Hfq modulates system-wide regulatory networks controlling biocontrol property in Lysobacter enzymogenes

Abstract

Lysobacter enzymogenes is a beneficial soil bacterium renowned for its potent biocontrol properties, primarily attributed to antimicrobial secondary metabolites such as HSAF and WAP-8294A2, as well as specialized secretion systems. In this study, we investigate the post-transcriptional regulatory roles of the RNA chaperone Hfq in L. enzymogenes OH11 using an integrated multi-omics approach, combining transcriptomic, proteomic, and RNA-binding data. Our comprehensive analysis reveals that Hfq systemically regulates central metabolism and coordinates biocontrol-associated processes, including antimicrobial biosynthesis and secretion systems. Notably, Hfq extensively modulates multiple regulatory pathways involved in HSAF biosynthesis, a well-studied compound with potential applications in combating fungal and oomycete diseases. Additionally, we identify a set of Hfq-associated small RNAs. Through target prediction, we inferred that many of these sRNAs likely influence cellular functions and stress responses, reinforcing Hfq's role as a global regulator of bacterial physiology. These findings provide a systems-level understanding of Hfq's regulatory mechanisms in L. enzymogenes, offering valuable insights for optimizing its biocontrol potential.

Headnote

ARTICLE INFO

Keywords:

Lysobacter enzymogenes

Hfq

Multi-omics

Biocontrol property

Small RNA

ABSTRACT

Lysobacter enzymogenes is a beneficial soil bacterium renowned for its potent biocontrol properties, primarily attributed to antimicrobial secondary metabolites such as HSAF and WAP-8294A2, as well as specialized secretion systems. In this study, we investigate the post-transcriptional regulatory roles of the RNA chaperone Hfq in L. enzymogenes OH11 using an integrated multi-omics approach, combining transcriptomic, proteomic, and RNA-binding data. Our comprehensive analysis reveals that Hfq systemically regulates central metabolism and coordinates biocontrol-associated processes, including antimicrobial biosynthesis and secretion systems. Notably, Hfq extensively modulates multiple regulatory pathways involved in HSAF biosynthesis, a well-studied compound with potential applications in combating fungal and oomycete diseases. Additionally, we identify a set of Hfq-associated small RNAs. Through target prediction, we inferred that many of these sRNAs likely influence cellular functions and stress responses, reinforcing Hfq's role as a global regulator of bacterial physiology. These findings provide a systems-level understanding of Hfq's regulatory mechanisms in L. enzymogenes, offering valuable insights for optimizing its biocontrol potential.

1. Introduction

Lysobacter enzymogenes is a non-flagellated, plant-associated bacterium that has emerged as a potent biocontrol agent due to its distinctive antimicrobial capabilities. This bacterium produces a set of antifungal and antibacterial secondary metabolites, including the heat-stable antifungal factor (HSAF) [1] and WAP-8294A2 [2]. Among these, HSAF has been the most extensively studied. HSAF is a polycyclic tetramate macrolactam (PoTeM) that exhibits broad-spectrum activity against filamentous fungi and oomycetes [3-5], proving effective in controlling various plant diseases caused by fungal and oomycete pathogens [6], while being non-toxic to plants and animals [4,7-9]. In addition to these metabolites, Г. enzymogenes employs specialized secretion systems, including the flagellar type III secretion system (FT3SS), type IV secretion system (T4SS) and type VI secretion system (T6SS), which contribute to its competitive advantage. Notably, FT3SS differs from those in flagellated bacteria as it does not play participate in flagellar biosynthesis, owing to the absence of the flagellin-encoding gene (fliC) [10]. Instead, FT3SS has evolved to secrete antifungal toxins and promote twitching motility via type IV pili [10,11]. The T4SS and T6SS are pivotal mechanisms for contact-dependent competition. The unique T4SS functions as a dual-purpose weapon, directly killing or weakening competing bacteria by delivering lethal or disruptive effectors [12,13], while also cooperating with other beneficial soil bacteria through the delivery of non-toxic effectors [13]. The T6SS facilitates contact with pathogenic fungi, enabling the delivery of toxic effectors into fungal cells to suppress infections and protect plants [14].

