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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

70088
10.1038/s41598-024-70088-0
Article
Response and disease resistance evaluation of sorghum seedlings under anthracnose stress
Chen Songshu 1
Zhao Zhi 1
Liu Xiaojuan 1
Li Kuiyin 12
Arif Muhammad 1
Zhang Beiju 1
Dong Lili 1
Wang Rui 1
Ren Mingjian mxren@gzu.edu.cn

1
Xie Xin ippxiexin@163.com

1
1 grid.443382.a 0000 0004 1804 268X Guizhou Branch of National Wheat Improvement Center, Guizhou Key Laboratory of Propagation and Cultivation On Medicinal Plants, Key Laboratory of Agricultural Microbiology, College of Agriculture, Guizhou University, Guiyang, 550025 Guizhou China
2 https://ror.org/009jy0c86 grid.488144.5 0000 0004 7417 3852 Anshun University, Anshun, 561000 Guizhou China
20 9 2024
20 9 2024
2024
14 2197828 5 2024
13 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Sorghum is the world’s fifth-largest cereal crop, and anthracnose (Colletotrichum sublineola) is the main disease affecting sorghum. However, systematic research on the cellular structure, physiological and biochemical, and genes related to anthracnose resistance and disease resistance evaluation in sorghum is lacking in the field. Upon inoculation with anthracnose (C. sublineola) spores, disease-resistant sorghum (gz93) developed a relative lesion area (RLA) that was significantly smaller than that of the disease-susceptible sorghum (gz234). The leaf thickness, length and profile area of leaf mesophyll cells, upper and lower epidermal cells decreased in the lesion area, with a greater reduction observed in gz234 than in gz93. The damage caused by C. sublineola resulted in a greater decrease in the net photosynthetic rate (Pn) in gz234 than in gz93, with early-stage reduction due to stomatal limitation and late-stage reduction caused by lesions. Overall, the activities of superoxide dismutase (SOD) and catalase (CAT), the content of proline (Pro), abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), and gibberellic acid (GA3), are higher in gz93 than in gz234 and may be positively correlated with disease resistance. While malondialdehyde (MDA) may be negatively correlated with disease resistance. Disease-resistant genes are significantly overexpressed in gz93, with significant expression changes in gz234, which is related to disease resistance in sorghum. Correlation analysis indicates that GA3, MDA, peroxidase (POD), and disease-resistance genes can serve as reference indicators for disease severity. The regression equation RLA = 0.029 + 8.02 × 10−6 JA–0.016 GA3 can predict and explain RLA. Principal component analysis (PCA), with the top 5 principal components for physiological and biochemical indicators and the top 2 principal components for disease-resistant genes, can explain 82.37% and 89.11% of their total variance, reducing the number of evaluation indicators. This study provides a basis for research on the mechanisms and breeding of sorghum with resistance to anthracnose.

Keywords

Sorghum (Sorghum bicolour (L.) Moench)
Anthracnose (Colletotrichum sublineola)
Cell tissue structure
Physiological and biochemical indicators
Disease-resistant genes
Multivariate analysis
Subject terms

Plant hormones
Plant molecular biology
Plant physiology
Plant stress responses
the Talent 532 Base Project of the Organization Department in Guizhou Province, ChinaGrant number: QRLF(2013)533 no.15) (Grant number: QRLF(2016) no.23) (Grant number: QRLF(2020) no.2 Zhao Zhi special Fund for Revitalization of Top Ten Industries (High-quality Tobacco and Alcohol) Industry in Guizhou Province for ‘Research on breeding of New varieties of Sorghum’Guizhou Finance Industry [2020] No.198 Ren Mingjian http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 32272514 and 32060614 Xie Xin issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Sorghum is the fifth-largest cereal crop cultivated worldwide. It can be used as food, feed, and industrial raw material, playing an important role in food security1,2. Sorghum has a long history of cultivation and widespread distribution in China, and it is a key ingredient for making alcohol and vinegar3. Sorghum anthracnose is one of the most severe diseases affecting sorghum plantations worldwide2. It can occur at various stages of sorghum growth4, primarily targeting the leaves but also affecting stems, panicles, spikes, and grains, panicles, spikes, and grains5. C. sublineola infection can lead to premature ageing in plants, lodging, reduced sorghum yields, and even complete crop failure6,7. Pesticides are traditionally used to control anthracnose in sorghum production; however, long-term use of pesticides can lead to the development of resistance in sorghum anthracnose fungi and damage to the natural ecosystem. Planting anthracnose-resistant sorghum varieties is a cost-effective, efficient, and environmentally friendly measure, and breeding programs that develop anthracnose-resistant sorghum varieties are the most effective for increasing yields, reducing costs, and preventing anthracnose infections2.

