NF-κB subunits direct kinetically distinct transcriptional cascades in antigen receptor-activated B cells.

The nuclear factor kappa B (NF-κB) family of transcription factors orchestrates signal-induced gene expression in diverse cell types. Cellular responses to NF-κB activation are regulated at the level of cell and signal specificity, as well as differential use of family members (subunit specificity). Here we used time-dependent multi-omics to investigate the selective functions of Rel and RelA, two closely related NF-κB proteins, in primary B lymphocytes activated via the B cell receptor. Despite large numbers of shared binding sites genome wide, Rel and RelA directed kinetically distinct cascades of gene expression in activated B cells. Single-cell RNA sequencing revealed marked heterogeneity of Rel- and RelA-specific responses, and sequential binding of these factors was not a major mechanism of protracted transcription. Moreover, nuclear co-expression of Rel and RelA led to functional antagonism between the factors. By rigorously identifying the target genes of each NF-κB subunit, these studies provide insights into exclusive functions of Rel and RelA in immunity and cancer.

Reduced expression of MYC increases longevity and enhances healthspan.

MYC is a highly pleiotropic transcription factor whose deregulation promotes cancer. In contrast, we find that Myc haploinsufficient (Myc(+/-)) mice exhibit increased lifespan. They show resistance to several age-associated pathologies, including osteoporosis, cardiac fibrosis, and immunosenescence. They also appear to be more active, with a higher metabolic rate and healthier lipid metabolism. Transcriptomic analysis reveals a gene expression signature enriched for metabolic and immune processes. The ancestral role of MYC as a regulator of ribosome biogenesis is reflected in reduced protein translation, which is inversely correlated with longevity. We also observe changes in nutrient and energy sensing pathways, including reduced serum IGF-1, increased AMPK activity, and decreased AKT, TOR, and S6K activities. In contrast to observations in other longevity models, Myc(+/-) mice do not show improvements in stress management pathways. Our findings indicate that MYC activity has a significant impact on longevity and multiple aspects of mammalian healthspan.

RNA-binding protein Nocte regulates Drosophila development by promoting translation reinitiation on mRNAs with long upstream open reading frames.

RNA-binding proteins (RBPs) with intrinsically disordered regions (IDRs) are linked to multiple human disorders, but their mechanisms of action remain unclear. Here, we report that one such protein, Nocte, is essential for Drosophila eye development by regulating a critical gene expression cascade at translational level. Knockout of nocte in flies leads to lethality, and its eye-specific depletion impairs eye size and morphology. Nocte preferentially enhances translation of mRNAs with long upstream open reading frames (uORFs). One of the key Nocte targets, glass mRNA, encodes a transcription factor critical for differentiation of photoreceptor neurons and accessory cells, and re-expression of Glass largely rescued the eye defects caused by Nocte depletion. Mechanistically, Nocte counteracts long uORF-mediated translational suppression by promoting translation reinitiation downstream of the uORF. Nocte interacts with translation factors eIF3 and Rack1 through its BAT2 domain, and a Nocte mutant lacking this domain fails to promote translation of glass mRNA. Notably, de novo mutations of human orthologs of Nocte have been detected in schizophrenia patients. Our data suggest that Nocte family of proteins can promote translation reinitiation to overcome long uORFs-mediated translational suppression, and disruption of this function can lead to developmental defects and neurological disorders.

HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.

Enhancers are crucial cis-regulatory elements that control gene expression in a cell-type-specific manner. Despite extensive genetic and computational studies, accurately predicting enhancer activity in different cell types remains a challenge, and the grammar of enhancers is still poorly understood. Here, we present HEAP (high-resolution enhancer activity prediction), an explainable deep learning framework for predicting enhancers and exploring enhancer grammar. The framework includes three modules that use grammar-based reasoning for enhancer prediction. The algorithm can incorporate DNA sequences and epigenetic modifications to obtain better accuracy. We use a novel two-step multi-task learning method, task adaptive parameter sharing (TAPS), to efficiently predict enhancers in different cell types. We first train a shared model with all cell-type datasets. Then we adapt to specific tasks by adding several task-specific subset layers. Experiments demonstrate that HEAP outperforms published methods and showcases the effectiveness of the TAPS, especially for those with limited training samples. Notably, the explainable framework HEAP utilizes post-hoc interpretation to provide insights into the prediction mechanisms from three perspectives: data, model architecture and algorithm, leading to a better understanding of model decisions and enhancer grammar. To the best of our knowledge, HEAP will be a valuable tool for insight into the complex mechanisms of enhancer activity.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data.