Hfq is a conserved RNA-binding protein that has been extensively studied and recognized as an RNA chaperone. It facilitates the binding of small RNAs (sRNAs) to target mRNAs, thereby modulating their stability or translation in response to environmental signals [15-17]. Additionally, Hfq has been shown to bind directly to mRNAs independently of sRNA mediation, affecting mRNA translation or degradation [18-21]. Notably, in Gram-positive bacteria, Hfq's involvement in sRNA-mRNA interactions has been reported in only a few species, such as Listeria monocytogenes [22] and Clostridioides difficile [23]. While its role in Gram-positive bacteria may be primarily limited to mRNAs, Hfq remains a crucial regulator of stress responses [24-26]. Furthermore, Hfq in teracts with rRNA [27,28], tRNA [29], and DNA [30], influencing various cellular processes such as ribosome biogenesis, protein synthesis accuracy, DNA compaction and replication, and protein-protein in teractions, thus expanding its regulatory roles. While Hfq is widely conserved across bacterial species, its functions have been most exten sively characterized in relation to virulence, stress adaptation, and metabolic flexibility. In previous study, we identified Hfq as a regulator of WAP-8294A2 biosynthesis and extracellular chitinase secretion in L. enzymogenes OH11 [31]. However, a systems-level understanding of its regulatory network remains elusive. Furthermore, to date, no sRNAs have been identified or characterized in L. enzymogenes OH11, and the interactions between Hfq and sRNAs remains largely unexplored.

In this study, we employ comprehensive multi-omics analyses to systematically investigate Hfq's regulatory roles in L. enzymogenes OH11. Our results demonstrate that Hfq orchestrates a complex regu latory network that modulates carbohydrate metabolism, antimicrobial production, and secretion systems, highlighting its critical role in the biocontrol capabilities of L. enzymogenes. Additionally, we identify a set of Hfq-associated putative sRNAs and predict that they have a broad impact on cellular functions and stress response, further establishing Hfq as a global modulator of bacterial physiology. This study provides new insights into the post-transcriptional regulatory mechanisms in L. enzymogenes and offers potential strategies for enhancing its biocon trol efficacy.

2. Materials and methods

2.1. Construction of bacterial strains

The bacterial strains used in this study are listed in Table S1, and the primers for strain construction are also provided in Table S1. L. enzymogenes mutant strains were generated using a two-step allelic exchange method. For chromosomal 3xFLAG-tagging, the 3xFLAG epitope was fused to the C-terminus of the protein by PCR, and the resulting construct, along with the upstream and downstream frag ments, was integrated into the pEX18Gm plasmid. The constructed vectors were electroporated into L. enzymogenes cells, and homologous recombinants were initially selected with gentamicin. Subsequent recombination events were selected using sucrose. The modified strains were confirmed by Sanger sequencing.

2.2. Transcriptome and proteome analysis

The wild-type L. enzymogenes OH11 and the Δhfq mutant were pre- cultured overnight in LB medium, then transferred to 20 % TSB me dium and cultured to the log phase. Each group was cultured indepen dently in triplicate. For transcriptome analysis, cells were collected for RNA extraction and transcriptome sequencing, which were performed by Guangzhou Gene Denovo Biotechnology Co. Ltd. Raw reads were further filtered. Clean reads were then mapped to OH11 reference genome, reads mapped to ribosomal RNA were removed. The retainted reads were aligned with the reference genome, and gene expression levels were calculated using RSEM [32]. The gene expression level was further normalized by using FPKM method to eliminate the influence of different gene lengths and sequencing depth. The edgeR package [33] was used to identify differentially expressed genes (DEGs) among sam ples with fold change >2 and FDR <0.05. For proteome analysis, cells were collected for protein extraction and quantification using the iTRAQ/TMT method at Guangzhou Gene Denovo Biotechnology Co. Ltd. Proteins with a fold change >1.2 and P < 0.05 were considered signif icantly differentially expressed. This threshold was selected because protein levels exhibit greater stability than RNA levels due to the buffering effect [34]. Using a higher differential threshold might miss differentially expressed proteins (DEPs) with low fold changes. Therefore, this study adopted this widely used proteomics standard to enhance DEP capture [35-37].

2.3. RIP-seq of L. enzymogenes Hfq-3xFLAG

Bacterial cells were cultured in 20 % TSB and collected at the log phase. RNA co-immunoprecipitation and data analysis were performed at Wuhan Igenebook Biotechnology Co., Ltd. Briefly, the procedure involved cell lysis and RNA fragmentation, followed by the immuno precipitation of RNA-protein complexes using Anti-FLAG M2 Magnetic Beads (Sigma, M8823). RNA was then purified and reverse transcribed. The samples underwent end repair and adaptor ligation before PCR enrichment and sequencing. RIP-seq data analysis begins with quality assessment, followed by trimming of sequences. Reads are then mapped to the reference genome, and the resulting SAM files are sorted and deduplicated. Peak calling to identify signal distribution is performed with MACS2 [38], and peak and motif annotations are generated using bedtools [39] and HOMER [40], respectively.