Following pathogen infection, plants activate their defence systems, with reactive oxygen (ROS), defence enzymes, antioxidants, and other substances starting to resist pathogen invasion. Continued pathogen invasion leads to the failure of the defence system, leading to imbalances in gene expression and physiological and biochemical substances, followed by tissue damage and ultimately accelerated aging and death of the plant8,9. For example, after inoculation with Ralstonia solanacearum, the number of disease resistance-related genes significantly increased in the resistant tobacco varieties10. After rice becomes infected with the bacterial stripe disease, significant changes occur in the contents of SA, JA, GA, and zeatin (ZT), as well as in the activities of defence enzymes11. After the corn is infected with grey leaf spot disease, Pn, stomatal conductance (Gs), intercellular CO2 concentration (Ci), and transpiration rate (Tr) all decrease, and the activities of defence enzymes and SA and JA content increase12. When Fusarium wilt invades susceptible sweet potatoes, vascular bundle damage, separation of parenchyma cell walls, and other structural changes in the tissues occur13.

Many factors influence the stress tolerance of plants, and a single indicator cannot be used to comprehensively and accurately evaluate them. Therefore, when utilising multiple indicators, multivariate statistical methods can provide a more comprehensive and objective evaluation method14,15. For example, the resistance metabolic pathways of rice blast infection disease in rice seedlings were analysed using an analysis of variance and PCA16. The physiological and biochemical responses of melon leaves under salt stress, including the relative water content, osmotic potential, Pn, Tr, and ABA, were evaluated using PCA and hierarchical clustering analysis17. The physiological and biochemical responses and drought resistance evaluation of soluble protein, Pro, MDA content, defence enzyme activity, as well as Pn, Tr, Gs, and Ci of Gleditsia sinensis seedlings under drought and rehydration conditions were analysed using correlation analysis and PCA analyses18.

Recent research on anthracnose resistance in sorghum has predominantly focused on plant pathology, including the distribution of C. sublineola in sorghum, the epidemiology and aetiology of the disease19–21, identification of resources for C. sublineola resistance in sorghum22–24, mapping of resistance genes1,25,26, and analysis of resistance gene structure1,27. Breeding research on anthracnose-resistant sorghum is the most effective way to increase yields, reduce costs, and prevent anthracnose disease in sorghum. Moreover, research on the response and evaluation of anthracnose-resistant sorghum is fundamental for developing successful methods to breed anthracnose-resistant sorghum. However, limited reports exist on the changes in cellular tissue structure, defence enzymes, antioxidant enzymes, photosynthetic physiology, and endogenous hormones post-infection caused by C. sublineola, as well as the evaluation of disease resistance using multivariate statistical methods.

Therefore, we researched the response and evaluation of the sorghum tissue structure, physiological and biochemical and disease-resistance genes of sorghum anthracnose resistance. We aim to provide this information for advancing sorghum disease resistance breeding programs and addressing production disease issues.

Results and analysis

Changes in sorghum leaf lesions after C. sublineola infection

Photographs depicting the progression of sorghum C. sublineola infection on the leaves of cultivars gz93 and gz234 were taken at time points before and after infection (Fig. 1), with images of the top and side of gz93 and gz234 sorghum seedlings at 120 h (Fig. S1), indicating substantial disparities in disease development between the two cultivars. The gz93 and gz234 did not show any lesions from 0 to 24 h (Fig. 1A–C). In gz234, by 36 h, sporadic pinpoint lesions appeared on the main leaf veins, which increased in number by 48 h, with some lesions elongating into small shapes. By 72 h, these elongated lesions had grown longer and thicker, with a higher density on the leaves. At 96 h, the elongated lesions on the main veins had expanded further and merged into patches, and by 120 h, continuous lesion patches were increasingly evident on the leaves (Fig. 1D–H). In contrast, only a few star-shaped lesions were visible on the main veins in gz93 at 48 h, becoming more pronounced by 72 h and increasing in number. By 96 h, these lesions had extended into small shapes and started appearing on the leaves in significantly higher numbers. By 120 h, the lesions on the main veins had distinct elongated shapes with larger lesions (Fig. 1E–H). Significant differences in RLA existed among all subsequent time points from 48 to 120 h in gz234. Similarly, significant differences were observed among all periods from 72 to 120 h in gz93 (Fig. 2). These results indicate a significant increase in RLA for both cultivars in later stages, although gz234 exhibited a notably faster and greater increase in RLA than gz93.Figure 1 Lesions images of the first, second, and third leaves of gz93 and gz234 under C. sublineola infection. (A) 0 h, (B) 12 h, (C) 24 h, (D) 36 h, (E) 48 h, (F) 72 h, (G) 96 h, and (H) 120 h.

Figure 2 Changes in relative leaf area (RLA) of gz93 and gz234 at different times under C. sublineola infection. The letters indicate significant differences at the p < 0.05 level; the same convention is used in subsequent figures.

