Complete mitochondrial genome of Melia azedarach L., reveals two conformations generated by the repeat sequence mediated recombination.

Melia azedarach is a species of enormous value of pharmaceutical industries. Although the chloroplast genome of M. azedarach has been explored, the information of mitochondrial genome (Mt genome) remains surprisingly limited. In this study, we used a hybrid assembly strategy of BGI short-reads and Nanopore long-reads to assemble the Mt genome of M. azedarach. The Mt genome of M. azedarach is characterized by two circular chromosomes with 350,142 bp and 290,387 bp in length, respectively, which encodes 35 protein-coding genes (PCGs), 23 tRNA genes, and 3 rRNA genes. A pair of direct repeats (R1 and R2) were associated with genome recombination, resulting in two conformations based on the Sanger sequencing and Oxford Nanopore sequencing. Comparative analysis identified 19 homologous fragments between Mt and chloroplast genome, with the longest fragment of 12,142 bp. The phylogenetic analysis based on PCGs were consist with the latest classification of the Angiosperm Phylogeny Group. Notably, a total of 356 potential RNA editing sites were predicted based on 35 PCGs, and the editing events lead to the formation of the stop codon in the rps10 gene and the start codons in the nad4L and atp9 genes, which were verified by PCR amplification and Sanger sequencing. Taken together, the exploration of M. azedarach gap-free Mt genome provides a new insight into the evolution research and complex mitogenome architecture.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

Naturally selected CD7 CAR-T therapy without genetic manipulations for T-ALL/LBL: first-in-human phase 1 clinical trial.

Derivation of CD7-targeted chimeric antigen receptor (7CAR) T cells often requires genetic manipulations to ablate the CD7 gene or block CD7 cell surface expression. Our novel approach deriving naturally selected 7CAR (NS7CAR) T cells from bulk T cells was able to overcome major fratricide by minimizing accessible CD7 epitopes. The CD7 molecules of NS7CAR T cells were masked or sequestered by the CD7-targeting CAR. Compared with sorted CD7-negative 7CAR T cells and CD7 knocked-out 7CAR T cells, NS7CAR exhibited similar or superior therapeutic properties, including a greater percentage of CAR+ cells and a higher proportion of CD8+ central memory T cells. In our first-in-human phase 1 trial (NCT04572308), 20 patients with relapsed/refractory T-cell acute lymphoblastic leukemia (T-ALL) (n = 14) and T-cell lymphoblastic lymphoma (T-LBL) (n = 6) were treated with NS7CAR. Nineteen patients achieved minimal residual disease negative complete remission (CR) in the bone marrow (BM) by day 28, and 5 of 9 patients achieved extramedullary CR. With a median follow-up of 142.5 (32-311) days after infusion, 14 patients subsequently received allogeneic hematopoietic stem cell transplant (10 consolidative, 4 salvage) following NS7CAR infusion with no relapses to date. Of the 6 patients who did not receive a transplant, 4 remained in CR at a median time of 54 (32-180) days. Eighteen patients experienced mild cytokine release syndrome (CRS) (grade ≤2), 1 developed grade 3 CRS, and 2 had grade 1 neurotoxicity. These results indicate that NS7CAR-T therapy is a safe and highly effective treatment for T-ALL/LBL. More patients and longer follow-up are needed for validation.

Highly transparent Ce:Nd:YAG ceramic with good light conversion capacity for solar-pumped solid-state lasers.