2.4. Analysis of metabolites

To analyze HSAF, the bacterial culture, grown in 20 % TSB, was acidified by adding 4 µ L of concentrated hydrochloric acid per milliliter of culture fluid, followed by the addition of an equal volume of ethyl acetate and gentle shaking. After phase separation, the upper layer was carefully transferred to a centrifuge tube, evaporated, and re-dissolved in methanol. HPLC analysis was then conducted using a mobile phase of acetonitrile and water, with separation achieved on an Agilent SB-C18 column (250 mm × 4.6 mm), and detection at a wavelength of 318 nm. To analyze carbohydrate metabolism-related metabolites, log-phase bacterial cells were subjected to metabolite detection using an ultra- high performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS) system (ExionLC™ AD UHPLC-QTRAP® 6500+, AB SCIEX Corp., Boston, MA, USA) at Novogene Co., Ltd.

2.5. qRT-PCR

RNA was extracted using the MolPure TRIeasy Plus Total RNA Kit (Yeasen Biotechnology). Reverse transcription was performed using the Hifair III 1st Strand cDNA Synthesis SuperMix (Yeasen Biotechnology). qPCR was conducted to analyze RNA abundance using a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems) and HieffqPCR SYBR Green Master Mix (Yeasen Biotechnology), according to the manufac turer's instructions. The primers used are listed in Table S1. The experiment was conducted in triplicate and repeated three times. Data were analyzed using the 2-ΔΔCT method.

2.6. sRNA prediction, annotation and prediction of target mRNA

Following quality control and ribosomal RNA depletion, small RNAs (sRNAs) were predicted from the RNA-seq data using the APERO algo rithm [41]. For functional annotation, sRNA sequences were queried against the Rfam database [42]. The retained reads were aligned to the reference genome, and sRNA expression levels were quantified using RSEM [32]. Differential sRNA expression analysis was performed with DESeq2 [43], applying thresholds of fold change ≥2 and adjust P < 0.05 for significance. Putative mRNA targets were defined as regions ±100 bp surrounding CDSs, and sRNA-mRNA interactions were predicted using IntaRNA [44]. ClusterProfiler [45] were used to conduct func tional enrichment.

3. Results

3.1. Integrated multi-omics analysis reveals Hfq-associated regulatory networks in L. enzymogenes OH11

To systematically characterize the regulatory roles of Hfq in L. enzymogenes OH11, we integrated transcriptomic (RNA sequencing, RNA-seq), proteomic (isobaric tags for relative and absolute quantitation, iTRAQ), and RNA-binding (RNA Immunoprecipitation sequencing, RIP-seq) datasets. RNA-seq comparing wild-type (WT) and Ahfq strains identified 1047 differentially expressed genes, while iTRAQ analysis of WT vs Ahfq detected 364 differentially expressed proteins (Fig. 1 A). RIP-seq data from a C-terminal 3 x FLAG-tagged Hfq strain mapped 350 high-confidence peaks to 296 genes, with 51 % localized to coding sequences (CDS), 21 % to 5' untranslated regions (5'UTRs), and 14 % to 3'UTRs (Fig. 1A and B; Table S2), suggesting that Hfq interacts with both coding and noncoding regions during regulation. Comparative analysis revealed multi-omics overlaps: 11 genes/proteins exhibited consistent changes across all three datasets, while 59 genes exhibited changes in RNA abundance, and their RNAs are bound by Hfq, and 16 genes displayed both translational changes and Hfq-binding (Fig. 1A), implying post-transcriptional regulatory modes. Notably, 210 RIP-seq peaks mapped to genes with no significant differential expression in RNA-seq or iTRAQ may represent binding sites that do not influence gene expression under the growth conditions employed in our analysis.

Functional enrichment of all differentially expressed genes and tar gets highlighted functions associated with the bacterial outer mem brane, suggesting their crucial role in overcoming nutrient scarcity by facilitating the transport of essential molecules across membranes. Additionally, this enrichment indicated that Hfq may play a key role in regulating signaling receptor expression, potentially facilitating envi ronmental adaptation (Fig. 1C). Furthermore, enrichment analysis of both upregulated and downregulated differentially expressed genes revealed that Hfq knockout disrupts bacterial metabolism, including the TCA cycle and organic acid metabolism (Fig. S2C and D). In response, the cell appears to activate various catabolic pathways and synthesizes protective compounds to gather resources (Fig. S2A and B). Notably, T6SS activity showed significant upregulation, indicating enhanced protein export for competition or defense (Fig. S2 B). It should be noted that the enrichment results presented here (which are marked with gray shading over the term names in Fig. S2 A-C) and those for the RIP-seq- associated genes represent cases where P < 0.05 but P.adjust >0.05, lacking strong statistical significance. Despite this limitation, the enrichment of RIP-seq-associated genes may still suggest potential roles of Hfq in nucleic acid metabolism, such as DNA recombination and rRNA and tRNA modification (Fig. S1). These enrichment patterns collectively establish Hfq as a global regulator of stress responses in L. enzymogenes OH11.