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

A dual-activity topoisomerase complex promotes both transcriptional activation and repression in response to starvation.

Topoisomerases are required to release topological stress generated by RNA polymerase II (RNAPII) during transcription. Here, we show that in response to starvation, the complex of topoisomerase 3b (TOP3B) and TDRD3 can enhance not only transcriptional activation, but also repression, which mimics other topoisomerases that can also alter transcription in both directions. The genes enhanced by TOP3B-TDRD3 are enriched with long and highly-expressed ones, which are also preferentially stimulated by other topoisomerases, suggesting that different topoisomerases may recognize their targets through a similar mechanism. Specifically, human HCT116 cells individually inactivated for TOP3B, TDRD3 or TOP3B topoisomerase activity, exhibit similarly disrupted transcription for both starvation-activated genes (SAGs) and starvation-repressed genes (SRGs). Responding to starvation, both TOP3B-TDRD3 and the elongating form of RNAPII exhibit concomitantly increased binding to TOP3B-dependent SAGs, at binding sites that overlap. Notably, TOP3B inactivation decreases the binding of elongating RNAPII to TOP3B-dependent SAGs while increased it to SRGs. Furthermore, TOP3B-ablated cells display reduced transcription of several autophagy-associated genes and autophagy per se. Our data suggest that TOP3B-TDRD3 can promote both transcriptional activation and repression by regulating RNAPII distribution. In addition, the findings that it can facilitate autophagy may account for the shortened lifespan of Top3b-KO mice.

Aberrant expression and localization of the RAP1 shelterin protein contribute to age-related phenotypes.

Short telomeres induce a DNA damage response (DDR) that evokes apoptosis and senescence in human cells. An extant question is the contribution of telomere dysfunction-induced DDR to the phenotypes observed in aging and telomere biology disorders. One candidate is RAP1, a telomere-associated protein that also controls transcription at extratelomeric regions. To distinguish these roles, we generated a knockin mouse carrying a mutated Rap1, which was incapable of binding telomeres and did not result in eroded telomeres or a DDR. Primary Rap1 knockin embryonic fibroblasts showed decreased RAP1 expression and re-localization away from telomeres, with an increased cytosolic distribution akin to that observed in human fibroblasts undergoing telomere erosion. Rap1 knockin mice were viable, but exhibited transcriptomic alterations, proinflammatory cytokine/chemokine signaling, reduced lifespan, and decreased healthspan with increased body weight/fasting blood glucose levels, spontaneous tumor incidence, and behavioral deficits. Taken together, our data present mechanisms distinct from telomere-induced DDR that underlie age-related phenotypes.

Transcriptome sequencing and metabolome analysis reveal the molecular mechanism of Salvia miltiorrhiza in response to drought stress.

Salvia miltiorrhiza is commonly used as a Chinese herbal medicine to treat different cardiovascular and cerebrovascular illnesses due to its active ingredients. Environmental conditions, especially drought stress, can affect the yield and quality of S. miltiorrhiza. However, moderate drought stress could improve the quality of S. miltiorrhiza without significantly reducing the yield, and the mechanism of this initial drought resistance is still unclear. In our study, transcriptome and metabolome analyses of S. miltiorrhiza under different drought treatment groups (CK, A, B, and C groups) were conducted to reveal the basis for its drought tolerance. We discovered that the leaves of S. miltiorrhiza under different drought treatment groups had no obvious shrinkage, and the malondialdehyde (MDA) contents as well as superoxide dismutase (SOD) and peroxidase (POD) activities dramatically increased, indicating that our drought treatment methods were moderate, and the leaves of S. miltiorrhiza began to initiate drought resistance. The morphology of root tissue had no significant change under different drought treatment groups, and the contents of four tanshinones significantly enhanced. In all, 5213, 6611, and 5241 differentially expressed genes (DEGs) were shared in the A, B, and C groups compared with the CK group, respectively. The results of KEGG and co-expression analysis showed that the DEGs involved in plant-pathogen interactions, the MAPK signaling pathway, phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction responded to drought stress and were strongly correlated with tanshinone biosynthesis. Furthermore, the results of metabolism analysis indicated that 67, 72, and 92 differentially accumulated metabolites (DAMs), including fumarate, ferulic acid, xanthohumol, and phytocassanes, which were primarily involved in phenylpropanoid biosynthesis, flavonoid biosynthesis, and diterpenoid biosynthesis pathways, were detected in these groups. These discoveries provide valuable information on the molecular mechanisms by which S. miltiorrhiza responds to drought stress and will facilitate the development of drought-resistant and high-quality S. miltiorrhiza production.