Developing a high quality ceramic laser gain medium for solar directly pumped solid state lasers is essential, and yet the light conversion efficiency of the gain media for solar pumping remains a challenge. In this study, Ce and Nd ions, co-doped YAG transparent ceramics with theoretical transmittance and stable Ce3+ valent state were developed, and revealed that the absorbed visible light and light conversion efficiency in Ce,Nd:YAG ceramics were 3.98 times and 1.34 times higher than those in widely reported Cr,Nd:YAG ceramics, respectively. A concentration matching principle between Ce3+ and Nd3+ ions in YAG was established, and a higher Nd3+ ion doping concentration with a relatively low Ce3+ concentration was favorable to improve both the light conversion efficiency and emission intensity at 1064 nm of Ce,Nd:YAG ceramics. Energy transfer efficiency from Ce3+ to Nd3+ of the 0.3 at.%Ce,1.5at.%Nd:YAG ceramic reached as high as 61.71% at room temperature. Surprisingly, it was further promoted to 64.31% at a higher temperature of 473 K. More excited electrons at the upper energy level of Ce3+ ion under the high temperature accounted for this novel phenomenon. This study proposes a new design strategy of gain materials for solar directly pumped solid state lasers.

Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

Selective effect of winter weather on a songbird's morphology depends on individual sex and winter condition.

Knowledge of the effect of harsh weather on the phenotypic traits of organisms is essential for understanding the environmental influence on phenotype evolution and holds implications for predicting how species respond to current climate change. For many birds, harsh weather in winter often imposes a strong selective effect on their survival, and only the individuals with certain phenotypes may survive. However, whether the selective effect on phenotype varies with winter weather conditions has been poorly investigated. Here, we explored the selective effect of winter weather on black-throated tit's (Aegithalos concinnus) morphological traits under winters with and without severe snowstorms. We found that for males, the sizes of their bills, heads and wings significantly affected their overwinter survival, but the effects varied with winter conditions. In relatively benign winters, males with smaller bill depths, smaller bill surface areas, and greater head lengths survived better; whereas, in winters with severe snowstorms, a reverse pattern was found. This phenomenon was likely driven by selection pressures from heat retention and foraging requirements, with their relative importance depending on winter conditions. Additionally, wing length was positively correlated with male survival and the relationship was stronger in harsher winters, which was probably due to longer wings' higher flight efficiency in adverse weather. By contrast, we found no correlation between morphological traits and survival in females. These results suggest a sex-specific and condition-dependent selective effect of environment on bird phenotypes, implying complicated interactions between different selection pressures and phenotype evolution.

DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network.

Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach  is validated in accurately predicting DNA transcription factor sequences.

DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.

PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.

Transcriptional profiling of Xanthomonas campestris pv. campestris in viable but nonculturable state.

BACKGROUND: Xanthomonas campestris pv. campestris (Xcc) is an important seed-borne plant pathogenic bacteria that can cause a serious threat to cruciferous crops. Bacteria can enter into the viable but non-culturable (VBNC) state under stress conditions, and cause potential risks to agricultural production because the VBNC bacterial cells will evade culture-based detection. However, little is known about the mechanism of VBNC. Our previous study showed that Xcc could be induced into VBNC state by copper ion (Cu2+). RESULTS: Here, RNA-seq was performed to explore the mechanism of VBNC state. The results indicated that expression profiling was changed dramatically in the different VBNC stages (0 d, 1 d, 2 d and 10 d). Moreover, metabolism related pathways were enriched according to COG, GO and KEGG analysis of differentially expressed genes (DEGs). The DEGs associated with cell motility were down-regulated, whereas pathogenicity related genes were up-regulated. This study revealed that the high expression of genes related to stress response could trigger the active cells to VBNC state, while the genes involved in transcription and translation category, as well as transport and metabolism category, were ascribed to maintaining the VBNC state. CONCLUSION: This study summarized not only the related pathways that might trigger and maintain VBNC state, but also the expression profiling of genes in different survival state of bacteria under stress. It provided a new kind of gene expression profile and new ideas for studying VBNC state mechanism in X. campestris pv. campestris.

Experimental evidence that cuckoos choose host nests following an egg matching strategy.