3.2. Hfq coordinates carbohydrate metabolism in L. enzymogenes OH11

Carbohydrate metabolism underpins bacterial growth by supplying energy and biosynthetic precursors. Our enrichment analysis reveals significant regulatory roles of Hfq in the carbohydrate metabolism of L. enzymogenes OH11 (Fig. S2). In particular, several enzymes in the tricarboxylic acid (TCA) cycle, glycolysis, and other metabolic pathways show notable RNA and protein levels modulation upon Hfq knockout (Fig. 2A). Specifically, it was found that the levels of glycerate-3P, glycerate-2P, isocitrate, and acetyl-CoA were significantly upregulated in the hfq knockout strain, while the level of Pyruvate decreased. These f indings confirm that Hfq plays a role in regulating carbohydrate metabolism.

At the glucose phosphorylation step, glkA and glkB exhibit con trasting changes, with glkA downregulated and glkB upregulated in the Δhfq strain at the transcript level, but with no accompanying protein expression changes, suggesting a compensatory mechanism to maintain glucose utilization. Downstream, translational suppression of gapA and pgk may impede glycolytic flux. In the TCA cycle, key enzymes such as icd, sucC, sucD, and mdh were downregulated at both RNA and protein levels in the Δhfq strain, indicating Hfq's essential role in maintaining the activity of the TCA cycle. Furthermore, the Lpd enzyme, critical in connecting glycolysis and the TCA cycle, exhibited downregulation at both the RNA and protein levels. As these key enzymes in glycolysis and the TCA cycle are significantly altered, the reduced efficiency in energy production could explain the slower growth phenotype observed in Ahfq strains [31]. In addition to central carbon metabolism, the analysis of the pentose phosphate (PPP) and Entner-Doudoroff (ED) pathways reveals suppression of enzymes at the translational level, despite unchanged translational levels.

RIP-seq identified direct Hfq binding to transcripts of metabolic genes, including pgl (ED pathway), edd (ED pathway), sucC (TCA cycle), aceB (glyoxylate shunt), ppsA (gluconeogenesis), and acsA (acetate assimilation) (Fig. 2). However, many differentially expressed genes lacked RIP-seq peaks, suggesting that Hfq may also regulate carbohydrate metabolism genes indirectly.

3.3. НА regulates biocontrol functions in L. enzymogenes ОН11

The biocontrol efficacy of L. enzymogenes relies on its multifaceted antimicrobial arsenal, including antifungal HSAF, antibacterial WAP8294A2, and secretion systems (FT3SS, T4SS and T6SS) mediating pathogen suppression [46]. Elucidating Hfq's regulatory control over antimicrobial metabolite biosynthesis and secretion machinery enhances our understanding of microbial interactions and provides insights for optimizing OH11 as a biocontrol agent.

WAP-8294A2 is a cyclic lipodepsipeptide with potent activity against Gram-positive bacteria [2]. Its biosynthesis in L. enzymogenes OH11 is controlled by a 12-module nonribosomal peptide synthetase (NRPS) cluster, which includes two large NRPS genes (wapsl and waps2) responsible for assembling the cyclic depsipeptide scaffold [2] (Fig. 3A). Adjacent genes encode an MbtH-like protein (ORF1), a taurine catabolism dioxygenase (ORF4) and resistance/transport-associated proteins (ORF5-ORF10) [2] (Fig. ЗА). In our previous study, deletion of hfq increased WAP-8294A2 production alongside elevated transcription of waps1 [31], which is consistent with the RNA-seq data presented here, though the increase was not significant (Fig. 3B). Proteomics revealed increased ORF1-ORF5 protein levels in the Ahfq strain (Fig. 30), although some increases were below the differential expression threshold. Intriguingly, orfé-orf10 (putative resistance/transport genes) exhibited decreased RNA abundance in the Ahfq strain (Fig. ЗВ) despite unchanged protein abundance (Fig. 3C). This regulatory dichotomy suggests Hfq represses the RNA levels of the biosynthetic genes (orf1-orf4) and a putative efflux transporter (orf5) while activating other resistance/transport genes (orf6-orf10), suggesting that Hfq fine-tunes WAP-8294A2 synthesis through exerting opposing controls over biosynthetic genes.

