The Perception of Rhizosphere Bacterial Communication Signals Leads to Transcriptome Reprogramming in Lysobacter capsici AZ78, a Plant Beneficial Bacterium

The rhizosphere is a dynamic region governed by complex microbial interactions where diffusible communication signals produced by bacteria continuously shape the gene expression patterns of individual species and regulate fundamental traits for adaptation to the rhizosphere environment. Lysobacter spp. are common bacterial inhabitants of the rhizosphere and have been frequently associated with soil disease suppressiveness. However, little is known about their ecology and how diffusible communication signals might affect their behavior in the rhizosphere. To shed light on the aspects determining rhizosphere competence and functioning of Lysobacter spp., we carried out a functional and transcriptome analysis on the plant beneficial bacterium Lysobacter capsici AZ78 (AZ78) grown in the presence of the most common diffusible communication signals released by rhizosphere bacteria. Mining the genome of AZ78 and other Lysobacter spp. showed that Lysobacter spp. share genes involved in the production and perception of diffusible signal factors, indole, diffusible factors, and N-acyl-homoserine lactones. Most of the tested diffusible communication signals (i.e., indole and glyoxylic acid) influenced the ability of AZ78 to inhibit the growth of the phytopathogenic oomycete Pythium ultimum and the Gram-positive bacterium Rhodococcus fascians. Moreover, RNA-Seq analysis revealed that nearly 21% of all genes in AZ78 genome were modulated by diffusible communication signals. 13-Methyltetradecanoic acid, glyoxylic acid, and 2,3-butanedione positively influenced the expression of genes related to type IV pilus, which might enable AZ78 to rapidly colonize the rhizosphere. Moreover, glyoxylic acid and 2,3-butanedione downregulated tRNA genes, possibly as a result of the elicitation of biological stress responses. On its behalf, indole downregulated genes related to type IV pilus and the heat-stable antifungal factor, which might result in impairment of twitching motility and antibiotic production in AZ78. These results show that diffusible communication signals may affect the ecology of Lysobacter spp. in the rhizosphere and suggest that diffusible communication signals might be used to foster rhizosphere colonization and functioning of plant beneficial bacteria belonging to the genus Lysobacter.


INTRODUCTION
The soil is one of the largest microbial reservoirs on Earth, where millions of bacteria and fungi, and less frequently archaea, algae, and protozoa, interact with each other and the plants.
In particular, the rhizosphere, the soil compartment influenced by the root exudates, is a hot spot of microbes, as root exudates are a major carbon source for soil microorganisms and a driving force of their population density and activities (Raaijmakers et al., 2009).
Lysobacter spp. belonging to the Xanthomonadaceae family are commonly found in agricultural soils Postma et al., 2008;Choi et al., 2014) and especially in the plant rhizosphere. Indeed, they have been found in high abundance in the rhizosphere of maize (Schmalenberger and Tebbe, 2003; Xanthomonadaceae family also use diffusible factors (DFs) as diffusible communication signals; these signals are involved in the regulation of secondary metabolites biosynthesis and antioxidant activity (He et al., 2011;Qian et al., 2013;Zhou et al., 2013). In addition, plant-associated bacteria produce volatile organic compounds (VOCs) that are involved in communication and competition between physically separated soil microorganisms (Schmidt et al., 2015). Among VOCs, indole (IND) is a ubiquitous interkingdom signal that influences antibiotic resistance, motility, biofilm formation, and virulence and has the potential to be a diffusible communication signal (Lee and Lee, 2010). 2,3-Butanedione (BUT) and glyoxylic acid (GLY) are other VOCs mediating changes in gene expression related to motility and antibiotic resistance (Kim et al., 2013).
Based on this body of knowledge, it is conceivable that when Lysobacter spp. are applied to the rhizosphere, they will firstly perceive diffusible communication signals produced by the indigenous microbial community before establishing any physical interaction with other rhizosphereassociated microorganisms. The perception of these diffusible communication signals might lead to changes in their transcriptome, which in turn might ultimately lead to changes in Lysobacter spp. rhizosphere competence and their ability to control plant pathogens. Indeed, it has been shown that DSFs, DFs, and IND regulate the biosynthesis of the heat-stable antifungal factor (HSAF), a potent antifungal compound, and twitching motility in Lysobacter enzymogenes (Qian et al., 2013;Han et al., 2017;Su et al., 2017;Feng et al., 2019). However, with the only exception of the involvement of DSFs and AHLs in Lysobacter brunescens behavior (Ling et al., 2019a,b), a complete overview of the overall effect of diffusible communication signals in the ecology of Lysobacter spp. in the rhizosphere has not been described yet.

