. Specifically, 13 proteins related to quorum sensing, 12 proteins related to ABC transporters, and 9 proteins involved in benzoate degradation pathways were significantly upregulated. Conversely, 6 proteins associated with cofactor biosynthesis and 4 proteins involved in propionate metabolism pathways were significantly downregulated (Table 3). Detailed protein information can be found in Table S1 and S2.

Fig. 6.

Statistical map of KEGG pathway annotations for differentially expressed proteins in the group (Top20). Red represents metabolism, yellow represents environmental information processing, and blue represents cellular processes. The vertical coordinate is the name of the differentially expressed protein-containing pathway, and the horizontal coordinate indicates the number of differentially expressed proteins involved in the pathway

Table 3.

Up-regulated and down-regulated proteins of V. boronicumulans CGMCC 4969

Map_ID	Map_Name	Protein accession number
		Up-regulated
ko02024	Quorum sensing	ATA51874.1 ATA51875.1 ATA52143.1 ATA56139.1 ATA52145.1 ATA52142.1 ATA56137.1 ATA56136.1 ATA56142.1 ATA56143.1 ATA56138.1 ATA57166.1 ATA52144.1
ko02010	ABC transporters	ATA51874.1 ATA51875.1 ATA52143.1 ATA56139.1 ATA52145.1 ATA52142.1 ATA56137.1 ATA56136.1 ATA56142.1 ATA56143.1 ATA56138.1 ATA52144.1
ko00362	Benzoate degradation	ATA56157.1 ATA57522.1 ATA51899.1 ATA51882.1 ATA51877.1 ATA51881.1 A TA53551.1 ATA56131.1 ATA51878.1
ko01240	Biosynthesis of cofactors	ATA53544.1 CKY39_32335 ATA53020.1 ATA54951.1 ATA56134.1 ATA56144.1 ATA56133.1 ATA57189.1
ko00061	Fatty acid biosynthesis	ATA54951.1 ATA56134.1 ATA56144.1 ATA56133.1 ATA57166.1 ATA57189.1
		Down-regulated
ko01240	Biosynthesis of cofactors	ATA52721.1 ATA57786.1 ATA55673.1 ATA56418.1 ATA51988.1 ATA53366.1
ko00640	Propanoate metabolism	ATA53232.1 ATA55325.1 ATA54260.1 ATA55268.1

PPI analysis

In the ongoing PPI analysis, a comparison between the experimental group and the control group revealed several proteins with significantly upregulated expression and high connectivity. These proteins are marked in red in Fig. S3 for differentiation. Similarly, the significantly downregulated proteins are shown in blue. The metal-dependent hydrolase (ATA56137.1), ABC transporter ATP-binding protein (ATA56136.1), and branched-chain amino acid ABC transporter substrate-binding protein (ATA56143.1, IadK2) are among those interacting with at least 20 other proteins. Furthermore, these proteins are primarily involved in two significantly enriched metabolic pathways, namely the ABC transporter pathway and the quorum sensing pathway.

Impact of IAA on the expression levels of IAA-degrading genes in V. boronicumulans CGMCC 4969

The expression differences of IAA-degrading genes in V. boronicumulans CGMCC 4969 were analyzed using qPCR technology after being cultured in LB medium and LB medium with 1 mmol/L IAA for 17 h. As compared with the control, the expression levels of the iadE, iadD, and iadC were upregulated by 2.7, 2.4, and 5.0-fold, respectively, in the presence of IAA (Fig. S5). These results are in accordance with that of proteomics analysis.

Discussion

Plant-associated microorganisms modulate plant health and development through the production and degradation of the key phytohormone IAA. While approximately 80% of rhizosphere bacteria can produce IAA, the ability to degrade it is less common. To date, only a limited number of bacterial strains, including P. putida 1290, Paraburkholderia phytofirmans PsJN, Acinetobacter baumannii ATCC 19606, Enterobacter soli LF7, Caballeronia glathei DSM 50014, Bradyrhizobium japonicum E109, V. paradoxus CL14, Achromobacter xylosoxidans SOLR10, and Ac. insolitus AB2, have been confirmed as IAA degraders. Consistent with its role as an IAA degrader, we demonstrate here that V. boronicumulans CGMCC 4969 can utilize IAA as a sole carbon/nitrogen source for growth. Interestingly, while CGMCC 4969 produces IAA via the indole-3-acetonitrile pathway (Sun et al. 2018), exogenous IAA supplementation (> 1 mmol/L) in nutrient-rich LB medium significantly inhibited its growth (Fig. 3A). This inhibition likely stems from the metabolic burden imposed by high IAA concentrations under these conditions. Beyond its established role in regulating plant growth, IAA also functions as a signaling molecule within bacterial cells, modulating gene expression and physiology (Zúñiga et al. 2013; Conway et al. 2022). Our proteomic analysis provides direct evidence for this signaling role in V. boronicumulans CGMCC 4969, revealing that IAA exposure induces significant changes in the abundance of proteins involved in diverse metabolic pathways (ABC transporters, benzoate degradation, fatty acid biosynthesis, QS; Tables 3, S1, S2; Fig. 6). These proteomic shifts represent a bacterial response mechanism that likely influences plant-microbe interactions (Leveau and Gerards 2008).