The antifungal PoTeM HSAF in Г. enzymogenes ОН11 is biosynthesized by a 10-gene cluster, with lafB encoding a hybrid polyketide synthase (PKS)/NRPS that catalyzes the scaffold structure [1] (Fig. 3D). Four adjacent genes (ORF2-ORF5) encode redox enzymes that are crucial for forming the cyclic structures of the HSAF molecule. Prote omics revealed significant upregulation of LafB and most ORF-encoded proteins in the Δhfq strain (Fig. 3D), despite downregulation RNA levels of orf8 and orf9. HPLC quantification confirmed increase in HSAF yield in Δhfq compared to WT, consistent with elevated WAP-8294A2 production [31] (Fig. 3E and F). These data demonstrate that Hfq acts as a repressor of OH11's two major antimicrobial secondary metabolites.

Secretion systems, including the FT3SS, T4SS and T6SS, are crucial for the ecological fitness of L. enzymogenes OH11 [9]. FT3SS facilitates microbial targeting via type IV pilus (T4P)-driven twitching motility and protein export, while T4SS executes targeted bacterial killing [9]. The FT3SS has evolved to secrete antifungal proteins, such as proteases and glucanases, instead of flagella [10]. It secretes antifungal weapons to target fungi like S. cerevisiae [10]. FT3SS components, including the ATPase FliI, are also essential for motility via Type IV Pili (T4P), likely influencing PilA secretion [11]. The coordination between secretion and motility that supports the predatory behavior of OH11 involves the FT3SS, which facilitates microbial targeting through type IV pilus (T4P)-driven twitching motility and concurrent protein export [9,46]. The T4SS and T6SS enable contact-dependent microbial competition. The T4SS kills competing bacteria by directly transferring toxic effector proteins into them [12]. The T6SS inhibits fungal growth through contact-dependent delivery of effectors into fungal cells [14]. Both the T4SS and T6SS operate without relying on diffusible antibiotics or metabolites, instead requiring direct cell-cell contact. T4SS and T6SS are crucial for OH11's biocontrol function, eliminating bacterial competi tors and protecting host plants from fungal infection. In Δhfq, prote omics revealed significant upregulation of FlgI (Fig. 3G), which encodes the P-ring protein. RNA-seq data showed increased expression of fliR, while RIP-seq analysis identified a direct interaction of Hfq with fliI and f liH mRNA (Fig. 3G). fliH was previously considered absent in OH11 [10] but identified here via resequencing and alignment (Fig. S3). FliR is a transmembrane export gate component, while both FliI and FliH are cytoplasmic ATPase ring components. The export gate works with the cytoplasmic ATPase ring for energy transduction to mediate proton-driven secretion. Both FliR and FliI were confirmed critical for T4P-driven twitching motility, and FliI was confirmed essential for toxin secretion [11]. T4SS functions by directly delivering effectors into other bacteria, suppressing competitors in the complex soil microbiome [12]. In the hfq mutant, iTRAQ analysis revealed translationally upregulated VirB2 (pilin subunit), whereas RNA-seq data indicated downregulation of virB8 (inner membrane scaffold-encoding gene) RNA levels (Fig. 3G). Notably, virB10 was identified to bind Hfq, with a significant increase in its translation (Fig. 3G), suggesting that Hfq may post-transcriptionally activate this energy sensor to regulate T4SS. T6SS mediates contact-dependent antifungal activity by translocating toxic effectors into fungal cells [14]. In Δhfq, expression of most T6SS components was significantly upregulated. Among these, the spike protein VgrG2, inner tube component Hcp, and sheath component TssC were identified as direct RNA targets of Hfq.

3.4. Hfq modulates the biosynthetic regulatory components of HSAF

HSAF is a potent antifungal compound with a unique mode of action [4,7,8] and promising applications, though its inherently low yield [47] and the challenges associated with its chemical hinder its broader use [48]. Consequently, extensive research has focused on deciphering the regulatory mechanisms governing HSAF biosynthesis, aiming to enhance its production in L. enzymogenes. Our integrated multi-omics analysis of L. enzymogenes OH11 reveals that Hfq broadly modulates a network of previously characterized regulatory factors that converge on the PHSAF promoter, controlling the transcription of the co-transcribed HSAF biosynthetic gene cluster (Fig. 4A). Notably, these genes, including the pivotal backbone gene lafB (encoding a PKS/NRPS hybrid), are co-transcribed, meaning that regulation of lafB transcrip tion mirrors the overall transcriptional activity of the entire cluster.