Genome Mining
AZ78 genome was mined to identify putative genes involved in cell-cell communication systems using nucleotide and protein sequence comparison. Genes from L. enzymogenes C3, Stenotrophomonas maltophilia (Sm) K279a, and Xanthomonas campestris pv. campestris (Xcc) ATCC 33913 T were aligned against AZ78 genome, using RAST (Aziz et al., 2008) to identify putative AZ78 genes responsible for diffusible communication signal synthesis, reception, and regulation using a cut-off of 1 × 10 −5 at amino acid level. Putative genes were analyzed with BLASTP (Johnson et al., 2008), and length >70 and identity >70% at amino acid level were used as threshold. Identified gene clusters encoding putative proteins involved in cell-cell communication systems in AZ78 were then used to mine the Lysobacter spp. genomes, following the methodology described above. All genomes were downloaded from the National Center for Biotechnology Information (NCBI) 1 (Supplementary Table 1). For the phylogenetic analyses, nucleotide sequences were aligned using ClustalW (Thompson et al., 1994). Evolutionary distances were assessed by applying Kimura's two-parameter model (Kimura, 2020); and the best phylogenetic trees were inferred by neighbor-joining method (Saitou and Nei, 1987) implemented in MEGA 7 (Kumar et al., 2016). Confidence values for nodes in the trees were generated by bootstrap analysis (Felsenstein, 1985) using 1,000 permutations of the data sets.

Assessment of Diffusible Communication Signal Production
Production of AHLs by AZ78 and Lysobacter spp. type strains was assessed by evaluating their ability to restore violacein production in Chromobacterium violaceum CV026 and/or to promote lacZ transcription in Agrobacterium tumefaciens NT1 (pZLR4) as 1 https://www.ncbi.nlm.nih.gov/ previously described (Cha et al., 1998;Steindler and Venturi, 2007). In brief, candidate strains were grown on NA close to each reporter strain to form a "T, " and the phenotypic change associated with the presence of AHLs was observed as a gradient with the most response observed at the meeting point of the two strains. Medium used in assays involving A. tumefaciens was supplemented with 1.6 µg/ml of X-Gal (5-bromo-4-chloro-3-indolyl β-D-galactopyranoside, Sigma-Aldrich). Likewise, the ability to release DSF was determined using the bacterial reporter strain Xcc 8523 pL6engGUS according to Slater et al. (2000). Briefly, Xcc 8523 pL6engGUS was grown in 10 ml of NYG (5 g/L of peptone, 3 g/L of yeast extract, and 20 g/L of glycerol) supplemented with 10 µg/ml of tetracycline to an optical density (OD 600 ) of 0.7. Cells were harvested by centrifugation and reconstituted in 1 ml of fresh NYG, added to 100 ml of cold NGY containing 1% BD Difco Noble Agar (BD Biosciences, Sparks, MD, United States), supplemented with 80 µg/ml of X-Glu (5-bromo-4-chloro-3-indolyl β-D-glucuronide sodium salt; Sigma-Aldrich), and plated into petri plates. Candidate strains were then pin inoculated and incubated for 48 h at 27 • C. The presence of a blue halo around the colony indicated DSF activity. Pseudomonas chlororaphis M71 (Puopolo et al., 2011) was used as an AHL-positive control, whereas Xcc 8004 was used as a DSF positive control (Barber et al., 1997). For each condition, five replicates were used, and the experiment was repeated.

