Background

Food production needs to increase by 60% to meet the demands of approximately 10 billion people around the world by 2050 [1]. The fast-growing human population requires an increased crop yield, along with a reduction in crop loss caused by crop pathogens and pests. An expert-based assessment of crop losses on an individual pathogen and pest basis for wheat, rice, maize, potato and soybean, as five major crops, globally indicated that the average of crop losses caused by pathogens and pests are about 22.5% for all five crops [2]. Wheat is the staple crop for an estimated 35% of the world population [3]. A critical approach to meeting the increased demand is better management of fungal diseases, which can be responsible for 15%–20% yield losses per annum, such as rusts, blotches and Fusarium head blight (FHB) [4]. For example, the annual average occurrence of FHB, which is mainly caused by Fusarium graminearum in China, affects more than 4.5 million hectares, approximately 20% of the total planted area of wheat, and has caused serious yield losses [5]. Furthermore, FHB pathogens contaminate grains with various mycotoxins, especially deoxynivalenol (DON), which poses a health threat to humans and livestock. Currently, chemical fungicides are still the most effective approach to control FHB. However, fungicide resistant F. graminearum isolates have been detected in fields after long-term intensive use of fungicides [6, 7]. Moreover, treatments with some fungicides at sub-lethal concentrations stimulate mycotoxin production [8,9,10]. There is an urgent need to develop and apply new approaches to control FHB and mycotoxin contamination.

Biological control by living microorganisms is considered to be a suitable, alternative strategy for the control of plant diseases. Several antagonistic microorganisms were identified as biocontrol agents (BCAs) to combat plant diseases and achieved comparatively good efficiency [11]. Moreover, more than 30 species of microbes have been reported to specifically inhibit the mycelial growth of F. graminearum, or/and provided successful control efficiencies against FHB and mycotoxin reduction under greenhouse and/or field conditions [12,4: Fig. S3a). The ORF2643 gene encodes a putative LysR-type DNA-binding transcriptional repressor, LrhA (Additional file 4: Fig. S3b). Its homologues were previously reported to repress biosynthesis of secondary metabolisms in Photorhabdus and Xenorhabdu [43,44,45]. The two identified genes were named purR and lrhA for further investigation.

To confirm the roles of PurR and LrhA on HA production, we constructed single deletion mutants, double deletion mutants, and corresponding complementation strains of the two genes. The mRNA expression levels of genes in the biosynthetic gene cluster AcbA-AcbJ [21]. Lipopeptides constitue a specific class of microbial secondary metabolites and harbor diverse biological functions, especially antimicrobial and anticancer activities [19, 20, 57,58,59,60,61,62]. Lipopeptides can be employed for pharmaceutical, food-related, agricultural, and environmental protection applications [63, 64]. In the present study, we demonstrated that increased HA yields in the ZJU23 deletion mutant result in control effects of fungal pathogens that are comparable to synthetic fungicides. This highlights its applicability for crop protection. HA belongs to the cyclic lipopeptides and its structure consists of a peptide ring of eight amino acids where a fatty acid chain, a dehydrobutyric acid, and a sugar moiety are attached [Quantification of HA via HPLC analysis

Quantitative analysis of HA was performed as previously described [13]. Briefly, 1 mL of the fermentation supernatant was collected and freeze-dried. The precipitate formed was resuspended with 1 mL methanol for HA extraction and the crude samples were subjected to HPLC analysis (Agilent Technologies 1100 Infinity) under the following conditions: C18 reversed-phase column [Agilent ZORBAX RX-C18 column (250 × 4.6 mm)] eluted with methanol/H2O (A/B) (1 mL/min, 30–90% A in 30 min, followed by 90–100% A in 10 min). The peak areas were used to quantify the production of HA according to the standard sample. To quantify HA more precisely, the extracted samples were analyzed by liquid chromatography-mass spectrometry (LC–MS).