The arms race between brood parasites and their hosts provides a classic model to study coevolution. Hosts often reject the parasitic egg, and brood parasites should therefore select host nests in which the colour of the eggs best matches that of their own. Although this hypothesis has received some support, direct experimental evidence is still lacking. Here, we report on a study of Daurian redstarts, which show a distinct egg-colour dimorphism, with females laying either blue or pink eggs. Redstarts are often parasitized by common cuckoos, which lay light blue eggs. First, we showed that cuckoo eggs were more similar in spectral reflectance to the blue than to the pink redstart egg morph. Second, we report that the natural parasitism rate was higher in blue than in pink host clutches. Third, we performed a field experiment in which we presented a dummy clutch of each colour morph adjacent to active redstart nests. In this set-up, cuckoos almost always chose to parasitize a blue clutch. Our results demonstrate that cuckoos actively choose redstart nests in which the egg colour matches the colour of their own eggs. Our study thus provides direct experimental evidence in support of the egg matching hypothesis.

Heterogeneous graph embedding model for predicting interactions between TF and target gene.

MOTIVATION: Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale. Existing computational methods for predicting underlying genes for TF to target is mainly proposed for their binding sites rather than the direct interaction. To bridge this gap, we in this work proposed a deep learning prediction model, named HGETGI, to identify the new TF-target gene interaction. Specifically, the proposed HGETGI model learns the patterns of the known interaction between TF and target gene complemented with their involvement in different human disease mechanisms. It performs prediction based on random walk for meta-path sampling and node embedding in a skip-gram manner. RESULTS: We evaluated the prediction performance of the proposed method on a real dataset and the experimental results show that it can achieve the average area under the curve of 0.8519 ± 0.0731 in fivefold cross validation. Besides, we conducted case studies on the prediction of two important kinds of TF, NFKB1 and TP53. As a result, 33 and 32 in the top-40 ranking lists of NFKB1 and TP53 were successfully confirmed by looking up another public database (hTftarget). It is envisioned that the proposed HGETGI method is feasible and effective for predicting TF-target gene interactions on a large scale. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available at https://github.com/PGTSING/HGETGI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of allyl isothiocyanate fumigation on medicinal plant root knot disease control, plant survival, and the soil bacterial community.

BACKGROUND: Allyl isothiocyanate (AITC) is a natural product with high volatility that is used as a biofumigant to alleviate soil-borne plant diseases, and problems such as root knot nematodes (RKNs) that necessitate continuous cropping. However, little research has assessed the effects of AITC fumigation on medicinal plants. RESULTS: AITC significantly reduced the population of RKNs in soil (p < 0.0001) and showed an excellent RKN disease control effect within 6 months after sowing Panax notoginseng (p < 0.0001). The seedling survival rate of 2-year-old P. notoginseng was approximately 1.7-fold higher after soil treatment with AITC (p = 0.1008). 16S rRNA sequencing indicated that the AITC treatment affected bacterial richness rather than diversity in consecutively cultivated (CC) soil. Furthermore, biomarkers with statistical differences between AITC-treated and untreated CC soil showed that Pirellulales (order), Pirellulaceae (family), Pseudomonadaceae (family), and Pseudomonas (genus) played important roles in the AITC-treated group. In addition, the microbiome functional phenotypes predicted using the BugBase tool suggested that AITC treatment is more conducive to improving CC soil through changes in the bacterial community structure. Crucially, our research also suggested that AITC soil treatment significantly increases soil organic matter (p = 0.0055), total nitrogen (p = 0.0054), and available potassium (p = 0.0373), which promotes the survival of a succeeding medicinal plant (Polygonatum kingianum). CONCLUSION: AITC is an ecologically friendly soil treatment that affects the top 10 bacterial richness but not diversity. It could also provide a basis for a useful agricultural soil management measure to alleviate soil sickness.

Preparation of Ge-Sb-Se glass fibers with excellent bending properties by the pulsated orifice ejection method.