Changes in sorghum leaf cell tissue structure in response to C. sublineola infection

The cellular tissue structures of gz93 and gz234 differed, as did the changes in the cellular tissue structures at the lesion sites after C. sublineola infection. The leaf cell tissue structures of gz234 and gz93 were different before infection (Figs. 3A,B, and 4D–I). These results indicate that the upper and lower epidermal, vascular bundles, and mesophyll cells of the leaf tissues of susceptible and resistant sorghum seedlings were morphologically different, which may have led to different physical defence mechanisms of disease resistance in the two sorghum varieties. Comparing the healthy and lesion areas of the leaves of gz93 at 120 h (Fig. 3C), with the exceptions of vascular bundle cell length (Fig. 4C), leaf thickness (Fig. 4A), interveinal distance (Fig. 4B), bundle sheath cell cell profile area (Fig. 4D), mesophyll cell cell length (Fig. 4E), mesophyll cell cell profile area (Fig. 4F), upper epidermis cell cell length (Fig. 4G), upper epidermis cell cell profile area (Fig. 4H), lower epidermis cell cell length (Fig. 4I), lower epidermis cell cell profile area (Fig. 4J) in the lesion were significantly reduced when compared to those of the healthy leaf area. In comparing the healthy and lesion areas of the gz234 leaves at 120 h (Fig. 3D), excepting the vein distance (Fig. 4B), vascular bundle cell length (Fig. 4C), and vascular bundle cell profile area (Fig. 4D), the remaining indicators (Fig. 4A, E–J) in the lesion areas were significantly reduced when compared to those of the healthy area. These findings indicate that the leaf thickness, the length, and profile area of the leaf mesophyll cells, and the upper and lower epidermal cells decreased in the lesion area, with a greater reduction observed in gz234 than in gz93.Figure 3 Changes in cell tissue structures of gz93 and gz234 under C. sublineola infection. (A) 0 h diagram of gz93, (B) 0 h diagram of gz234, (C) 120 h diagram of gz93, (D) 120 h diagram of gz234.

Figure 4 Histogram of cell tissue structure index changes of gz93 and gz234 under C. sublineola infection. S-0: represents the 0 h time point before the infection of susceptible sorghum gz234; R-0: represents the 0 h time point before the infection of resistant sorghum gz93; S-C: represents the non-lesion area of gz234 at 120 h; S-T: represents the lesion area of gz234 at 120 h; R–C: represents the non-lesion area of gz93 at 120 h; R-T: represents the lesion area of gz93 at 120 h.

C. sublineola infection effects on sorghum photosynthetic physiology

Significant changes in the photosynthetic physiology of gz93 and gz234 occurred following C. sublineola infection (Fig. 5). The measurements for the photosynthetic response at light intensities of 0, 100, 300, 500, 800, 1000, 1300, 1500, 1800, and 2000 µmol m−2 s−1 were fitted to the light response curve using a hyperbolic correction model, and the average light saturation points for gz234 and gz93 were 1238 µmol m−2 s−1 and 935 µmol m−2 s−1, respectively. The photosynthetic physiological indicators were measured at their respective light saturation points. After 24 h, the values of Pn, Gs, Ci, and Tr for both gz234 and gz93 decreased and then rebounded at 48 h. Overall, Pn and Gs decreased after C. sublineola infection; the Pn of gz234 and gz93 decreased from 3.36 to 2.47 µmol m−2 s−1, respectively, at 0 h to 1.74 µmolm m−2 s−1 and 1.96 µmolm m−2 s−1, at 120 h; these results represented reductions of 48.21% and 20.65%, respectively. The Gs of gz234 and gz93 decreased from 0.02572 to 0.01909 molm m−2 s−1, respectively, at 0 h to 0.01152 molm m−2 s−1 and 0.00988 molm m−2 s−1, at 120 h, which represented respective reductions of 55.20% and 48.25% respectively. These results indicate that following the C. sublineola infection, the decreases in the Pn and Gs of gz234 were greater than those of gz93.Figure 5 Line chart of photosynthetic physiological indexes of gz93 and gz234 under C. sublineola infection at different times.

Anthracnose infection affects defensive enzyme activity and Pro and MDA contents in sorghum

Significant changes in POD, SOD, and CAT activities, as well as Pro and MDA contents, gz93 and gz234, were observed after C. sublineola infection (Fig. 6). Following inoculation, the overall POD activity in gz93 and gz234 continuously increased, with that of gz93 peaking at 120 h, while that of gz234 peaked at 96 h. These results reflect significant increases of 170.52% and 89.32%, respectively, when compared with 0 h (Fig. 6A). This result indicates that the duration of the elevated POD activity in gz93 was longer than that in gz234 and that the magnitude of increase was greater in gz93 than in gz234. The SOD activity in gz93 and gz234 also increased, decreased, and then increased again, with an overall increase observed. Except at 48 h and 72 h, the SOD activity in gz93 was consistently higher than that of gz234 (Fig. 6B). The CAT activities of both gz93 and gz234 initially increased and then decreased. Apart from the 0 to 24 h period, the CAT activity of gz93 was higher than that of gz234 (Fig. 6C). The Pro content of both gz93 and gz234 initially increased and then decreased. Except for the 36 h timepoint, the Pro content of gz93 was significantly higher than that of gz234 (Fig. 6D). These results indicate that the SOD and CAT activity and Pro content in gz93 were higher than those in gz234 following the C. sublineola infection, which may be positively correlated with disease resistance. The MDA content in both gz93 and gz234 showed an overall increasing trend over time, with significantly higher MDA content in gz234 than in gz93, except that measured at 24 h and 48 h (Fig. 6E). This finding indicates that MDA content in the resistant and susceptible sorghum cultivars increased after C. sublineola infection, with gz234 showing a greater increase than gz93 and the MDA may be negatively correlated with resistance.Figure 6 Line chart of changes in the activity of defence enzymes and the contents of MDA and Pro in gz93 and gz234 at different times under C. sublineola infection.