A survey on the algorithm and development of multiple sequence alignment.

Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy. Therefore, developing an efficient and accurate strategy for MSA has become one of the research hotspots in bioinformatics. In this work, we mainly summarize the algorithms for MSA and its applications in bioinformatics. To provide a structured and clear perspective, we systematically introduce MSA's knowledge, including background, database, metric and benchmark. Besides, we list the most common applications of MSA in the field of bioinformatics, including database searching, phylogenetic analysis, genomic analysis, metagenomic analysis and protein analysis. Furthermore, we categorize and analyze classical and state-of-the-art algorithms, divided into progressive alignment, iterative algorithm, heuristics, machine learning and divide-and-conquer. Moreover, we also discuss the challenges and opportunities of MSA in bioinformatics. Our work provides a comprehensive survey of MSA applications and their relevant algorithms. It could bring valuable insights for researchers to contribute their knowledge to MSA and relevant studies.

A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape.

The discovery of putative transcription factor binding sites (TFBSs) is important for understanding the underlying binding mechanism and cellular functions. Recently, many computational methods have been proposed to jointly account for DNA sequence and shape properties in TFBSs prediction. However, these methods fail to fully utilize the latent features derived from both sequence and shape profiles and have limitation in interpretability and knowledge discovery. To this end, we present a novel Deep Convolution Attention network combining Sequence and Shape, dubbed as D-SSCA, for precisely predicting putative TFBSs. Experiments conducted on 165 ENCODE ChIP-seq datasets reveal that D-SSCA significantly outperforms several state-of-the-art methods in predicting TFBSs, and justify the utility of channel attention module for feature refinements. Besides, the thorough analysis about the contribution of five shapes to TFBSs prediction demonstrates that shape features can improve the predictive power for transcription factors-DNA binding. Furthermore, D-SSCA can realize the cross-cell line prediction of TFBSs, indicating the occupancy of common interplay patterns concerning both sequence and shape across various cell lines. The source code of D-SSCA can be found at https://github.com/MoonLord0525/.

HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency.

MOTIVATION: Transcription factor (TF) binds to conservative DNA binding sites in different cellular environments and development stages by physical interaction with interdependent nucleotides. However, systematic computational characterization of the relationship between higher-order nucleotide dependency and TF-DNA binding mechanism in diverse cell types remains challenging. RESULTS: Here, we propose a novel multi-task learning framework HAMPLE to simultaneously predict TF binding sites (TFBS) in distinct cell types by characterizing higher-order nucleotide dependencies. Specifically, HAMPLE first represents a DNA sequence through three higher-order nucleotide dependencies, including k-mer encoding, DNA shape and histone modification. Then, HAMPLE uses the customized gate control and the channel attention convolutional architecture to further capture cell-type-specific and cell-type-shared DNA binding motifs and epigenomic languages. Finally, HAMPLE exploits the joint loss function to optimize the TFBS prediction for different cell types in an end-to-end manner. Extensive experimental results on seven datasets demonstrate that HAMPLE significantly outperforms the state-of-the-art approaches in terms of auROC. In addition, feature importance analysis illustrates that k-mer encoding, DNA shape, and histone modification have predictive power for TF-DNA binding in different cellular environments and are complementary to each other. Furthermore, ablation study, and interpretable analysis validate the effectiveness of the customized gate control and the channel attention convolutional architecture in characterizing higher-order nucleotide dependencies. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/ZhangLab312/Hample.

Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding.