Effect of Diffusible Communication Signals on Antimicrobial Activity
The effect of diffusible communication signals on AZ78 antimicrobial activity was evaluated on Rhizosphere Mimicking Agar (RMA) (Brescia et al., 2020). At first, preliminary experiments where AZ78 was grown on RMA amended with different concentrations of the selected compounds were carried out to select minimum effective concentrations-the lowest concentrations showing the highest effect-of diffusible communication signals. Thereafter, the final experimental design was made up of eight treatments (Supplementary Table 2). Inhibitory activity of AZ78 against P. ultimum was evaluated by using the classic dual-culture method as previously described . In brief, 10 µl of AZ78 cell suspension (1 × 10 8 CFU/ml) were spot-inoculated at 3 cm of the edge of a plate. After 48-h incubation at 27 • C, mycelium plugs (4 mm) were cut from the edge of 1-week-old P. ultimum plate, placed at 2.5-cm distance from AZ78, and incubated at 25 • C for 168 h. AZ78 activity against Rhodococcus fascians LMG 3605 was determined by spot-inoculating 10 µl of AZ78 cell suspension (1 × 10 8 CFU/ml) in the center of an RMA plate . After 48-h incubation at 27 • C, AZ78 cells were killed by exposure to chloroform vapor for 60 min . Dishes were aerated under a laminar flow for 60 min, overlaid with 4 ml of 0.45% agar phosphate-buffered saline (PBS) containing R. fascians LMG 3605 (1 × 10 7 CFU/ml) and incubated at 27 • C for 72 h. RMA dishes seeded only with P. ultimum or R. fascians LMG 3605 were used as control.
Pictures were obtained with Bio-Rad Quantity One software implemented in a Bio-Rad GelDoc Imaging system (Bio-Rad Laboratories, Hercules, CA, United States). Inhibitory activity was quantified by scoring P. ultimum or R. fascians LMG 3605 growth area (cm 2 ) using ImageJ 1.52a (Schneider et al., 2012) and calculated according to the formulas below: In all cases, treatments included five replicates, and experiments were repeated.

Evaluation of Diffusible Communication Signal Effect on Cell Growth
The effect of diffusible communication signals on AZ78 cell growth rate was assessed on 1/10 Tryptic Soy Broth (Oxoid) amended with each diffusible communication signal (Supplementary Table 2). AZ78 (starting concentration 1 × 10 7 CFU/ml) was grown at 27 • C on a 96-well plate (200 µl), and absorbance at 600 mm was recorded on a microplate reader (Synergy 2 Multi-Mode Microplate Reader, BioTek, Winooski, VT, United States). Non-inoculated media were used as blank. For each condition, five replicates were used. The experiment was repeated.

RNA Extraction
The AZ78 response to diffusible communication signals was evaluated on RMA, and the experimental design was made up of eight treatments in triplicate (Supplementary Table 2). Ten microliters of AZ78 cell suspension (1 × 10 10 CFU/ml) were spot-inoculated in the center of an RMA plate and incubated at 27 • C for 48 h. Plugs (7-mm diameter) were collected from the AZ78 macrocolonies, immediately frozen in liquid nitrogen, and stored at −80 • C. Frozen samples were processed according to Brescia et al. (2020), and total RNA was extracted using Spectrum Plant Total RNA Kit (Sigma-Aldrich). DNase treatment was performed with the RNase-Free DNase set (Qiagen, Hilden, Germany). RNA integrity and concentration were assessed using a 2200 TapeStation System (Agilent Technologies, Santa Clara, CA, United States) and a Qubit 4 Fluorometer (Thermo Fisher Scientific, Carlsbad, CA, United States) with Qubit RNA BR assay kit (Thermo Fisher Scientific), respectively (Supplementary Table 3).