Anthracnose infection effects on plant hormones in sorghum

Following the infection with C. sublineola, ABA, JA, SA, GA3, IAA, and TZR contents in gz93 and gz234 showed significant changes, suggesting that they participated in the resistance response to anthracnose in sorghum (Fig. 7). From 0 to 120 h post-infection, the ABA content in gz93 was higher than that in gz234 (Fig. 7A), although ABA initially increased and then gradually stabilised in both cultivars. The JA content in gz93 was higher than that in gz234, showing an initial increase followed by a decrease, with an overall increasing trend (Fig. 7B). The SA content in gz93 was higher than in gz234; however, both showed a rapid increase followed by a decrease to pre-infection levels (Fig. 7C). The GA3 content in gz93 was higher than that in gz234, exhibiting an initial increase followed by a decrease to pre-infection levels (Fig. 7D). These findings indicate that after the C. sublineola infection, the contents of ABA, JA, SA, and GA3 in gz93 were higher than in gz234, and may be positively correlated with disease-resistance in sorghum. The IAA contents in both gz93 and gz234 showed an overall pattern of increase–decrease-increase–decrease, with the highest levels observed at 96 h and 72 h, respectively (Fig. 7E); the TZR contents in both gz93 and gz234 exhibited an overall pattern of increase–decrease-increase, reaching the highest levels at 24 h (Fig. 7F), and they were significantly higher than those at 0 h. These results indicate that the C. sublineola infection significantly affected on IAA and TZR, but the correlation with disease resistance may not be obvious.Figure 7 Line chart of plant hormone changes of gz93 and gz234 at different times under C. sublineola infection.

Changes in relative expression of disease-resistant genes in sorghum during C. sublineola infection

Following the C. sublineola infection, the relative expression levels of 18 disease-resistance genes in gz93 showed significant changes when compared with SbUBQ10, with an initial increase followed by a decrease (Fig. 8). SbBAK1 and SbBIK1 had the highest relative expression levels at 36 h (Fig. 8A,J), while SbWRKY13 and SbWRKY76 had the highest expression at 72 h (Fig. 8G,I). The rest of the tested genes reached their expression peaks at 48 h post-infection (Fig. 8B–F, H, K–Q). Therefore, the disease-resistant genes were overexpressed in gz93 following the C. sublineola infection, with SbBAK1 and SbBIK1 peaking earliest and SbWRKY13 and SbWRKY76 peaking latest. After infection, gz234 showed significant changes in the relative expression levels of the disease-resistance genes. The expression levels of SbBAK1, SbCEBiP, SbPUB40, SbWRKY13, SbWRKY71, SbWRKY76, SbBIK1, SbFLS2, SbAOC, SbLOX, SbAOS, SbOPR and SbRG initially decreased and then increased slightly after 48 h post-infection (Fig. 8A,B,D,G–O,R), indicating that these genes were initially suppressed and then recovered. The expression levels of SbSERK1, SbPAL, and SbPPO first increased and then decreased in gz234, reaching their highest levels at 48 h (Fig. 8F,P,Q). Thus, these three genes were initially overexpressed in response to C. sublineola infection and then returned to their pre-infection levels. The expression levels of SbFRK1 and SbRIN4 decreased continuously in gz234 cells (Fig. 8C,E), indicating that they were suppressed following C. sublineola infection.Figure 8 (A–R) Denote line charts of the relative expressions of gz93 and gz234 resistance genes at different times under C. sublineola infection.

Correlation and regression analyses of RLA with physiological and biochemical indicators, as well as disease-resistant genes during C. sublineola infection in sorghum