MOTIVATION: Multiple sequence alignment (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is a Nondeterministic Polynomially complete problem, and the existing methods still have room to improve the accuracy. RESULTS: We propose Deep reinforcement learning with Positional encoding and self-Attention for MSA, based on deep reinforcement learning, to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing, we introduce self-attention and positional encoding to improve accuracy and reliability. Firstly, positional encoding encodes the position of the sequence to prevent the loss of nucleotide position information. Secondly, the self-attention model is used to extract the key features of the sequence. Then input the features into a multi-layer perceptron, which can calculate the insertion position of the gap according to the features. In addition, a novel reinforcement learning environment is designed to convert the classic progressive alignment into progressive column alignment, gradually generating each column's sub-alignment. Finally, merge the sub-alignment into the complete alignment. Extensive experiments based on several datasets validate our method's effectiveness for MSA, outperforming some state-of-the-art methods in terms of the Sum-of-pairs and Column scores. AVAILABILITY AND IMPLEMENTATION: The process is implemented in Python and available as open-source software from https://github.com/ZhangLab312/DPAMSA.

Impaired kidney function and the risk of all-cause mortality and cardiovascular disease among Chinese hypertensive adults: Using three different equations to estimate the glomerular filtration rate.

OBJECTIVES: We aimed to seek accurate assessments of the glomerular filtration rate (GFR) in a Chinese hypertensive population to identify individuals at high risk for chronic kidney disease (CKD) progression. Then, the risk of cardiovascular disease (CVD) and all-cause death due to kidney injury were further investigated under appropriate GFR-estimation equations. METHODS: In this prospective follow-up cohort study of 10,171 hypertensive patients, we compared the discrimination power of a trio of GFR-estimation equations using Harrell's C-index, measuring the model fit by calculating the Akaike information criterion. Univariate and multivariable logistic regression analyses were respectively used to calculate the hazard ratio (HR) and 95% confidence interval [CI] values for CKD progression. In addition, we also assessed the risk of CVD and all-cause death with impaired renal function using multivariable-adjusted Cox regression models. RESULTS: The Modification of Diet in Renal Disease (MDRD) equation showed the highest C-index range for the predicted probability of CKD progression in the fully adjusted model. During MDRD analysis, a low eGFR (60-89 mL/min/1.73m2 or < 60 mL/min/1.73m2) was an independent risk factor for CVD, especially stroke (1.28 [95% CI, 1.05-1.55] and 1.89 [95% CI, 1.08-3.31]), as well as all-cause mortality (1.28 [95% CI, 1.09-1.50] and 1.68 [95% CI, 1.01-2.78]). CONCLUSIONS: The MDRD equation seems to be more suitable for screening CKD progression in Chinese hypertensive populations, targeting potential risk factors for effective prevention to reduce renal impairment so as to further limit CVD morbidity and mortality.

Local augmented graph neural network for multi-omics cancer prognosis prediction and analysis.

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phase, an encoder learns the conditional distribution of the multi-omics data. As a generative model, a decoder takes the conditional distribution and the original feature as inputs to generate the enhanced features. The cancer prognosis prediction model consists of a two-layer graph convolutional neural network and a Cox proportional risk network. The Cox proportional risk network consists of fully connected layers. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. LAGProg improved the C-index values by an average of 8.5% over the state-of-the-art graph neural network method. Moreover, we confirmed that the local augmentation technique could enhance the model's ability to represent multi-omics features, improve the model's robustness to missing multi-omics features, and prevent the model's over-smoothing during training. Finally, based on genes identified through differential expression analysis, we discovered 13 prognostic markers highly associated with breast cancer, among which ten genes have been proved by literature review.

A dual-activity topoisomerase complex regulates mRNA translation and turnover.

Topoisomerase 3ß (TOP3B) and TDRD3 form a dual-activity topoisomerase complex that interacts with FMRP and can change the topology of both DNA and RNA. Here, we investigated the post-transcriptional influence of TOP3B and associated proteins on mRNA translation and turnover. First, we discovered that in human HCT116 colon cancer cells, knock-out (KO) of TOP3B had similar effects on mRNA turnover and translation as did TDRD3-KO, while FMRP-KO resulted in rather distinct effects, indicating that TOP3B had stronger coordination with TDRD3 than FMRP in mRNA regulation. Second, we identified TOP3B-bound mRNAs in HCT116 cells; we found that while TOP3B did not directly influence the stability or translation of most TOP3B target mRNAs, it stabilized a subset of target mRNAs but had a more complex effect on translation-enhancing for some mRNAs whereas reducing for others. Interestingly, a point mutation that specifically disrupted TOP3B catalytic activity only partially recapitulated the effects of TOP3B-KO on mRNA stability and translation, suggesting that the impact of TOP3B on target mRNAs is partly linked to its ability to change topology of mRNAs. Collectively, our data suggest that TOP3B-TDRD3 can regulate mRNA translation and turnover by mechanisms that are dependent and independent of topoisomerase activity.