Illumina Sequencing and Mapping to the Reference Genomes
Library construction and Illumina Sequencing were carried out at Fasteris (Plan-les-Ouates, Switzerland). Ribosomal RNA (rRNA) depletion was performed using the Ribo-Zero rRNA Removal Kits (Bacteria) (Illumina, San Diego, CA, United States). Complementary DNA (cDNA) libraries were synthesized using TruSeq Stranded mRNA Library Prep (Illumina, United States); they were multiplexed (two libraries per lane); and pairedend reads of 150 nucleotides were obtained using an Illumina HiSeq 4000 (Illumina), resulting in ∼7-42 million reads per sample (Supplementary Table 4). Raw sequences were deposited at the Sequence Read Archive of the NCBI under BioProject number PRJNA714393.
Sequence analysis was carried out using Omicsbox 1.3.11. 2 Illumina HiSeq data were assessed for quality using FastQC (Andrews, 2010). Raw reads for each sample were trimmed to increase overall quality using Trimmomatic 0.38 (Bolger et al., 2014). The resulting reads were aligned to AZ78 genome (Supplementary Table 1) using the STAR 2.7.5a (Dobin et al., 2013), and read counts were extracted from STAR alignments using HTSeq (Anders et al., 2015).

Identification of Differentially Expressed Genes and Functional Annotation of RNA-Seq
Genes with zero counts in all replicates were excluded from the analysis, and raw counts were normalized using the trimmed mean of M-values method (Robinson and Oshlack, 2010). Differentially expressed genes (DEGs) were identified using edgeR 3.28.0 ) using a p-value < 0.01 and a log fold change (FC) of at least onefold upregulation/downregulation as cut-off values. Venn diagrams summarizing DEG distribution were drawn with VennPainter (Lin et al., 2016). Hierarchical clustering and heatmaps were created with TreView3 (Saldanha, 2004).
The protein sequences of all predicted genes  were functionally annotated using Blast2Go 3 (Conesa et al., 2005). Default settings were applied, and a minimum E-value of 10 −5 was imposed as cut-off. DEGs were further annotated based on the NCBI gene description and classified in 20 functional categories.

Validation of RNA-Seq
First-strand cDNA was synthetized from 600 ng of purified RNA with SuperScript III Reverse Transcriptase (Invitrogen, Carlsbad, CA, United States) using random hexamers, according to manufacturer's instructions. qRT-PCRs were carried out with Platinium SYBR Green qPCR Super-Mix-UDG (Invitrogen, United States), and specific primers (Supplementary Table 5) were designed using Primer3 software (Untergasser et al., 2012). Primer specificity was assessed using PCR before gene expression analysis. qRT-PCRs were run for 50 cycles (95 • C for 15 s and 60 • C for 45 s) on a LightCycler 480 (Roche Diagnostics, Mannheim, Germany). Each sample was examined in three technical replicates, and dissociation curves were analyzed to verify the specificity of each amplification reaction. LightCycler 480 software, version 1.5 (Roche Diagnostics, Mannheim, Germany) was used to extract cycle threshold (Ct) values based on the second derivative calculation; and the LinReg software, version 11.0, was used to calculate reaction efficiencies for each primer pair (Ruijter et al., 2009). Relative expression levels were calculated according to the Pfaffl equation (Pfaffl, 2001) using AZ78 growing in RMA as calibrator. The housekeeping gene recA (AZ78_1089; Puopolo et al., 2016) was used as constitutive gene for normalization, as its expression was not significantly affected by growth media and conditions (Tomada et al., , 2017Brescia et al., 2020). The linear relationship between the RNA-Seq log 2 FC values and the qRT-PCR log 2 FC values of selected genes was estimated by Pearson's correlation analysis.