The most obvious symptoms of sorghum infections are lesions, with the area of the lesions indicating disease severity. Thus, RLA can be used as a direct indicator of the degree of disease. RLA was highly negatively correlated with GA3, with a correlation coefficient (R) of − 0.76; RLA was significantly positively correlated with MDA, with an R of 0.69; and RLA was highly positively correlated with POD, with an R of 0.80 (Fig. 9A). These suggested that GA3, MDA, and POD were closely related to RLA and can be used as reference indicators to assess the severity of sorghum anthracnose during disease progression. RLA was negatively correlated with 18 plant disease resistance genes (Fig. 9B), indicating that these genes helped inhibit C. sublineola infection in sorghum. To analyse the relationship between physiological and biochemical indicators and the resistance of sorghum to disease to select reliable indicators for disease resistance identification, RLA was used as the dependent variable, and individual physiological and biochemical indicators were used as independent variables in stepwise regression analysis. After analysing the multicollinearity and normal distribution of residuals in the linear model, an optimal regression equation was obtained with RLA as the dependent variable and Pro, GA3, and JA as independent variables: RLA = 0.029 + 8.02 × 10−6 JA − 0.016 GA3. The equation had a coefficient of determination of R2 of 0.645, adjusted R2 = 0.2557, and P = 0.005. From this regression equation, we found that JA and GA3 had a significant impact on anthracnose resistance in sorghum and can predict and explain RLA observed during the C. sublineola infection in sorghum.Figure 9 (A) The heat map of the correlation between RLA and physiological and biochemical indexes of gz93 and gz234, (B) the heat map of the correlation between RLA and disease resistance genes of gz93 and gz234.

PCA of physiological and biochemical indicators and disease-resistant related genes during C. sublineola infection resistance in sorghum

PCA was performed on 15 physiological and biochemical indicators in the gz93 and gz234 cultivars during C. sublineola infection. Components with eigenvalues > 1 were extracted, resulting in five principal components with eigenvalues of 4.73, 3.26, 1.89, 1.46, and 1.01, which contributed to 31.55%, 21.75%, 12.61%, 9.72%, and 6.74% of the total variance, respectively. The cumulative contribution rate was 82.37%, indicating that the five principal components effectively represented the original data (Table 1) and could be used to characterise the 15 physiological indicators of anthracnose resistance in sorghum. When we evaluated the physiological resistance indicators of sorghum against anthracnose, the five feature vectors that contributed the most to principal component 1 (PC1) were ABA, Gs, Pn, JA, and Tr. ABA and JA were observed in the positive loadings of PC1, whereas Gs, Pn, and Tr were in the negative loadings of PC1. The top three feature vectors that contributed significantly to principal component 2 (PC2) were SA, TZR, and IAA, all of which were located in the positive loadings of PC2. These features can be used as positive indicators to assess sorghum resistance to anthracnose in PC2 (Fig. 10A). By can be performed PCA on 18 disease resistance genes in gz93 and gz234 during C. sublineola infection, the analysis extracted two principal components with eigenvalues of 14.47 and 1.57%, respectively, which contributed to 80.39% and 8.72% of the total variance, respectively. The cumulative contribution rate was 89.11%, which effectively represented the original data. Therefore, these two principal components can be used to characterise these disease-resistance genes as indicators of anthracnose resistance in sorghum. The 18 disease-resistant genes were positively correlated with PC1, while the three feature vectors that contributed the most to PC1 were SbCEBiP, SbAOS, and SbRG, which were all located in the positive quadrant of PC1. This result indicated that when evaluating the molecular resistance indicators of sorghum against anthracnose, SbCEBiP, SbAOS, and SbRG can serve as positively correlated loadings for PC1. The significant feature vector contributing to PC2 was SbWRKY76, which was located in the negative loadings of PC2. This finding suggests that SbWRKY76 can be used as a negatively correlated loading feature to assess sorghum resistance anthracnose in PC2 (Fig. 10B).Table 1 The total variance interpretation of the physiological and biochemical and disease resistance gene indicators of PCA.

Total variance explanation of physiological and biochemical indexes	Total variance explanation of disease resistance gene indexes	
Principal component number	Eigenvalue	Percentage of variance (%)	Cumulative (%)	Principal component number	Eigenvalue	Percentage of variance (%)	Cumulative (%)	
1	4.73185	31.54565	31.54565	1	14.46932	80.38512	80.38512	
2	3.26284	21.75226	53.29791	2	1.57017	8.72316	89.10828	
3	1.89142	12.60945	65.90736	3	0.85447	4.74703	93.85531	
4	1.4582	9.72134	75.6287	4	0.51663	2.87017	96.72548	
5	1.0106	6.73732	82.36602	5	0.31245	1.73584	98.46132	
6	0.73729	4.91528	87.28129	6	0.12404	0.6891	99.15041	
7	0.64061	4.27073	91.55203	7	0.05201	0.28892	99.43934	
8	0.33753	2.25019	93.80221	8	0.02654	0.14743	99.58677	
9	0.28671	1.91142	95.71363	9	0.02567	0.14264	99.72941	
10	0.23652	1.57678	97.29042	10	0.01943	0.10792	99.83733	
11	0.19621	1.30804	98.59846	11	0.01193	0.06629	99.90362	
12	0.07418	0.49455	99.09301	12	0.0053	0.02947	99.93309	
13	0.07005	0.46699	99.56	13	0.00429	0.02386	99.95695	
14	0.04181	0.27873	99.83873	14	0.00303	0.01681	99.97376	
15	0.02419	0.16127	100	15	0.00246	0.01369	99.98745	
				16	0.00102	0.00568	99.99313	
				17	8.77E−04	0.00487	99.998	
				18	3.60E−04	0.002	100	

Figure 10 (A) PCA map of 15 physiological and biochemical indicators, and (B) is a PCA map of 18 disease resistance genes.