Deletion of hfq results in reduced mRNA levels of several positive regulators, including lysR, cdgL, clp, lspE, and clpP (Fig. 4B). Specifically, lysR and clpP exhibited reduced mRNA and protein levels in the Δhfq strain (Fig. 4B), suggesting a direct or indirect role of Hfq in stabilizing their transcripts or enhancing their translation. LysR, a LysR-type tran scriptional activator, binds 4-hydroxybenzoate (4-HBA) and directly activate the PHSAF promoter [49], while ClpP activate the lafB tran scription indirectly [50]. In contrast, cdgL and clp show decreased mRNA levels but unchanged protein levels (Fig. 4B), indicating potential post-transcriptional compensatory mechanisms. CdgL, a c-di-GMP re ceptor, dissociates from LysR upon c-di-GMP binding, thus alleviating its activation of HSAF biosynthesis [51], while Clp directly binds the lafB promoter to enhance its transcription [52,53]. Conversely, Hfq posi tively regulates the expression of hcp and TBDR7, with both genes showing increased mRNA and protein levels in the Δhfq strain (Fig. 4B). Hcp, a component of the type VI secretion system (T6SS), interacts with Clp to promote HSAF synthesis [54], while TBDR7, a TonB-dependent receptor, is essential for HSAF production, with its deletion nearly abolishing biosynthesis [55]. Additionally, RIP-seq identified that Hfq directly binds to the mRNA transcripts of LysR and Hcp (Table S2), further supporting its role in post-transcriptional regulation of these key components in the HSAF biosynthetic pathway. This interaction sug gests that Hfq may modulate the stability or translation efficiency of LysR and Hcp mRNAs, thereby fine-tuning their expression levels and influencing HSAF biosynthesis.

Among the negative regulators, lesR exhibited reduced mRNA levels but unchanged protein levels in the Δhfq strain (Fig. 4C), indicating that Hfq may stabilize lesR transcripts or enhance their translation. LesR is a solo LuxR-type protein that negatively regulates HSAF biosynthesis by modulating downstream regulatory factors, particularly Clp [56,57]. Furthermore, Hfq modulates the expression of wspR [58], lchP [53], and rpfG [53], which participate in c-di-GMP signaling. WspR showed increased mRNA and protein levels in the Δhfq strain (Fig. 4D). Phos phorylation of WspR inhibits HSAF biosynthesis by enhancing c-di-GMP production and disrupting the WspR-CdgL-LysR signaling complex [58]. On the other hand, lchP exhibited decreased mRNA levels but increased protein levels, while rpfG showed reduced mRNA levels with no signif icant change at the protein level (Fig. 4D), suggesting that Hfq may regulate these factors post-transcriptionally. LchP enhances HSAF biosynthesis by interacting with Clp, stimulating its phosphodiesterase activity to reduce intracellular c-di-GMP levels and enhance Clp-mediated transcription of the HSAF operon [53]. RpfG participates in HSAF regulation through interactions with three hybrid two-component system (HyTCS) proteins [53,59].

In summary, our analysis demonstrates that Hfq regulates the expression of multiple transcriptional regulators of HSAF biosynthesis, including both positive and negative regulators, as well as factors which exert regulatory effects through self-phosphorylation or protein in teractions, while lafB transcription remained relatively unaffected in the Δhfq strain (Fig. 4A). This observation suggests that the combined ef fects of Hfq on multiple regulators (analyzed here or remained un known) may offset individual changes, resulting in a net neutral impact on lafB transcription. However, the marked upregulation of LafB protein levels provides a plausible explanation for the increased HSAF produc tion observed in this mutant. This discrepancy between mRNA and protein levels suggests that Hfq may also regulate HSAF biosynthesis at the post-transcriptional level, potentially through interactions with sRNAs.

3.5. Regulatory landscape of Hfq-associated sRNAs in L. enzymogenes OH11

Hfq, a conserved RNA chaperone, orchestrates post-transcriptional regulation by facilitating interactions between sRNAs and their target mRNAs, thereby modulating gene expression in response to environ mental stimuli. To comprehensively understand the Hfq-dependent regulatory network in L. enzymogenes OH11, we applied the APERO al gorithm for predictions of sRNA. APERO, a robust algorithm designed for the precise identification of bacterial sRNAs from RNA-seq data, le verages paired-end sequencing to infer transcript boundaries with high accuracy [41]. APERO identified 3931 putative sRNAs (Table S3), classified by genomic context relative to annotated protein-coding se quences (Fig. S4B). Of these, 583 sRNAs resided in intergenic regions (IGRs), 6 of which were experimentally validated using quantitative real-time PCR (qRT-PCR) (Fig. S4B, Fig. 5), showing expression trends consistent with RNA-seq data, supporting the robustness of APERO-based predictions. The observed concordance between sRNA downregulation in the hfq knockout strain by both qRT-PCR and RNA-seq aligns with Hfq's established role in protecting sRNAs from degradation, including by ribonucleases PNPase and RNase E [60]. Notably, RIP-seq analysis identified 8 peaks within intergenic regions that aligned with our predicted intergenic sRNAs (Table S2). Functional annotation via Rfam [42] classified 96 sRNAs into known families, including 23 tRNAs, 19 riboswitches such as c-di-GMP_I_riboswitch and Glycine riboswitch, and 5 Csr/Rsm-class sRNAs (Fig. S4C, Table 54). The majority of sRNAs lacked homology to known RNA families, suggesting novel, species-specific regulatory elements.