Statistical Analysis
Percentage values were arcsine square root transformed to normalize distributions and to equalize variances. Comparisons between repeated experiments of antimicrobial activity were done using two-way analysis of variance (ANOVA), and the data were pooled when no significant differences were found according to the F-test (p > 0.05). Data were analyzed using one-way ANOVA, and Tukey's test (α = 0.05) was used to detect significant differences. Statistical analyses were carried out using IBM SPSS Statistics for Windows, version 21.0 (IBM Corp, Armonk, NY, United States).

Cell-Cell Communication Systems in Lysobacter capsici AZ78 Genome
Putative rpf genes were found in the AZ78 genome (Table 1 and Supplementary Figures 1, 2). The rpfF/rpfC region (3,947,548-3,948,450 bp) was located far from the rpfG/rpfB region (857,114-859,152 bp) in AZ78 (Supplementary Figure 3a). DSF biosynthesis was confirmed by AZ78 ability to induce the glucuronidase activity in Xcc 8523 pL6engGUS like the control strain Xcc 8004 (Supplementary Figure 3b). As for VOCs, putative gene-encoding IND synthase and QseB/QseC system were found in AZ78 genome and in other Lysobacter spp. (Table 1 and Supplementary Figure 4). Homologues of the chorismatase needed for DF production and the LysR family transcription factor involved in the DF regulatory cascade were also found in AZ78 genome ( Table 1 and Supplementary Figure 5). luxR gene, responsible for the detection and response to AHLs, was also present in AZ78 (Table 1 and Supplementary Figure 5). LuxR was not associated with its cognate AHL synthase (LuxI) in AZ78 or in other Lysobacter species, with only exception of Lysobacter daejeonensis GH1-9 T having a luxI homologue. The absence of LuxI homologs was confirmed by the inability of AZ78 to restore β-galactosidase activity and violacein production in the reporter strains A. tumefaciens NT1 pZRL4 and C. violaceum CV026, respectively (Supplementary Figure 6). In contrast, L. daejeonensis GH1-9 T was able to restore violacein production in C. violaceum CV026, confirming the relation between the presence of luxI and AHL production.
Venn diagrams (Supplementary Figure 10) revealed overlaps among the seven conditions, but they did not identify genes modulated by all seven diffusible communication signals. Moreover, a heatmap (Figure 2) showed that GLY and BUT clustered together, and likewise for 3HBA, 4HBA, and AHL. Instead, LeDSF3 and IND grouped independently.
The Active Response of Lysobacter capsici AZ78 to Diffusible Communication Signals Functional annotation of AZ78 genes modulated by diffusible communication signals revealed that upregulated DEGs were mainly related to global metabolism; growth; RNA transcription, and degradation; and transport, phosphotransferase systems, and   Each treatment was subjected to pairwise comparison with the untreated control. | Log2-fold change| > 1 and p-value < 0.01 were chosen as cut-off values for differential gene expression.   (Table 3 and Supplementary Figure 11). Conversely, downregulated DEGs were mainly related to DNA metabolism. LeDSF3 and IND regulated several genes involved in transport, phosphotransferase systems, and secretion; global metabolism; RNA transcription and degradation; and growth ( Table 3 and Supplementary Figure 11). In addition, LeDSF3 affected translation and IND antagonism ( Table 3 and Supplementary Figure 11). Besides regulating genes related to global metabolism and transport, phosphotransferase systems, and secretion, GLY and BUT modulated a relevant number of genes classified into RNA transcription and degradation and translation, among which tRNA genes were mainly downregulated ( Table 3 and Supplementary Figure 11). Genes related to transport, phosphotransferase systems, and secretion and defense were modulated by 4HBA and AHL (Table 3 and Supplementary Figure 11).
Many genes ascribed to transport, phosphotransferase systems, and secretion were involved in drug and metal (particularly iron) transport (Supplementary Tables 6-12). Major facilitator superfamily (MFS) transporters were upregulated by LeDSF3 (Figure 3 and Supplementary Table 6). Resistance-nodulation-division (RND) efflux system genes were upregulated by diffusible communication signals, especially by AHL (Figure 3 and Supplementary Table 12). TonB-dependent receptors involved in the uptake of iron-siderophore complexes or vitamins were upregulated by LeDSF3 and downregulated by IND (Supplementary Tables 6, 7). Additionally, diffusible communication signals upregulated a relevant set of transcription regulators belonging to the AraC, ArsR, TetR, MerR, and MarR families (Supplementary Tables 6-12).
Diffusible communication signals also caused changes in the expression of genes involved in the reception and regulation of diffusible communication signals in AZ78 (Figure 3). Genes related to Type I secretion system (T1SS) were mostly upregulated by all diffusible communication signals, especially by LeDSF3 and IND (Figure 3 and Supplementary Tables 6, 7). Type II secretion system (T2SS) genes, such as gspG (AZ78_4200)  and gspH (AZ78_4201), were upregulated by LeDSF3, IND, GLY, and BUT (Figure 3 and Supplementary Tables 6-9).
On the contrary, the expression of Type III secretion system (T3SS) was downregulated by GLY and 3HBA (Figure 3 and Supplementary Tables 8, 10). Genes associated with type IV secretion system (T4SS) were downregulated by LeDSF3, IND, and GLY (Figure 3 and Supplementary Tables 6-8). ShlB from the two-partner secretion of Type V secretion system (T5bSS) was downregulated by BUT and 4HBA (Figure 3 and Supplementary Tables 9, 11). Finally, LeDSF3, IND, and BUT also regulated general secretory (Sec) pathways (Figure 3 and Supplementary Tables 6, 7, 9).

