Experimental design
This study was conducted at Gaoqiao Scientific Research Base of the Hunan Academy of Agricultural Sciences in the city of Changsha (112°58′42ʺ E, 28°11′49ʺ N), Hunan Province in China in 2018 and 2019. The soil was sandy loam. The trial crop was watermelon cultivars zaojia 8424, which was provided by Xinjiang Farmer Seed Technology Co., Ltd. China. The dazomet was provided by Beijing Sino Green Agri-Biotech Co., Ltd. Six greenhouses (30 m × 6 m) with the same background, which were cultivated watermelon under monocropping system for five years, were selected. Three of them were treated with dazomet as three replicates, others were as control group. The routine cultivation managements in all the greenhouses were the same. Every March before transplanting the watermelon seedlings, 6 kg (98% C5H18N2S2) of dazomet were applied to one greenhouse, which was then tilled the soil by a rotary immediately after spraying. Controlling the depth of tillage soil 0–20 cm to ensure that dazomet was evenly mixed into the tillage layer. As soon as the soil temperature is above 8 °C, film mulching was set up to maintain the fumes of dazomet into the soil to kill most of the soil organisms, as well as to maintain the soil moisture content at approximately 40% for the germination and growth of weeds and pathogens. After 20 days, the film was uncovered and the greenhouse was kept ventilated. Then 15 days later, the watermelon seedlings nutrition bowl was cultivated and transplanted into the greenhouse. We planted the watermelon in the greenhouse with 50–60 cm plant spacing to enable pruning the climbing vines.
We designed six different sampling times as following: 1 (March 6th, 2018, before dazomet treatment), 2 (April 24th, 2018, watermelon seedling stage), 3 (May 3rd, 2018, Fusarium wilt symptom appearance), 4 (March 6th, 2019, before dazomet treatment), 5 (April 22th, 2019, watermelon seedling stage), 6 (April 29th, 2019, Fusarium wilt symptom appearance). For each replicate, nine independent soil samples within depth of 0–20 cm in the shape of “S” from each greenhouse were pooled. Three greenhouses within same treatment regarded as three independent replicates. DAZ represents dazomet treatment group and CK represents the control group without dazomet application but using same conventional planting system. All the soil samples from greenhouses were packed into sealed sterile bags separately and brought back to the laboratory. After removing the plant roots and stones from the samples, we sieved them with a 20-μm mesh, and then divided each sample into three parts. Two of them were placed in sterile centrifuge tubes, stored at − 80 °C for sequencing analysis and Q-PCR test. While the other was used for measuring the soil properties, stored at room temperature. We have collected total of 36 samples in six different sampling times.
Field disease investigation
The incidence of Fusarium wilt was calculated during the whole watermelon onset period (Started from plants with rotted, discolored root and the vascular bundle became brown until the whole plant died). The disease incidence (%) = (number of infected plants/total number of surveys) × 100%.
Determination of soil physical and chemical properties
The soil characteristics are listed in Supplementary Table S1. Soil pH was determined in a soil: water ratio of 1:2.5 (wt./vol) using a pH meter (BPH-220, Bell Instrument Equipment Co. Ltd., Dalian, LN, China). To extract the water-soluble salts from the soil, samples of 1 mm sieved and air-dried soil weighing 20.00 g were placed in a 250 ml Erlenmeyer flask, 100 ml of distilled water was added (water: soil ratio of 5:1). Then put it into a dry triangular bottle after shaking for 5 min which was used for the determination of salt. A total of 30 ml of the soil leachate was placed in 50 ml of burnout solution. The solution temperature was measured, and then the conductivity of the solution was determined using a conductometer. The soil organic matter (SOM) was determined by oxidation with potassium dichromate by DF-101S heat collecting constant temperature magnetic stirrer (Gongyi yuhua instrument Co., Ltd, Gongyi, HN, China). Total P and K and available P and K concentrations in the soil were determined by ICP-AES (PerkinElmer 2100DV, PerkinElmer, Waltham, MA, USA) after the soils were digested using concentrated HNO3-HF-HClO4. Total nitrogen (N) and available nitrogen (AN) in the soil were determined by the Kjeldahl method and the alkali diffusion method, respectively (China Agricultural Technology Extension Service Center, 2014).