To investigate Hfg-dependent sRNA regulatory networks, we analyzed differentially expressed sRNAs between Ahfq and wild-type strains and predicted their mRNA targets using IntaRNA [44] (Table S5). ClueGO [61] enrichment analysis of the upregulated and downregulated sRNA target genes provided an overview of the functional categories significantly influenced by Hfg-associated sRNA regulation. The ClueGO analysis revealed that putative sRNA target genes are involved in a wide range of cellular functions and stress responses (Fig. S5A and B). Further analysis of the top 20 enriched functional terms (Fig. 6) indicated that the upregulated sRNA target genes are associated with transport processes for various substrates, including metal ions, organic molecules, essential cofactors and phosphate-related metabolic pathways, as well as bacterial secretion systems. In contrast, the downregulated sRNA target genes are enriched in substrate-specific transport machinery for amino acids, carbohydrates, monocarboxylic acids and micronutrients, alongside biosynthetic and catabolic pathways, amino acid activation, and protein synthesis. Furthermore, to investigate whether Hfg-associated sRNAs are linked to the biocontrol property of 1. enzymogenes ОН11, we performed target prediction analysis to identify sRNAs potentially interacting with mRNAs of the WAP-8294A2 and HSAF biosynthetic gene clusters (Table S6). Although experimental validation remains necessary, this work establishes a foundation for future investigations into Hfq-mediated sRNA-mRNA interactions in OH11's biocontrol function.

4. Discussion

In this study, we provide a comprehensive analysis of Hfq's regulatory roles in L. enzymogenes OH11 using an integrated multi-omics approach that combines transcriptomics, proteomics, and RNAbinding assays. This approach allows us to extend our previous understanding of Hfq's function [31], shifting from its regulation of specific phenotypes to a more global perspective, as highlighted by recent advances in multi-omics studies across multiple bacterial species [62-65]. Our findings establish that Hfq is a critical global regulator in this bacterium, modulating a wide range of cellular processes, including carbohydrate metabolism, antimicrobial biosynthesis, secretion systems, and post-transcriptional regulatory networks.

The extensive role of Hfq in the regulation of central metabolism has been highlighted by global transcriptome/proteome analyses of other hfq mutants such as those of Escherichia coli [66], Pseudomonas aeruginosa [67] or Yersinia pestis [68]. In this study, we found that several key enzymes involved in carbohydrate metabolism (including the TCA cycle, glycolysis, the PPP pathway, and the ED pathway) are suppressed at various levels following Hfq deletion. At the same time, the levels of some carbohydrate-related metabolites also changed. These findings provide deeper insights into the potential mechanisms behind the significant reduction in bacterial growth rate observed upon hfq knockout in our previous study [31].

Hfq is also an important global regulator involved in secondary metabolites. Mutation of Hfq leads to a reduction in the production of the secondary metabolite 2,4-DAPG in Pseudomonas fluorescens 2P24 [69] and Pseudomonas protegens H78 [70]. Meanwhile, even for the same secondary metabolite, the regulatory outputs of Hfq may be completely different in different bacteria. While it negatively regulates pyoluteorin (PLT) biosynthesis in P. protegens FD6 [71] and P. aeruginosa M18 [72], it positively controls the production of antifungal metabolites (including PLT, pyrrolnitrin, HCN, and 2,4-DAPG) in P. protegens H78 [70]. In our previous study and this study, we demonstrated that the deletion of Hfq in OH11 will increase the production of antibacterial secondary metabolite WAP-8294A2 [31] and antifungal secondary metabolite HSAF respectively. This differential regulation of secondary metabolites production suggests that НЮ may employ complex, context-dependent mechanisms to modulate antimicrobial/antifungal biosynthesis across different bacterial strains. Notably, the production of secondary metabolites in L. enzymogenes is strongly influenced by both environmental stress and nutrient availability [73], with compounds such as HSAF and WAP-8294A2 being exclusively produced under nutrient-limited conditions [2,74], while HSAF has also been shown to protect 1. enzymogenes from oxidative damage [75]. We hypothesize that the negative regulation of Hfq on the production of both HSAF and WAP-8294A2 could be due to the cost associated with the production of secondary metabolites. Hfq may play a role in resource allocation under environmental stress, prioritizing bacterial growth and other processes over the synthesis of secondary metabolites. Nevertheless, the mechanism of Hfg-mediated coordination between stress adaptation and secondary metabolite production remains to be elucidated.