We have innovatively introduced the pulsated orifice ejection method into the preparation of glass fibers, successfully preparing high-purity Ge28Sb12Se60 glass fibers. These fibers have a smooth surface, uniform elemental distribution, and excellent bending properties, with a minimal bending radius of 2 mm. In the infrared spectrum from 2.5 to 13.5 µm, the fibers achieve 65% transmission. Additionally, the fibers possess a density of 4.586 g/cm3, a diameter of 35 µm, a glass transition temperature (Tg) of 369°C, and an onset crystallization temperature (Tx) of 557°C. We have also measured the surface tension of the glass fibers, finding values from 0.288 N/m to 0.124 N/m as temperatures rose from 450°C to 500°C. The POEM holds the potential to achieve fiber cores of lengths up to hundreds of meters in theory. Our work provides a distinctive perspective for the preparation of glass fibers.

Behavioral laterality is correlated with problem-solving performance in a songbird.

Cerebral lateralization, which is often reflected in an individual's behavioral laterality (e.g., handedness and footedness), may bring animals certain benefits such as enhanced cognitive performance. Although the lateralization-cognition relationship has been widely studied in humans and other animals, current evidence supporting their relationship is ambiguous and warrants additional insights from more studies. Moreover, the lateralization-cognition relationship in non-human animals has been mostly studied in human-reared populations, and investigations of wild populations are particularly scarce. Here, we test the footedness of wild-caught male yellow-bellied tits (Pardaliparus venustulus) and investigate its association with their performance in learning to solve a toothpick-pulling problem and a drawer-opening problem. The tested birds showed an overall trend to gradually spent less time solving the problems, implying that they learned to solve the problems. Left- and right-footed individuals showed no significant differences in the latency to explore the experimental apparatuses and in the proportions that completed and did not complete the tasks. However, the left-footed individuals learned faster than the right-footed individuals in the drawer-opening experiment, indicating a potential cognitive advantage associated with left-footedness. These results contribute to the understanding of the behavioral differences between differently footed individuals and, in particular, the relationship between lateralization and cognitive ability in wild animals.

Analysis of 60 patients with relapsed or refractory T-cell acute lymphoblastic leukemia and T-cell lymphoblastic lymphoma treated with CD7-targeted chimeric antigen receptor-T cell therapy.

While the use of chimeric antigen receptor-T (CAR-T) therapy for T-cell malignancies is in the early stage of clinical trials, it exhibits substantial potential to offer long-term remission for patients with refractory/relapsed (R/R) T-cell malignancies. In our phase I/II clinical trials, 65 pediatric and adult patients with R/R T-cell acute lymphoblastic leukemia and lymphoblastic lymphoma (T-ALL/LBL) were enrolled (NCT04572308 and NCT04916860). Of these, 60 participants (T-ALL 35, T-LBL 25) received a single dose of naturally selected anti-CD7 CAR (NS7CAR) T cells at three levels: a low dose (5 × 105 /kg), a medium dose (1 to 1.5 × 106 /kg), and a high dose (2 × 106 /kg). On day 28, 94.4% of patients achieved deep complete remission (CR) in bone marrow. Among the 32 patients with extramedullary disease, 78.1% showed response, with 56.3% in CR and 21.9% in partial remission. The 2-year overall survival and progression-free survival (PFS) were 63.5% (95% CI 47.7-79.4) and 53.7% (95% CI, 38.9-68.6), with no difference between pediatric and adult patients. PFS was significantly higher among the 37 CR patients who proceeded with consolidation transplant than the 10 patients who did not with 1-year PFS 67.2% (95% CI 51.9-82.4) versus 15.0% (95% CI 0-40.2), p < .0001. Of the 10 CR patients without transplants, eight relapsed, while two sustained CR on day 128, and day 180, respectively. Cytokine release syndrome occurred in 91.7% of patients (grade 1/2 in 80.0%, grade 3/4 in 11.7%) and 5% of patients had neurotoxicity. NS7CAR-T therapy is effective in treating R/R T-ALL/LBL patients with promising PFS while maintaining a manageable safety profile.

A mutual promotion encoder-decoder method for ultrasonic hydronephrosis diagnosis.