The significance of bacterial production of phytohormones in plants has been a focal point for researchers over the years. However, investigations into the molecular mechanisms underlying the degradation capacity of V. boronicumulans CGMCC 4969 for IAA have only recently gained attention (Finkel et al. 2020; Sun et al. 2018; Conway et al. 2022; Li et al. 2022). A key finding of this study is the identification of 3-hydroxy-anthranilate as a novel intermediate in the IAA degradation pathway of V. boronicumulans CGMCC 4969. This metabolite, distinct from intermediates reported in the canonical iac (catechol) or iaa (2-aminobenzoyl-CoA) pathways, suggests a potentially unique catabolic route in this strain. In addition, we found that IAA can be used as the carbon and nitrogen source for V. boronicumulans CGMCC 4969, but inhibits V. boronicumulans CGMCC 4969 growth when other adequate nutrient sources are present (Whiteley et al. 2017; Fuqua et al. 1994). Intriguingly, pre-culturing CGMCC 4969 in LB medium supplemented with IAA (particularly 1 mmol/L) significantly enhanced the subsequent IAA degradation activity of its resting cells (threefold increase; Fig. 3B, C). This induction suggests that IAA itself acts as a signal upregulating its own degradation machinery. We hypothesized that this could involve (i) induction of specific IAA transporters accelerating uptake, or (ii) positive feedback regulation of the IAA-degrading enzymes. Proteomic analysis of IAA-exposed cells provided strong support for hypothesis (i), revealing the most significantly upregulated proteins to be branched-chain amino acid ABC transporter substrate-binding proteins (IadK2, IadK3; Table 2, Fig. S4), implicated in IAA uptake. Similar to the observation that the IAA-degrader P. putida 1290 shows chemotaxis towards IAA, our findings suggest that IAA may regulate the expression of relevant proteins in V. boronicumulans CGMCC 4969 to enhance its degradation ability (Whiteley et al. 2017; Fuqua et al. 1994).

Furthermore, the GO analysis demonstrated a significant enrichment of oxidation-related proteins and transporters. The KEGG analysis revealed substantial enrichment of metabolic pathways, particularly ABC transporter and benzoate metabolism. Interestingly, we also observed significant upregulation of proteins associated with community-sensing metabolism, cofactor synthesis, and fatty acid synthesis. Dynamic regulation of IAA degradation and synthesis enables bacterial adaptation to environmental changes. Crucially, this balance likely mediates CGMCC 4969’s interactions with plants, akin to IAA-metabolizing PGPR that promote crop growth (Zúñiga et al. 2013). Functional validation confirmed the central role of the iad operon in IAA degradation. Heterologous co-expression of iadD (encoding the Rieske domain) and iadE (encoding the dioxygenase β-subunit) conferred IAA degradation capability (Fig. 4). Degradation efficiency was further augmented by the additional expression of iadC (encoding a 2Fe-2S ferredoxin), suggesting IadC acts as an electron transfer component enhancing the activity of the IadD/E dioxygenase (Table 2, Fig. 4). This structure (IadC/D/E) aligns with known two-component dioxygenase systems and corroborates recent structural findings (Ma et al. 2023). The significant upregulation of the entire iad operon (Table 2) and key genes (Fig. S5) in response to IAA exposure in CGMCC 4969 underscores its critical function in modulating cellular IAA levels (Conway et al. 2022).

In conclusion, our study elucidates the complex interplay of IAA degradation, signaling, and metabolic regulation in the PGPR V. boronicumulans CGMCC 4969. We demonstrate context-dependent effects of IAA on growth, identify a novel catabolic intermediate (3-hydroxy-anthranilate), functionally characterize the core iadC/D/E degradation genes, and reveal extensive proteomic remodeling induced by IAA—particularly upregulation of transporters, QS components, and specific catabolic pathways. This multifaceted response, centered around the inducible iad operon, highlights the sophisticated mechanisms bacteria employ to manage IAA levels. Notably, while our study demonstrates IAA-induced upregulation of QS-related proteins (Table 3) and enzymes involved in fatty acid biosynthesis, whether the inhibition of cell growth by IAA and its effect on QS protein expression are directly mediated through signaling requires further investigation. Elucidating this mechanism could clarify how IAA modulates bacterial communication in the rhizosphere. Deciphering such regulatory networks is essential for understanding the ecological roles of IAA-metabolizing bacteria and harnessing their potential in plant-microbe interactions.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

GJJ and YPP designed research and analyzed data. YJY, YRC, SWH, and YJ conducted experiments. GJJ, YPP, GL, and DYJ wrote and revised the manuscript. All authors read and approved the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (Grant number 31970094), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Data availability

The data supporting the findings of this study are included within the article and/or its supplementary materials. Additional or raw data are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This article does not include any experiments involving human participants or animals conducted by any of the authors.

Competing interests

The authors declare no competing interests.