Discussion

Following the C. sublineola infection, the leaf thickness, mesophyll cells, epidermal cells, and Pn of gz234 and gz93 all decreased, indicating that the changes in sorghum cell tissue structure affected photosynthetic capacity. This result is consistent with previous studies28 that demonstrated that the changes in tissue structure of newly grown sorghum leaves affect photosynthetic characteristics. When leaf Gs, Tr, and Ci decrease simultaneously, the factor causing the decrease in Pn is stomatal limitation; conversely, if the decrease in leaf Pn is accompanied by an increase in Ci, the limiting factor of photosynthesis is non-stomatal limitation29. In this study, in the early stages, Gs, Tr, and Ci of gz93 and gz234 decreased simultaneously; stomatal limitation caused the initial decrease in Pn, possibly due to stomatal closure during early C. sublineola infection to reduce pathogen invasion. In the later stages, Gs and Tr decreased in gz234, Gs decreased and Tr increased in gz93, and Ci increased in both gz93 and gz234; this indicated that the factors causing the decrease in Pn in the later stages of gz93 and gz234 may be non-stomatal limitations. This occurred because after pathogen infection, the chloroplast structure is damaged, chlorophyll content decreases, limiting their photosynthesis30,31.

The magnitude and duration of the increase in POD, SOD, and CAT activities in gz93 were greater than in gz234, which is consistent with previous studies32 on maize’s resistance to grey leaf spot disease. This is because when plants are subjected to stress, the levels of ROS within cells rapidly increase. ROS can inhibit pathogenic microorganisms and induce defence responses such as lignin, phytoalexins, and cell hypersensitivity. However, the continuous increase of ROS can disrupt the balance of ROS within cells, leading to an increase in POD, SOD, and CAT activities. SOD and CAT are important protective enzymes that clear superoxide anion radicals within plant cells, while POD is a redox enzyme that can eliminate H2O2 in cells. Several studies have reported that the activities of POD, SOD, and CAT in disease-resistant varieties are higher than those in susceptible varieties11,33,34. Excessive ROS can cause lipid peroxidation of cell membranes, leading to cell damage. MDA is an important product of cell membrane peroxidation, which can harm cells and thus, the MDA content of cells reflects the degree of damage to the host plant35. The MDA content in resistant varieties of chrysanthemum infected with Botrytis cinerea was significantly lower than that in susceptible varieties36. Consistent with the results of this study, the MDA content of gz93 was lower than that of gz234, indicating that the cell membrane of gz234 was more severely damaged by the pathogen than that of gz93. This also corresponded with the greater RLA in gz234 than in gz93. Pro is a very important osmotic regulator that can protect biological substances and the cell membranes of plant cells37. In this study, the duration of the increase in Pro content in gz93 was longer and higher than that in gz234, consistent with previous studies that showed that the Pro content in disease-resistant soybeans38 and foxtail39 millet was higher than that in susceptible soybeans and foxtail.

BIK1 belongs to the protein kinase family and is primarily involved in the transmission and activation of immune signals during disease resistance40. Upon recognition of microbial-associated molecular patterns by pattern recognition receptors, the initial inducible defence is activated, triggering the BIK1 expression. BAK1 belongs to the leucine-rich repeat receptor kinase family, is a co-receptor of pattern recognition receptors and a regulator of signal transduction that controls immune signal recognition and transduction41. The SERK1 in foxtail millet is involved in brassinosteroid signal transduction and plant immunity42. During interactions with wheat rust, TaSERK1 is induced and expressed by wheat rust to positively regulate the wheat defence response against rust disease43. PPO is an enzyme widely present in plants that catalyses the formation of lignin and quinone compounds as a protective shield against pathogen invasion44. In our study, SbBIK1 and SbBAK1 exhibited different expression patterns in the gz93 and gz234 cultivars. In gz93, the expression of genes first increased and then decreased, with the highest expression observed at 36 h. However, in gz234, the expression first decreased and then increased, reaching the highest level at 48 h. SbSERK1 and SbPPO showed similar expression patterns in gz93 and gz234, both increasing at first and then decreasing, with the highest expression observed at 48 h. The expression of disease resistance genes is associated with resistant and susceptible varieties. This is similar to the expression pattern of the disease-related protein gene Seita.2G024600 in millet infected with Sclerospora graminicola, where it is significantly upregulated in resistant varieties and downregulated in susceptible varieties39.

