AI Article Synopsis

  • Lithocarpus polystachyus, known as 'sweat tea,' is an important tree in China whose leaves are beneficial for diabetes treatment due to their high dihydrochalcone content.
  • In January 2024, a significant disease outbreak was reported on these trees in Hunan Province, affecting about 74% of surveyed plants, with symptoms evolving from small yellow lesions to large brown patches and ultimately leaf necrosis.
  • Pathogen isolation methods revealed that the fungus responsible for the disease was identified as Diaporthe sp., confirmed by examining colony characteristics and genomic DNA analysis.

Article Abstract

The leaves of Lithocarpus polystachyus (Wall. ex A. DC.), an economically significant tree species in China, are commonly referred to as 'sweat tea' due to their high dihydrochalcone content, which holds biomedical importance, particularly in the treatment of diabetes (Hou et al. 2011). In January 2024, brown spots on L. polystachyus leaves were widely observed in Ningxiang (28°23'N, 112°59'E), Hunan Province, China. According to the investigation, the incidence rate of this disease was about 74% (222/300 plants surveyed). On each infected plant, nearly 60% leaves had symptoms. The disease initially presented as small yellow lesions that eventually developed into large brown patches with dark brown edges. More than 80% of the area was covered by leaf lesions, which eventually turned into leaf necrosis. To ascertain the pathogenic species responsible for this disease, pathogen isolation was conducted using a tissue separation method (Xu et al. 2023). The infected leaf tissues were surface-disinfected by immersing in 75% ethanol followed by 0.1% HgCl2. Small pieces (0.5 × 0.5 cm) were then excised and placed onto PDA medium, and incubated at 28°C for 6-9 days. Sterilized dissecting needles were used to pick mycelia from the edge of the colonies and placed onto PDA for strains purification. On the PDA, the colony color of upper side initially appeared white (Rayner 1A1), and then turned grey (Rayner 11C1), while the reverse side turnd faint yellow (Rayner 4A3). Black pycnidia were induced on PDA at 28°C under a 12 h/12 h light/dark cycle for 12 days. Alpha conidia were 5.37 _8.84 × 1.53 _3.19 μm (average: 6.77 × 2.37 μm, n = 50), hyaline, fusiform or ellipsoidal. Beta conidia were 13.61 _23.45 μm × 0.94 _1.47 μm (average: 18.78×1.18 μm, n = 50), hyaline, aseptate, filiform, straight or hamate. Morphologically, the fungi were identified as Diaporthe sp. (Guarnaccia and Crous 2017). To further affirm the identification of the pathogen, the genomic DNA was extracted from representative isolate, referred to as Dip, for molecular identification. The internal transcribed spacer region (ITS), translation elongation factor 1α (EF1-α), β-tubulin (TUB2) and histone H3 (HIS) genes were amplified from genomic DNA using primers ITS1/ITS4, EF1-728F/EF1-986R, Bt2a/Bt2b, and CYLH3F/H3-1b, respectively, to sequence for BLAST (Huang et al. 2015). The results showed that the ITS (GenBank: PP502145), EF1-α (GenBank: PP505773), TUB2 (GenBank: PP505774) and HIS (GenBank: PP505772) sequences of Dip isolate, respectively, showed 100% (469/469 bp), 98.47% (257/261 bp), 97.63% (617/632 bp), and 100% (379/379 bp) identity to their counterparts (GenBank: MW504747, ON049530, MW514138 and ON113058) in Diaporthe phoenicicola. The Maximum Likelihood tree was built based on the ITS, EF1-α, HIS and TUB2 sequences using MEGA11.0. Isolate Dip clustered with D. phoenicicola. The fungus was finally identified as D. phoenicicola by combining morphological and molecular characteristics. To confirm the pathogenicity of the isolated D. phoenicicola to induce brown spot, the pathogenicity assay was assessed following Koch's postulates (Gradmann, 2014). Conidial suspension (1×105 conidia per mL) was inoculated on 12 unwounded leaves collected from six 3-years-old plants, and sterile water was as control. The inoculated leaves were then incubated in chambers at 28℃ and 90% humidity with a 12 h photoperiod. The experiment was repeated three times. The results showed that inoculated leaves other than control developed brown spot symptoms within six days after inoculation. The test proved D. phoeicicola as the causal agent of this brown spot disease on L. polystachyus. The pathogen was exclusively re-isolated from the infected leaves and showed identical morphological characteristics to those of the original pathogens. To our knowledge, this is the first report of leaf brown spot of L. polystachyus caused by D. phoenicicola in China. This disease severely delays the plant development of L. polystachyus and significantly reduces the yield and quality of sweat tea. Our findings will contribute to the control of brown spot of L. polystachyus.

Download full-text PDF

Source
http://dx.doi.org/10.1094/PDIS-04-24-0846-PDNDOI Listing

Publication Analysis

Top Keywords

brown spot
24
brown
9
report leaf
8
leaf brown
8
lesions eventually
8
μm average
8
μm hyaline
8
genomic dna
8
inoculated leaves
8
spot polystachyus
8

Similar Publications

First Report of Leaf Spot Caused by on Invasive Weed in Korea.

Plant Dis

December 2024

Korea University, Environmental Science & Ecological Engineering, Seoul, Seoul, Korea (the Republic of), 02841;

Cerastium glomeratum Thuill., known as sticky mouse-ear chickweed, is native to Europe and has become naturalized in the wild on most continents. After its accidental introduction to Korea around the 1980s, it quickly became one of the dominant invasive weeds on the Korean peninsula and is now considered a significant threat to the Korean agroecosystem (Park et al.

View Article and Find Full Text PDF

Fangfeng (Saposhnikovia divaricata) is a perennial plant belonging to the Umbelliferae family, and is widely cultivated as a traditional Chinese medicine plant used to treat various diseases in northern China. In August 2022, a widespread leaf spot disease emerged on the Fangfeng leaves across a 2.5-acre farmland located in the Naiman District of Tongliao City, China ( 44°17' N; 121°29' E), where 5,000 acres of Fangfeng had been cultivated.

View Article and Find Full Text PDF

The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases.

View Article and Find Full Text PDF

Increasing financial incentives can lower the cost of trial recruitment.

Trials

December 2024

Center for Clinical Management Research, Health Service Research & Development, LTC Charles S. Kettles VA Medical Center, VA Ann Arbor Healthcare System, Ann Arbor, USA.

Monetary incentives are commonly used to help recruit trial participants. Some studies have found greater recruitment with larger incentives, while others have found smaller incentives more cost-effective in terms of cost per participant. As part of an implementation study, we compared the impact of four approaches to recruitment, three of which involved phone recruitment with varying financial incentives.

View Article and Find Full Text PDF

Enhances Brown Spot Disease Resistance in Rice.

Plants (Basel)

November 2024

Department of Plant Bioscience, College of Natural Resources and Life Science, Pusan National University, Miryang 50463, Republic of Korea.

Brown spot (BS) is caused by necrotrophs fungi () which affects rainfed and upland production in rice, resulting in significant losses in yield and grain quality. Here, we explored the meJA treatment that leads to rice resistance to BS. Fibrillins (FBNs) family are constituents of plastoglobules in chloroplast response to biotic and abiotic stress, many research revealed that and are not only associated with the rice against disease but also with the JA pathway.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!