Revealing the material basis and mechanism for the inhibition of intestinal peristalsis by Zingiber officinale Roscoe through integrated metabolomics, serum pharmacochemistry, and network pharmacology.

Abnormal relaxation and contraction of intestinal smooth muscle can cause various intestinal diseases. Diarrhea is a common and important public health problem worldwide in epidemiology. Zingiber officinale Roscoe (fresh ginger) has been found to treat diarrhea, but the material basis and mechanism of action that inhibits intestinal peristalsis remain unclear. Metabolomics and serum pharmacology were used to identify differential metabolites, metabolic pathways, and pharmacodynamic substances, and were then combined with network pharmacology to explore the potential targets of ginger that inhibit intestinal peristalsis during diarrhea treatment, and the targets identified were verified using molecular docking and molecular dynamic simulation. We found that 25 active components of ginger (the six most relevant components), 35 potential key targets (three core targets), 40 differential metabolites (four key metabolites), and four major metabolic pathways were involved in the process by which ginger inhibits intestinal peristalsis during diarrhea treatment. This study reveals the complex mechanism of action and pharmacodynamic material basis of ginger in the inhibition of intestinal peristalsis, and this information helps in the development of new Chinese medicine to treat diarrhea and lays the foundation for the clinical application of ginger.

Maternal exposure to dibutyl phthalate (DBP) impairs angiogenesis and AR signalling pathway through suppression of TGFB1I1 in hypospadias offspring.

Early exposure to dibutyl phthalate (DBP) can cause hypospadias in newborn foetuses. However, the underlying molecular mechanism is not well defined. Aberrant angiogenesis is associated with various dysplasias including urogenital deficits. In vivo and in vitro angiogenesis assays showed reduced angiogenesis in the hypospadias group and DBP exposed group. RNA-sequencing analysis of DBP-treated HUVECs revealed decreased expression of transforming growth factor beta 1-induced transcript 1 (TGFB1I1) and a significantly enriched angiogenesis-associated pathway. Further experiments revealed that decreased TGFB1I1 expression was associated with disrupted tube formation and migration, which resulted in decreased angiogenesis. Functional assays revealed that the overexpression of TGFB1I1 promoted tube formation and migration of HUVECs in the DBP-treated group. Moreover, we showed that the transcription factor AR was regulated by TGFB1I1 through inhibiting its translocation from the cytoplasm to the nucleus. Together, our results identified TGFB1I1 as a component of aberrant angiogenesis in hypospadias rats and its interaction with AR might be a potential target for hypospadias development.

Face presentation at term: incidence, risk factors and influence on maternal and neonatal outcomes.

OBJECTIVES: The incidence, diagnosis, management and outcome of face presentation at term were analysed. METHODS: A retrospective, gestational age-matched case-control study including 27 singletons with face presentation at term was conducted between April 2006 and February 2021. For each case, four women who had the same gestational age and delivered in the same month with vertex position and singletons were selected as the controls (control group, n = 108). Conditional logistic regression was used to assess the risk factors of face presentation. The maternal and neonatal outcomes of the face presentation group were followed up. RESULTS: The incidence of face presentation at term was 0.14‰. After conditional logistic regression, the two factors associated with face presentation were high parity (adjusted odds ratio [aOR] 2.76, 95% CI 1.19-6.39)] and amniotic fluid index > 18 cm (aOR 2.60, 95% CI 1.08-6.27). Among the 27 cases, the diagnosis was made before the onset of labor, during the latent phase of labor, during the active phase of labor, and during the cesarean section in 3.7% (1/27), 40.7% (11/27), 11.1% (3/27) and 44.4% (12/27) of cases, respectively. In one case of cervical dilation with a diameter of 5 cm, we innovatively used a vaginal speculum for rapid diagnosis of face presentation. The rate of cesarean section and postpartum haemorrhage ≥ 500 ml in the face presentation group was higher than that of the control group (88.9% vs. 13.9%, P < 0.001, and 14.8% vs. 2.8%, P = 0.024), but the Apgar scores were similar in both sets of newborns. Among the 27 cases of face presentation, there were three cases of adverse maternal and neonatal outcomes, including one case of neonatal right brachial plexus injury and two cases of severe laceration of the lower segment of the uterus with postpartum haemorrhage ≥ 1000 ml. CONCLUSIONS: Face presentation was rare. Early diagnosis is difficult, and thus easily neglected. High parity and amniotic fluid index > 18 cm are risk factors for face presentation. An early diagnosis and proper management of face presentation could lead to good maternal and neonatal outcomes.

