Delayed Sowing Reduced Verticillium Wilt by Altering Soil Temperature and Humidity to Enhance Beneficial Rhizosphere Bacteria of Sunflower

Abstract

Sunflower Verticillium Wilt (SVW) caused by Verticillium dahliae is a significant threat to sunflower production in China. This soilborne disease is difficult to control. It has been observed that delayed sowing reduces the severity of SVW on different varieties and across various locations. Soil was collected from multiple locations with different sowing dates to understand the underlying biological mechanisms driving this phenomenon. The soil bacterial community was characterized through 16S rRNA gene amplicon sequencing performed on the Illumina MiSeq platform, followed by comprehensive bioinformatics analysis. Microsclerotia numbers in soil were detected using both NP-10 selective medium and quantitative polymerase chain reaction (qPCR). By delaying the sowing date, the number of microsclerotia in soil and the biomass of V. dahliae colonized inside sunflower roots were reduced during the early developmental stages (V2-V6) of sunflowers. Amplicon sequencing revealed an increased abundance of bacterial genera, such as Pseudomonas, Azoarcus, and Bacillus in soil samples collected from delayed sowing plots. Five bacterial strains isolated from the delayed sowing plot exhibited strong antagonistic effects against V. dahliae. The result of the pot experiments indicated that supplying two different synthetic communities (SynComs) in the pot did increase the control efficiencies on SVW by 19.08% and 37.82% separately. Additionally, soil temperature and humidity across different sowing dates were also monitored, and a significant correlation between disease severity and environmental factors was observed. In conclusion, delayed sowing appears to decrease microsclerotia levels by recruiting beneficial rhizosphere bacteria, thereby reducing the severity of SVW.

Keywords: SynComs; antagonistic effect; disease severity; sowing date.

Figure 1

Correlation between soil temperature, moisture, and disease severity. (A) Soil temperature changes from first (1 May) to last sowing date (10 June); (B) Soil humidity changes with delayed sowing date. (C) Soil pH (acidity and alkalinity) changes with delayed sowing date; differences between treatment means were analyzed using one-way ANOVA, followed by Tukey’s HSD test (p < 0.05). Error bars represent standard deviation. The three points on each bar represent the mean values of three replicates. (D) Heatmap illustrating correlation between different environmental factors and disease index. DI, disease index; Temp., temperature; Hum., humidity. Correlation analysis was performed to evaluate the relationships between soil temperature, moisture, and disease severity using Pearson’s correlation coefficient. Graphs were generated using OriginPro 2021 and GraphPad Prism 8, and statistical significance is indicated by different letters. Numbers and circle sizes represent correlation coefficients and strength, separately. Negative sign represents a negative correlation.

Figure 2

Correlations between microsclerotia number in rhizosphere soil and different sowing dates of sunflowers. (A) Number of microsclerotia in the rhizosphere soil when sunflowers reached V2 (fourth leaf) growth stage at different sowing date plots. (B) Number of microsclerotia in the rhizosphere soil when sunflowers reached V6 (eighth leaf) stage at different sowing date plots. (C) Number of microsclerotia in the rhizosphere soil when sunflowers reached R1 (early budding) stage at different sowing date plots. (D) Number of microsclerotia in rhizosphere soil when sunflowers reached R5 (mid-blooming) stage at different sowing date plots. Box plot illustrates the distribution of data, with the median represented by the central line in each box. The interquartile range (IQR), spanning from the first quartile (Q1) to the third quartile (Q3), captures the central 50% of values. Whiskers extend to smallest and largest values within 1.5 times the IQR from Q1 and Q3. Data points outside this range are displayed as outlier plots showing median and interquartile range of each treatment group. Statistical differences were analyzed using one-way ANOVA, followed by Tukey’s HSD test (p < 0.05). Different letters mean significant differences among groups (p < 0.05).

Figure 3

Quantification of biomass of V. dahliae in rhizosphere soil using qRT-PCR at different developmental stages at location 3. (i) Different growth stages of sunflower: V2 stage = fourth leaf stage; V6 stage = eighth leaf stage; R1 stage = early budding period; R5 stage = mid-blooming period. Treatment means were compared using one-way ANOVA, followed by Tukey’s HSD test (p < 0.05). All statistical analyses were performed in SPSS (version 22.0, IBM Corp., Armonk, NY, USA). Different letters denote significant differences among treatments (p < 0.05).

Figure 4

Quantification of the biomass of V. dahliae inside sunflower roots using qRT-PCR at different growth stages at location 3; V2 stage (fourth leaf stage), V6 stage (eighth leaf stage), R1 (early budding period) stage, and R5 (mid-blooming period). Treatment means were compared using one-way ANOVA, followed by Tukey’s HSD test (p < 0.05). Different letters denote significant differences among treatments (p < 0.05).

Figure 5

Quantification of V. dahliae biomass in the sunflower, detected by qRT-PCR in response to sunflower growth stages in a greenhouse. (i) V2 stage, fourth leaf stage; V6 stage, eighth leaf stage; R1 stage, early budding period; R5 stage, mid-blooming period; (ii) root, stem, and disc (capitula) represent plant sampling organs. Treatment means were compared using one-way ANOVA, followed by Tukey’s HSD test (p < 0.05). Different letters denote significant differences among treatments (p < 0.05).

Figure 6

Analysis of species composition in different rhizosphere soil of sunflower under different sowing dates. (A). Community barplot analysis:Relative abundance of dominant bacteria at phylum classification level in different groups; (B). Venn focused analysis: number of unique and shared species in different groups. Species were determined based on operational taxonomy unit (out) levels.

Figure 7

Principal coordinates analysis (PCoA) of sunflower rhizosphere soil under different sowing dates analyzed based on Bray–Curtis distance at the operational taxonomy unit (out) level in the field.

Figure 8

Community analysis at the genus level of sunflower rhizosphere soils under different sowing dates in the field.

Figure 9

Bacterial species variability in sunflower rhizosphere soils across different combinations of sowing dates and growth stages. (A) Bar Plot of Multi-Species Differential Abundance Analysis. A bar chart was used to illustrate differences in the mean relative abundance of the same species across different groups, with significance levels (p-values) indicated by asterisks to denote statistically significant differences. This visualization provides a clear representation of the species’ significant differences among multiple groups. The Y-axis represents species names at a given taxonomic level, while the X-axis shows the mean relative abundance of the species across different groups. Bars of different colors correspond to distinct groups. The rightmost section displays the P-values, with significance levels indicated as follows: * 0.01 < p ≤ 0.05. (B) Co-occurrence Network of Microbial Taxa Associated with Different Soil Samples. The co-occurrence network illustrates the relationships between microbial taxa at the genus level (green nodes) in two soil samples, S1_V6 (red node) and S5_V6 (blue node). Red lines indicate significant co-occurrence interactions. The size and connectivity of each node reflect the number of associations with other taxa. The network highlights the shared and unique microbial genera associated with each sample.

Figure 10

Phylogenetic tree of 16 isolated bacterial strains constructed based on 16S rDNA sequences.

Figure 11

Effect of different SynComs treatments on the growth of sunflowers and disease index of SVW. (A) Statistical chart of disease grade of sunflower by different SynCom treatments. DI (Diseae Index); (B) Effect of different SynCom treatments on sunflower growth. Data were analyzed by one-way ANOVA, followed by Tukey’s HSD test for multiple comparisons (p < 0.05) using SPSS software (version 22.0). Error bars represent the standard deviation. Different lowercase letters indicate significant differences (Tukey’s multiple range test) (p < 0.05).