Soil microbial diversity analysis
Total genomic DNA was extracted from the soil samples using the E.Z.N.A Soil DNA kit (Omega Bio-tech, Norcross, GA, USA) according to manufacturer’s protocols. The final DNA concentration and purity were determined using a Nanodrop 2000 UV–Vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and the DNA quality was checked by 1% agarose gel electrophoresis. Distinct regions of the 16S rRNA gene (V3-V4) and ITS1 were amplified by PCR (ABI Geneamp 9700, Applied Biosystems, Inc., Carlsbad, CA, USA) using specific primers (16S: 338F (5′-ACTCCTACGGGAGGCAGCAG-3′), 806R (5′-GGACTACHVGGGTWTCTAAT-3′); ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′), ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′)), separately. The PCRs were conducted using the following programme: 3 min of denaturation at 95 °C, 27 cycles of 30 s at 95 °C for ITS1 rRNA gene/35 cycles of 30 s at 95 °C for 16S rRNA gene, 30 s of annealing at 55 °C, and 45 s of elongation at 72 °C with a final extension at 72 °C for 10 min, 10 °C ∞. PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), followed by quantification using the QuantiFluor-ST kit (Promega, Madison, MI, USA) according to the manufacturer’s protocol.
Purified amplicons were pooled in equimolar amounts and sequenced (paired-end; 2 × 300 bp) on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) according to the standard protocols of the Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: SRP268536).
Quantitative detection of FON by real-time PCR
Distinct regions of the FON rRNA genes were amplified by PCR (Bio-Rad T100 Thermal Cycler, Bio-Rad Laboratories, Inc. Hercules, CA, USA) using specific primers (Fonq-F(5′- GTTGCTTACGGTTCTAACTGTGC -3′), Fonp1-R(5′- CTGGTACGGAATGGCCGATCAG -3′)) . Then the PCR products were used as templates to construct the standard curve of the fluorescence quantitative PCR (Bio-Rad iQ5 Optical Module, Bio-Rad Laboratories, Inc. Hercules, CA, USA) using primers (Fonq-F(5′- GTTGCTTACGGTTCTAACTGTGC -3′), Fonq-R(5′- GGTACTTGGAAGGAATTGTGGG -3′)). A 1446 bp DNA fragments containing the qPCR target sequence was amplified from soil DNA by conventional PCR (initial incubation at 94 °C for 4 min, followed by 18 cycles of 94 °C 40 s, 60 °C 40 s, 72 °C 70 s, and a final extension at 72 °C for 10 min). The PCR products were used as templates to construct the standard curve of the fluorescence quantitative PCR (reaction consisted of an initial incubation at 95 °C for 1 min, followed by 40 cycles of 95 °C 15 s, 60 °C 30 s, 72 °C 30 s). The fluorescence intensity was monitored every 0.5 °C between 65 °C-95°C to making standard melting curve13.
Data analysis
Raw FASTQ files were demultiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) The reads were truncated at any site receiving an average quality score < 20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded; (ii) exact barcode matching, primers were exactly matched, and reads containing ambiguous bases were removed; (iii) sequences with over 10 bp of overlap were merged according to their overlap sequence. The singletons were removed for further analyses. Operational taxonomic units (OTUs) were clustered with a 97% similarity cut-off using UPARSE Version 7.1 (http://drive5.com/uparse/) and chimeric sequences were identified and removed using UCHIME 14. The taxonomy of each 16S rRNA gene sequence was analyzed by the RDP Classifier algorithm against the Silva (SSU123) 16S rRNA database using a confidence threshold of 70%. The taxonomy of each ITS region sequence was analyzed by the RDP Classifier algorithm against the UNITE (8.0) ITS rRNA database using a confidence threshold of 70%.
The diversity analysis for the sequencing data were performed on the free online platform of Majorbio Cloud Platform (www.majorbio.com) based using the Qiime2 software15 (https://qiime2.org/). For example, the sobs index of rarefaction curve, alpha diversity (student’s t-test shannon index) and beta diversity of (NMDS, non-metric multidimensional scaling analysis) with ANOSIM statistical analysis to compare the composition between treatments on OTU level. Moreover, the significant difference of microbial community species in different sampling groups was tested by Kruskal–Wallis H test on OTU, phyla and genera level respectively. The FDR (Falsely Discovery Rate) and Tukey’s method16 were used to analyze the multiple test correction for p-value > 0.95. One-way ANOVA test was used to analyze significant differences of two groups. Differences between two groups were analyzed by student’s t test. Correlation heatmap analysis of the correlation coefficient between environmental factors and selected species was determined by MeV (Multi Experiment Viewer) software (http://mev.tm4.org).
Other statistical analysis was performed using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA). The figures of the microbial diversity indices and relative abundance of functional profiles were prepared using Microsoft Office 2010 (Microsoft Corporation, Redmond, WA, USA)and Adobe Illustrator CS5 (Adobe Systems Incorporated, San Jose, CA, USA) (https://www.adobe.com/cn/products/illustrator.html).
Source: Ecology - nature.com