DISCUSSION
The behavior of bacterial species in polymicrobial communities mainly relies on communication systems (Venturi and Keel, 2016); and many secreted metabolites characterizing the cooperation among microorganisms, as well as antibiotics and toxins involved in microbial competition, are controlled by diffusible communication signals (Hibbing et al., 2010;FIGURE 4 | Schematic representation of Lysobacter capsici AZ78 response to diffusible communication signals. LeDSF3, 13-methyltetradecanoic acid; IND, indole; GLY, glyoxylic acid; BUT, 2,3-butanedione; 3HBA, 3-hydroxybenzoic acid; 4HBA, 4-hydroxybenzoic acid; AHL, N-acyl-homoserine lactones. Cornforth and Foster, 2013;Schuster et al., 2013). Diffusible communication signals are involved not only in signaling among self-cells but also in the detection of specific cues produced by other strains or species (Cornforth and Foster, 2013). In fact, many bacterial species have receptors for diffusible communication signals that are not produced by the same species, such as LuxR solos, and abundant twocomponent signaling systems (Cornforth and Foster, 2013). Genome mining results indicate that AZ78 and Lysobacter spp. may (at least) produce DSFs, IND, and DFs and perceive DSFs, IND, DFs, and AHLs. As a consequence, diffusible communication signals (mainly IND and GLY) influenced AZ78 antagonistic activity against the phyopathogenic Gram-positive bacteria R. fascians and the phyopathogenic oomycete P. ultimum. Different intraspecies, interspecies, and interkingdom diffusible communication signals might be used as cues for AZ78 to favor the regulation of molecular pathways related to cell persistence in the rhizosphere or for coercion (Figure 4). Thus, transcriptome profiles showed that diffusible communication signals might contribute to alert AZ78 against toxic compounds produced by other (micro)organisms in the rhizosphere by triggering the expression of gene-encoding efflux pumps that could actively extrude antibiotics, heavy metals, biocides, and solvents (Blanco et al., 2016). In addition, diffusible communication signals might help cells to escape from adverse conditions or to reach nutrients . For example, LeDSF3, GLY, and BUT upregulated the expression of genes related to the biogenesis of T4P involved in twitching motility. T4P-driven twitching motility is involved in a variety of physiological and social behaviors of a wide range of bacteria (Burrows, 2012;Zhang et al., 2012). For instance, twitching motility is required for colonization and infection of phytopathogenic fungi and oomycetes in Lysobacter spp. (Patel et al., 2011;Tomada et al., 2017), and it seems to be a DSF-dependent trait in L. brunescens and L. enzymogenes (Qian et al., 2013;Feng et al., 2019;Ling et al., 2019b). Interestingly, upregulation of T4P by GLY and BUT came along with increased antimicrobial activity, suggesting that microbial partners producing this kind of diffusible communication signals might encourage AZ78 to form a stable community and stimulate traits responsible for disease suppressiveness in soils. Moreover, GLY and BUT downregulated the transcription of tRNA genes, which might determine a decrease in the tRNA abundance in AZ78 cells. The decrease of tRNA abundance has been already studied in Escherichia coli (Potrykus and Cashel, 2008;Zhong et al., 2015). In this bacterial species, the decrease in tRNA abundance was associated with the ability to rapidly adapt to amino acid starvation (Potrykus and Cashel, 2008) and oxidative stress (Zhong et al., 2015). Thus, it is conceivable that the downregulation of tRNA genes in AZ78 cells upon perception of GLY and BUT might contribute to reduce the negative impact of environmental stresses in AZ78 cells. Nevertheless, this hypothesis needs to be proved in future works. In contrast, IND caused a dysregulation of T4P genes in