With the deepening of the concept of multi-index comprehensive evaluation, multivariate statistical analysis methods such as principal component analysis, cluster analysis, and linear regression analysis are being increasingly widely and deeply used in the evaluation of complex crop traits45–47. Utilizing the advantages of various statistical analysis methods in indicator system construction, index weighting, data requirements, and so on can reduce the likelihood of random bias and systematic errors. This helps address inconsistencies in evaluation conclusions and improves the quality of comprehensive evaluations48. The RLA of sorghum is significantly negatively correlated with GA3 and significantly positively correlated with MDA and POD. Different correlations exist among other defence enzyme indicators, hormone indicators, and photosynthetic indicators, indicating that the resistance of sorghum to anthracnose is a complex and comprehensive trait. Varying degrees of correlation exist among different indicators, necessitating a comprehensive evaluation of disease resistance by considering each indicator. PCA, with the top 5 principal components for physiological and biochemical indicators and the top 2 principal components for disease-resistant genes, can explain 82.37% and 89.11% of their total variance, respectively, reducing the number of evaluation indicators to improve evaluation efficiency. Concurrently, multiple regression analysis with RLA as the dependent variable and Pro, GA3 and JA as independent variables can predict and explain the severity of anthracnose in sorghum.

In conclusion, this study inoculated C. sublineola spores on resistant and susceptible sorghum seedlings in a controlled environment. Firstly, it analysed the response of leaf lesions and cell tissue structures during anthracnose resistance in sorghum. Secondly, it examined the physiological and biochemical responses of photosynthetic physiology, defence enzymes, antioxidants, and plant hormones. Furthermore, it analysed the genetic-level responses of disease-resistant genes. At the same time, the analysis of defence enzymes, plant hormones, and disease-related gene changes in resistant and susceptible sorghum after infection by C. sublineola, as well as the correlation analysis of sorghum anthracnose resistance, indicate that GA3, MDA, POD, and disease-resistant genes can serve as reference indicators for the severity of disease. Regression equations can predict and explain RLA, and PCA reduces the number of physiological and biochemical evaluation indicators. Therefore, these physiological, biochemical, and disease-resistant gene indicators can be used for comprehensive evaluation and prediction of sorghum resistance to anthracnose. In sorghum breeding for anthracnose resistance, these indicators can be used to screen resistant and susceptible sorghum materials, as well as for research and application in sorghum breeding for anthracnose resistance. The study systematically provides new evidence for investigating the mechanism of anthracnose resistance in sorghum, valuable insights for studying disease-resistant sorghum breeding strategies, and offers methodologies for exploring plant disease resistance pathways.

Materials and methods

Experimental materials and treatment

The sorghum anthracnose fungus (C. sublineola) used in this study was provided by the Plant Pathology Teaching and Research Office of Guizhou University. C. sublineola was inoculated on PDA medium at 25 °C in the dark for 12 h, followed by incubation at 28 °C under 15,000 lx light for 12 h for 7 days to produce a large number of spores. These spores were then obtained by filtering them from the mycelium in the medium with sterile water and dropping the spore solution onto a hemocytometer for observation and statistical analysis of spore concentration under a microscope. Seeds of sorghum varieties previously characterised in our laboratory as highly resistant (gz93) or highly susceptible (gz234) to anthracnose were surface-sterilized and sown in black flowerpots measuring 10 × 10 × 8 cm. The pots were filled with a substrate mixture consisting of loess soil, organic fertiliser, and seedling substrate in a volume ratio of 2:1:1. The flowerpots were placed in an artificial climate chamber with a temperature cycling between 25 °C and 23 °C, and a 12 h light (15,000 Lux)/12 h dark cycle. When the sorghum seedlings reached the three-leaf stage, they were inoculated with C. sublineola spores at a concentration of 1.5 × 105/ml. Each flowerpot is sprayed with 2.5 ml, with 12 seedlings per pot and approximately 0.208 ml per seedling. Following a 48 h incubation period in darkness. The plants were misted with sterile water every 12 h to maintain moisture levels while the normal cultivation process continued. Photographs were taken and the aboveground parts of the sorghum plants were harvested at designated time points: before inoculation (0 h) and then at 12 h, 24 h, 36 h, 48 h, 72 h, 96 h, and 120 h post-inoculation. Fifteen seedlings were sampled at each time point with three biological replicates. The samples were rapidly frozen in liquid nitrogen and stored at − 80 °C for future analysis.

The sorghum seeds used in this study were donated through friendly communication and consultation with the Guizhou Academy of Agricultural Sciences without any conflict of interest. The collection of plant materials and all experiments were conducted in accordance with relevant institutional, national, and international guidelines and regulations.