As a common cause of hydronephrosis in children, ureteropelvic junction obstruction (UPJO) may lead to a series of progressive renal dysfunction. Ultrasonography is a primary screening of UPJO, yet its further examinations are laborious, time-consuming, and mostly radioactive. The deep learning based automatic diagnosis algorithms on UPJO or hydronephrosis ultrasound images are still rare and performance remains unsatisfactory owning to limitation of manually identified region of interest, small dataset and labels from single institution. To relieve the burden of children, parents, and doctors, and avoid wasting every bit information in all datasets, we hence designed a deep learning based mutual promotion model for the auto diagnosis of UPJO. This model consists of a semantic segmentation section and a classification section, they shared a mutual usage of a transformation structure by separately training the encoder and decoder and loop this circle. Thorough comparative experiments are conducted and situations are explored by ablation experiments, results shown our methods outperformed classic networks with an accuracy of 0.891 and an F1-score of 0.895. Our design can jointly utilize different supervisions and maximize the use of all the characteristics of each dataset, and automatically diagnose the severity of UPJO on the basis of ultrasound images by first segmentate then classify the images, moreover, not only is the final result excellent, but also the midway segmentation result is also very accurate and have smooth edges that are convenient for doctors to recognize with their naked eyes. All in all, our proposed method can be an important auxiliary tool for smart healthcare.

SiBERT: A Siamese-based BERT network for Chinese medical entities alignment.

Entity alignment aims at associating semantically similar entities in knowledge graphs from different sources. It is widely used in the integration and construction of professional medical knowledge. The existing deep learning methods lack term-level embedding representation, which limits the performance of entity alignment and causes a massive computational overhead. To address these problems, we propose a Siamese-based BERT (SiBERT) for Chinese medical entities alignment. SiBERT generates term-level embedding based on word embedding sequences to enhance the features of entities in similarity calculation. The process of entity alignment contains three steps. Specifically, the SiBERT is firstly pre-trained with synonym dictionary in the public domain, and transferred to the task of medical entity alignment. Secondly, four different categories of entities (disease, symptom, treatment, and examination) are labeled based on the standard terms selected from standard terms dataset. The entities and their standard terms form term pairs to train SiBERT. Finally, combined with the entity alignment algorithm, the most similar standard term is selected as the final result. To evaluate the effectiveness of our method, we conduct extensive experiments on real-world datasets. The experimental results illustrate that SiBERT network is superior to other compared algorithms both in alignment accuracy and computational efficiency.

First report of Leptosphaeria biglobosa 'brassicae' causing leaf spots in Eutrema japonicum in Yunnan, China.