AZ78 with possible losses of T4P functionality. Accordingly, IND decreases motility and biofilm formation in E. coli (Domka et al., 2006;Bansal et al., 2007;Lee et al., 2008;Mufti et al., 2015), probably as a manner to save energy and regulate growth dynamics (Nadell et al., 2008). In support of this hypothesis, IND diminished the AZ78 cell growth, although it was not possible to formulate a clear conclusion, as knowledge about IND functions is contrasting (Mueller et al., 2007(Mueller et al., , 2009Lee et al., 2009). In addition, Lysobacter cells might contribute to disease suppressiveness of soils by producing extracellular lytic enzymes and antibiotics. Previous findings showed that HSAF biosynthesis is positively regulated by DSF, IND, and 4HBA in Lysobacter spp. (Qian et al., 2013;Han et al., 2017;Ling et al., 2019b). Yet in AZ78, IND reduced antioomycete activity and downregulated the expression of the HSAF biosynthetic gene cluster. The downregulation of HSAF related genes by IND might be associated with the simultaneous upregulation of the LuxR solo (AZ78_4823), as previously reported in L. enzymogenes OH11, where overexpression of lesR (LuxR homologue) leads to a decrease in HSAF production (Qian et al., 2014;Xu et al., 2017). Moreover, IND upregulated several transcription regulators-among which various tetR repressors (Ramos et al., 2005), like AZ78_0770 and AZ78_3232that might be involved in HSAF biosynthesis regulation in AZ78, as found for LetR (a TetR family protein) in L. enzymogenes OH11 . The expression of HSAF biosynthetic cluster is also negatively regulated by cyclic-di-GMP (c-di-GMP) in L. enzymogenes OH11 . Interestingly, IND upregulated the expression of a diguanylate cyclase (AZ78_4062) that might be involved in c-di-GMP biosynthesis, implying a regulation role of c-di-GMP in HSAF production in AZ78. Besides producing secondary metabolites with antimicrobial activity, AZ78 might produce diffusible proteinaceous toxins and toxins deployed by contactdependent systems, such as Rhs toxins, which mediate growth inhibition of neighboring cells in Dickeya dadantii (Koskiniemi et al., 2013), or R-bodies, which are responsible for cell membrane disruption and toxins delivery in several bacterial genera (Raymann et al., 2013;Matsuoka et al., 2017). Thus, the downregulation of several rhs and reb genes required for Rhs toxins and R-bodies synthesis by LeDSF3 and IND might have contributed to lower AZ78 the antioomycete and antibacterial activities. Moreover, AZ78 downregulated signal transduction pathways in the presence of IND, such as TonB-dependent receptors, which play a key role in microbial competition with the uptake of iron-siderophore complex or vitamins (Braun, 1995).
Bacteria often use secretion systems to manipulate and kill rival bacterial and eukaryotic cells (Tseng et al., 2009;Green and Mecsas, 2016). Of those, T3SS, T4SS, and T6SS are related to the establishment of pathogenic interactions with microbial hosts in Lysobacter spp. Yang et al., 2020;Shen et al., 2021). Thus, modulation of genes related to secretion systems might result in gain/loss of ability to compete with other (micro)organisms (Ling et al., 2019a). In agreement with this statement, IND downregulated T4SS and decreased antimicrobial activity in AZ78. Downregulation of T4SS by IND might be related to the overexpression of diguanylate cyclases (e.g., AZ78_4062), responsible for c-di-GMP increase and T4SS inactivation in A. tumefaciens (McCarthy et al., 2019). However, T3SS was downregulated by GLY and 3HBA with no decrease in AZ78 toxic activity, suggesting that it was probably repressed to save energy under conditions where it does not provide an advantage, as found in Pseudomonas aeruginosa (Bleves et al., 2005), Vibrio harveyi (Ruwandeepika et al., 2015), and Yersinia pseudotuberculosis (Atkinson et al., 2011).