Experimental methods

Measurement of lesion area and analysis of sectioned paraffin-embedded tissues

The full plant photo of sorghum seedlings was taken with a camera (Canon, 5D Mark VI), sorghum leaves were scanned with a scanner (WinSEEDLE, Zealquest Scientific Technology Co., Ltd), and paraffin sections were scanned with a panoramic scanner microscope (3DHISTECH P250 FLASH, 3DHISTECH Ltd., Hungary).The lesion area on sorghum leaves was quantified using the ImageJ software49. Relative lesion area (RLA) = lesion area/leaf area. Three representative individuals (three replicates ) were selected, and the sum of the RLA of three leaves in each individual was calculated. The average value of the three individuals was calculated as the RLA of this study. Three leaves from each genotype (e.g., gz234 and gz93) were collected at 0 h and 120 h and then fixed in a 50% ethanol FAA fixative solution (Servicebio Company, lot no. CR2106105) for 7 d. Then, the leaves were embedded in paraffin and sectioned so that the cellular tissue structures could be assessed using the CaseViewer software(slideviewer2.7) that comes with the panoramic scanning microscope. Fifty random cell tissue samples were selected from each section for analysis.

Determination of defence enzyme activity and antioxidant content

The SOD (Lot No. 20220218), POD (Lot No. 20220413), CAT (Lot No. 20220720), MDA (Lot No. 20220609), and Pro (Lot No. 202206015) reagent kits (Solarbio Company) were used following the manufacturer’s instructions. A MULTISKAN SKY microplate reader (cat. N19038 Revl.O.2017; Thermo Scientific) was used to measure the absorbance values of the samples, which were used to calculate enzyme activity and antioxidant content.

Measurement of photosynthetic physiology

The photosynthesis system Li-6800 (American LI-COR Company, model Q116816) was used to measure the light-saturation response curves of the third leaf from the base of ten gz93 and gz234 plants under submerged conditions at 50% relative humidity. The light-saturation point was determined and at the specific light intensity corresponding to this point, five anthracnose-infected gz93 and gz234 sorghum plants were assessed for Pn, Ci, Tr, and Gs at 0 h, 24 h, 48 h, 72 h, 96 h, and 120 h.

Plant hormone measurement

Plant hormone levels were quantified using triple-quadrupole high-performance liquid chromatography-tandem mass spectrometry. Standard substances, including IAA (Lot No. 926J021), GA3 (Lot No. 705H021), TZR (Lot No. 617C021), ABA (Lot No. 621H021), SA (Lot No. 1221A0217), and 2,6-di-tert-butyl-p-cresol (Lot No. 309H021), were purchased from Solarbio, while the JA standard (Lot No. 14631-10MG) was obtained from Sigma. The data acquisition instrument included a high-performance liquid chromatography system (HPLC-LC-20AD model, Shimadzu Corporation, Japan) and a tandem mass spectrometer (4000 Qtrap organic mass spectrometer, ABSCIEX Mass Spectrometry Systems, USA, LC-MS/MS). Sample processing was conducted as previously described50, with the modification of replacing vacuum concentrated samples with freeze-dried sample solutions. Linear regression analysis of the peak area average value (Y) of quantified ions of the target component against the mass concentration (X, μg/L) produced the linear equations and correlation coefficients (r) for six standard plant hormone products. The characteristic fragment ion values of the six standard plant hormone products were obtained by scanning in the selected reaction monitoring mode (Appendix Table S1).

Relative expression levels of disease-resistant related genes

Total RNA was extracted using TRIzol® Reagent from (Lot No. 338111; Ambion by Life Technologies) and then reverse transcribed into cDNA using the BeyoRTTM III cDNA Synthesis Kit (Lot No. D7185M; Beyotime). SbUBQ10 was used as a reference gene51. Disease-resistance genes were selected for qRT-PCR amplification using the Universal SYBR Qpcr Master Mix (Lot No. BL697A; Biosharp) and are presented in Supplementary Table S2. All reagents were used according to the manufacturer’s instructions. BIO-RAD CFX96 Real-Time cycler (c100 Touch Thermal cycler) was used to quantify gene expression levels. The 2−∆∆CT method described by Schmittgen was used to determine the relative fold-change in expression of the target genes52.

Data analysis

Data were organised and analysed using Microsoft Excel 2020 and IBM SPSS Statistics 26. Graphs and images were created using OriginPro 2021 and Adobe Photoshop CS6, respectively. We used t-tests to assess the significance of differences between the two datasets, and multiple comparisons were conducted on experimental data from various treatments using the least significant difference method (p < 0.05).

Supplementary Information

Supplementary Figure S1.

Supplementary Tables.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-70088-0.

Author contributions

C.S., X.X., Z.Z. and R.M. conceived and designed the study; Z.B., D.L. and W.R. performed the experiments; L.X., L.K. contributed reagents/materials/analysis tools; C.S., X.X., M.A. wrote the manuscript and revised the manuscript. All authors read and approved the final version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No.32272514 and 32,060,614); the special Fund for Revitalization of Top Ten Industries (High-quality Tobacco and Alcohol) Industry in Guizhou Province for ‘Research on breeding of New varieties of Sorghum’ (Guizhou Finance Industry [2020] No.198), and the Talent 532 Base Project of the Organization Department in Guizhou Province, China (Grant Number: QRLF(2013)533 No.15) (Grant Number: QRLF(2016) No.23) (Grant Number: QRLF(2020) No.2).

Data availability

All data are presented in the article, and can be requested from the corresponding author if required.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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