Wasabi (Eutrema japonicum) is a root vegetable that is cultivated at large scales in southwestern China. In November 2021, approximately 40% of plants in a forested plantation in Dadishui, Yunnan Province, China (25.47°N, 103.22°E), showed leaf spot symptoms. The early symptoms were small black spots that gradually expanded into irregular brown to black lesions (0.5-1.5 cm), which were restricted by leaf veins. Yellow halos were observed at the outer edges of necrotic lesions. To identify the causal agent, we collected 20 diseased leaves and obtained fungal isolates from symptomatic leaf tissues. Following surface sterilization with 75% ethanol for 30 s, the tissues were cultured on potato dextrose agar (PDA) plates and incubated at 25°C under a 12 h light/12 h dark light cycle. After 7 days of incubation, a total of 12 isolates were obtained through single-spore culture. All isolates had similar colony morphology, and produced fluffy white mycelia and yellow pigment after 1 week of PDA culture at 25°C, and blackish- brown mycelium, tan pigment, and conidia after 2 weeks. The conidia were hyaline and cylindrical, with an average size of 4.6 µm × 2.2 µm. These morphological characteristics similar to the description of Leptosphaeria biglobosa (Shoemaker et. al, 2001) and Leptosphaeria maculans (Vincenot et al. 2008). Genomic DNA was extracted from mycelium of isolate SK-1, which was harvested from 10-day-old PDA culture using a FAST plant genomic DNA Extraction Kit (Biomed, China), following the manufacturer's instructions. The species-specific primers LbigF, LmacF, and LmacR (Liu et al. 2006) were used for identification via polymerase chain reaction (PCR). A 444-bp fragment characteristic of L. biglobosa 'brassicae' (Lbb), and a 330-bp of L. maculans 'brassicae' (Lmb) were amplified, respectively. Internal transcribed spacer (ITS) sequences (592 bp), part of the 5' end of beta-tubulin (968 bp), and actin (899 bp) were also amplified using the primers ITS1/ITS4, BT1/BT2, and ACTF/ACTR (Vincenot et al. 2008), respectively. PCR was performed in a volume of 25 µL containing 12.5 µL 2 × T5 Super PCR Mix (Tsingke Biotech, Beijing, China), 1 µL 10 µM primer (Tsingke Biotech), 1 µL DNA template, and an aliquot of sterile water to attain the total volume. The thermal cycler settings were 5 min at 98°C; 35 cycles of 10 s at 98°C, 10 s at 58°C, and 30 s at 72°C; and extension for 2 min at 72°C. The ITS sequence of isolate SK-1 (GenBank accession no. OQ216838), the partial ß-tubulin gene sequence (OQ241183), and the actin gene sequence (OQ241184) indicated 100% query cover and 100% identity with L. biglobosa (DQ458906), Lbb strain B3.6 (AY748995), and Lbb strain 2379-4 (AY748949), respectively. Phylogenetic analysis (King et al. 2022) also identified of isolate SK-1 as Lbb. To determinate its pathogenicity, isolate SK-1 was grown on PDA incubated at 28°C for 2 weeks, and conidial suspensions were prepared at a concentration of 106 conidia/mL. Then, 15 leaves of 4-month-old E. japonicum seedlings were needle-wounded on the front and inoculated by syringe injection of 10 µL of the appropriate conidial suspension. We used 10 µL of the sterilized distilled water as the control under forest growth conditions. All inoculation sites were covered with cotton strips and moistened with 1.0 mL sterile water to maintain humidity. After 12 days of incubation, the leaves developed symptoms similar to those observed in the field, and the fungus was reisolated from diseased leaves, whereas the controls remained healthy. Based on these results, we identified L. biglobosa 'brassicae' as the causal agent of leaf spot on E. japonicum in China. This fungus has been reported to cause blackleg in many Brassica crops in China such as Brassica napus (Fitt et al. 2006), Brassica oleracea (Zhou et al. 2019), B. juncea var. tumida (Deng et al. 2020), Brassica rapa subsp. pekinensis (Yu et al. 2021). To the best of our knowledge, this is the first report of L. biglobosa causing leaf spots in E. japonicum in China. Our data provide a basis for disease management in E. japonicum production in China.

Deletion of pbpC Enhances Bacterial Pathogenicity on Tomato by Affecting Biofilm Formation, Exopolysaccharides Production, and Exoenzyme Activities in Clavibacter michiganensis.

Penicillin-binding proteins (PBPs) are considered essential for bacterial peptidoglycan biosynthesis and cell wall assembly. Clavibacter michiganensis is a representative Gram-positive bacterial species that causes bacterial canker in tomato. pbpC plays a significant role in maintaining cell morphological characteristics and stress responses in C. michiganensis. The current study demonstrated that the deletion of pbpC commonly enhances bacterial pathogenicity in C. michiganensis and revealed the mechanisms through which this occurs. The expression of interrelated virulence genes, including celA, xysA, xysB, and pelA, were significantly upregulated in △pbpC mutants. Compared with those in wild-type strains, exoenzyme activities, the formation of biofilm, and the production of exopolysaccharides (EPS) were significantly increased in △pbpC mutants. It is noteworthy that EPS were responsible for the enhancement in bacterial pathogenicity, with the degree of necrotic tomato stem cankers intensifying with the injection of a gradient of EPS from C. michiganensis. These findings highlight new insights into the role of pbpC affecting bacterial pathogenicity, with an emphasis on EPS, advancing the current understanding of phytopathogenic infection strategies for Gram-positive bacteria.