CONCLUSION
Overall, functional and transcriptome analysis of AZ78 shed light on the key role of signaling communication systems on the recruiting and shaping of AZ78 in the rhizosphere. Our results show that GLY and BUT might facilitate AZ78 rhizosphere competence and soil suppressiveness to plant pathogens. On the other hand, IND might prevent AZ78 from growing at high cell densities and decrease motility. Moreover, IND and LeDSF3 might decrease AZ78 ability to control phytopathogenic microorganisms. Manipulating diffusible communication signals levels in the rhizosphere could therefore provide efficient means to favor persistence and functioning of specific groups of beneficial bacteria, such as Lysobacter strains, at the rootsoil interface.

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm. nih.gov/, PRJNA714393.

AUTHOR CONTRIBUTIONS
AB and GP conceived the study, performed the experiments, analyzed the data, and conceptualized and drafted the manuscript. MP helped in the experimental setup, provided input, and proofread the manuscript. IP provided input and proofread the manuscript. All authors contributed to the article and approved the submitted version.

SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb. 2021.725403/full#supplementary-material Supplementary Figure 1 | Neighbor-joining trees illustrating the relationships across Lysobacter members based on nucleotide sequences of the rpfG and rpfB genes. Pseudomonas aeruginosa PAO1 was used as outgroup sequence. Locus tag numbers are given in brackets, GenBank accession numbers for the whole genome sequences are given in Supplementary Table 1.
Supplementary Figure 2 | Neighbor-joining trees illustrating the relationships across Lysobacter members based on nucleotide sequences of the rpfC and rpfF genes. Pseudomonas aeruginosa PAO1 was used as outgroup sequence. Locus tag numbers are given in brackets, GenBank accession numbers for the whole genome sequences are given in Supplementary Table 1 Antioomycete and antibacterial activity is expressed as the mean value and standard error variation (percentage) of the reduction of the mycelium growth area of P. ultimum and R. fascians compared to the control (L. capsici AZ78 in not supplemented media), respectively. (a,g) 13-methyltetradecanoic acid, (b,h) glyoxylic acid, (c,i) 2,3-butanedione, (d,j) 3-hydroxybenzoic acid, (e,k) 4-hydroxybenzoic acid, (f,l) mix of N-acyl-homoserine lactones. Each treatment included five replicates and data originating from two independent experiments were pooled. Different letters indicate significant differences according to Tukey's test (α = 0.05). Eventual minimum effective concentrations are given in Supplementary Table 2 Table 4).