Previous studies revealed that Hfq is tightly related with secretion systems in different species of bacteria and the regulatory effect includes both positive and negative regulation. Hfq shows a positive regulation of secretin systems in Salmonella typhimurium [76] and Yersinia pseudotuberculosis [77] and deletion of Hfq leads to defective type III secretion system (T3SS) and attenuated virulence. However in some other bacteria, like EHEC [78], Shigella sonnei [79] and P. aeruginosa [80], Hfq shows a negative regulation of T3SS. These findings suggest that the role of Hfq in T3SS regulation can be distinct in different bacteria. In addition to T3SS, other secretion systems such as T4SS [81] and T6SS [82] have also been reported to be modulated by НК. In this study, we demonstrated that Hfq modulates three secretion systems (FT3SS [10,11], T4SS [12], and T6SS [14]) in 1. enzymogenes OH11. Interestingly, most of components of the recently characterized T6SS in OH11 are upregulated in Hfq mutant strain, highly indicating that Hfq negatively regulate the T6SS in L. enzymogenes OH11, which is conversely to the situation in other plant-associated bacteria Pectobacterium carotovorum [82], P. protegens H78 [70] and FD6 [71].

Leveraging Hfq's role as an RNA-binding protein, we employed the APERO [41] to predict potential sRNAs in OH11-a previously unex plored area. Functional enrichment analysis of putative Hfq-associated sRNA targets provides initial insights into their regulatory roles, laying the foundation for future studies on sRNA-mediated post- transcriptional regulation in L. enzymogenes.

In this study, we observed that Hfq in L. enzymogenes binds prefer entially to coding sequences rather than non-coding regions of mRNAs. Similar results were also observed in a study examining the interaction between Hfq and mRNA in Pseudomonas stutzeri A1501 that Hfq can bind to the CDS regions of up to 50 % of mRNAs [65]. These results could be possible since direct binding of Hfq to the coding sequence of mRNAs independently of sRNAs has been documented in both E. coli [83] and Salmonella [84]. Another possible reason accounting for the data could be resulted from the technique that we employed. In this study, we employed RIP-seq to perform a comprehensive analysis of the Hfq interactome. While this technique significantly simplifies such in vestigations by enriching full-length RNAs, thus negating the need for prior knowledge of non-model strains, it does have certain limitations. Specifically, this methodological feature renders it impossible to pre cisely pinpoint the binding site location, unlike CLIP-seq, which inten tionally shortens bound RNAs to enhance the identification of binding regions around the protein [67]. Consequently, some mRNA sequence containing both coding sequences and non-coding regions of mRNAs could be included.

Despite advances, certain limitations must be acknowledged. Tech nologically, omics-based experiments are subject to some degree of inherent inaccuracy or incomplete analysis due to missing annotations [85], and algorithms used to predict sRNAs and their targets do not guarantee complete accuracy. Strand-specific RNA-seq may be required for further validation the strand orientation of putative sRNAs. From a theoretical standpoint, some conclusions drawn in this study, such as Hfq's regulation of key genes, may require additional molecular biology experiments for further validation. Moreover, the regulatory effects of Hfq on secretion systems and other processes will require further phenotypic confirmation.

CRediT authorship contribution statement

Xinyi Cheng: Writing - original draft, Visualization, Validation, Software, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. Conceptualization. Wenhui Chen: Supervision, Software, Methodology, Investigation, Formal analysis. Data curation. Yangyang Zhao: Writing - review & editing, Supervision, Methodology, Investigation, Formal analysis. Yancun Zhao: Resources, Project administration, Funding acquisition. Feng-quan Liu: Writing - review & editing, Visualization, Supervision, Resources, Project administration, Methodology, Investigation, Conceptualization. Gaoge Xu: Writing - review & editing, Validation, Supervision, Resources, Investigation, Funding acquisition, Data curation, Conceptualization.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used DeepSeek in order to improve language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study was supported by Nanjing Funding Program for Post doctoral Research (No. 35012102), Jiangsu Funding Program for Excellent Postdoctoral Talent (No. 2022ZB771), National Natural Sci ence Foundation of China (No. 32372622), Jiangsu Province Agricul tural Science and Technology Independent Innovation Fund (No. CX(22) 5003) and the earmarked fund for China Agriculture Research System (CARS-28).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.synbio.2025.08.013.

Sidebar

Peer review under the responsibility of Editorial Board of Synthetic and Systems Biotechnology.

Received 5 May 2025; Received in revised form 20 August 2025; Accepted 27 August 2025

Available online 29 August 2025

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Footnote

* Corresponding author.

E-mail address: [email protected] (G. Xu).