Construction of transposon mutants and gene deletion mutants

A transposon mutagenesis library and gene deletion mutants of ZJU23 were constructed as described previously [13]. Briefly, transposon mutants were generated by conjugation of recipient ZJU23 resistant to rifampicin and donor E. coli SM10λ-pir. Each transposon was screened in an inhibition zone assay against F. graminearum. The selected transposons were re-sequenced by Bei**g Novogene Bioinformatics Technology Co., Ltd. to localize the insertion by comparing it with the wild type strain ZJU23. Gene deletion mutants were generated by using the λ-red recombinase method. The transformants were confirmed by PCR. Double mutant strains were generated using single mutants as a background strain as indicated. Complementation constructructions were generated as described previously [68]. The primers designed in this study for mutant strains and complementation constructions are listed in the Additional file 1: Table S4. The primer pair M13-F and M13-R were used for identification of pBBR-LrhA and pBBR-PurR plasmid construction [69].

RNA preparation and quantitative reverse transcription PCR (qRT-PCR)

For real-time quantitative PCR (RT-qPCR), all strains were cultured with HI medium at 25 °C and 180 rpm for 18 h. Total RNA purification was performed by using RNAprep Pure Cell/ Bacteria Kit (TIANGEN, DP430) and reverse transcription was done using HiScript II Q RT SuperMix for qPCR (+ gDNA wiper) (Vazyme, R223-01) according to the manufacturer's instructions. RT-qPCR was performed via qRT-PCR using ChamQ SYBR qPCR Master Mix (Vazyme, Q311-02). The experiments were performed in independent biological triplicates and 16S rRNA of ZJU23 was used as the internal control. The mRNA fold change was estimated by the threshold cycle (Ct) values of 2−(ΔΔCt). The primers used for qRT-PCR assays are listed in Additional file 1: Table S4.

Evaluation of biocontrol efficacy of the fermentation suspension

Biocontrol experiments with a fermentation suspension produced by ZJU23 in KB and ΔLrhA in HAI against gray mold caused by B. cinerea and Fusarium crown rot caused by F. pseudograminearum were conducted. The gray mold assay was performed as previously described [70]. The corresponding fermentation broth, pyrimethanil (50 mg/L) or water were sprayed on the tomato or apple surface. After 1 h air-drying, fresh mycelial plugs (6 mm in diameter) were inoculated. Pyrimethanil (50 mg/L) and clean water were used as fungicide treatment and negative control. Disease lesions were observed and measured 72 h after inoculation with the pathogenic fungus. The Fusarium crown rot assay was performed as described previously but with specific modifications [71]. F. pseudograminearum F303 conidia (1 × 106 CFU/mL) were mixed with soil at 1:10 (W/W) for three days. Then wheat seeds (cultivar: Jimai 22) were planted into conidia-inoculated soil. Non-inoculated soil was used as a control. At the seventh day, 50 mL of the fermentation suspension were sprinkled on the wheat root. Tebuconazole (150 mg/L) and clean water were used as fungicide treatment and negative control. After 21 days, the roots were washed and the browning areas of the wheat stem base were observed. The disease index (DI) was calculated as previously described [72] but with modifications. Five evaluation classes ranging from 0 to 4, were applied for the disease index. The disease index corresponds to the percentage of the browning length of the first stem node (0 = 0, 1 = 1–25%, 2 = 26–50%, 3 = 51–75%, and 4 ≥ 75%). The disease index was calculated as follows in Eq. (2):

$${\text{DiseaseIndex}}\left( {{\text{DI}}} \right)\, = \,\left( {\sum \, \left( {{\text{number of diseased plants in each class}}\, \times \,{\text{each evaluation class}}} \right)} \right)/\left( {{\text{total number of investigated plants}}\, \times \,{\text{the highest disease index}}} \right)\, \times \,{1}00$$
(1)

The control efficacy was calculated as follows in Eq. (3):

$${\text{Control efficacy}}\, = \,\left( {{\text{disease index of control}} - {\text{disease index of treatment}}} \right)/ \, \left( {\text{disease index of control}} \right)\, \times \,{1}00\%$$
(2)

Statistical analysis

All experiments in this study were repeated three times. Data presented are the mean ± standard errors. Differences between two groups were analyzed by Student’s t-test. Multiple comparisons were analyzed by one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) multiple-range test.