First Report of Leaf Spot Disease on Lonicera japonica Caused by Nigrospora oryzae in China.

Lonicera japonica Thunb. is a traditional Chinese medicinal plant, which widely cultivated in China, Japan and Korea. From August to October in 2021 and 2022, severe leaf spots symptoms were observed on L. japonica in medicinal botanical garden of Shandong University of Traditional Chinese Medicine (36°55'89"N, 116°79'91"E), Jinan, Shandong Province, China. The disease incidence was above 80% in the 25 acre cultivation area. Early symptoms were small brown spots on the leaves. Then the number of small spots gradually increased and spread over the entire leaves. The small brown spots seldom merge together to form larger lesions. Leaves with typical symptoms were collected from twenty individual plants, and cut into small 5×5 mm fragments in the junction of infected and healthy tissues. The fragments were sterilized in 75% ethanol for 30 s and 1% NaClO for 60 s, rinsed three times in sterile water, and then placed on potato dextrose agar (PDA). After 3 days of incubation at 25°C, fungal plugs along the edge of the colony were cut and transferred to new PDA for purification. A total number of 23 colonies with similar morphological characteristics were obtained, and three representative strains (Lj14, Lj18 and Lj20) were selected for subsequent study. The colonies grew rapidly on PDA and covered the entire petri dish in 4 days. Colonies had abundant aerial hyphae, initially white, round, later turning gray and black. Conidia were oblate or nearly spherical, single-celled, black, and measured in size from 9.6 to 13.2 µm × 7.9 to 16.1 µm in diameter (n=150) (Figure S1). The observed characteristics were close to those of Nigrospora spp. ( Wang et al. 2017). The genomic DNA was extracted, and PCR amplification of the rDNA internal transcribed spacer (ITS), ß-tubulin gene (TUB), and translation elongation factor 1-alpha gene (TEF1) were completed by primers ITS1/ITS4, Bt2a/Bt2b and EF1-728F/EF1-986R (Carbone and Kohn, 1999). Sequences were deposited in GenBank (accession nos. OR936661, OR936662, OR936671 for ITS, OR947626, OR947627, OR947628 for TUB, and OR947629, OR947630, OR947631 for TEF1 sequences, respectively). BLAST analyses of ITS (OR936661), TUB(OR947626) and TEF1 (OR947629) sequences exhibited 100% (487 bp out of 487 bp), 99.48% (380 bp out of 382 bp), and 99.6% (248 bp out of 249 bp) similarity to the sequences of N. oryzae strains KoLRI_053384 (MZ855426), LC2991 (KY019496) and LC7307 (KY019409), respectively. Lj14, Lj18 and Lj20 formed a clade with N. oryzae LC6763 and LC2991 in phylogenetic tree (Figure S2). Based on morphological and molecular evidence, the pathogen was identified as N. oryzae (Berk. &Broome) Petch. To fulfill Koch's postulates, the pathogenicity was tested in vivo experiments. Thirty non-wounded healthy leaves of ten intact plants were inoculated with 10 µl spore suspension (106 spores/ml) of three strains, respectively. As negative control, thirty leaves of ten healthy plants were inoculated with sterile water. The inoculated plants were placed at 28°C in the growth chamber with high relative humidity. The pathogenicity tests were repeated three times. Distinct symptoms similar to that of natural conditions were observed on the leaves of inoculated plants after 4 to 7 days. The strain was reisolated from the lesions and identified as N. oryzae by morphological features and ITS sequence. The pathogen has been reported to cause leaf spot disease on tobacco (Wang et al. 2022) and asiatic dayflower (Qiu et al. 2022). To our knowledge, this is the first report of leaf spot caused by N. oryzae on Lonicera japonica in China. The research will be helpful for leaf spot disease control.

Quality evaluation of Lonicerae Japonicae Flos from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition.

INTRODUCTION: Lonicerae Japonicae Flos (LJF) is widely used in food and traditional Chinese medicine. To meet demand, Lonicera japonica Thunb. is widely cultivated in many provinces of China. However, reported studies on the quality evaluation of LJF only used a single or a few active components as indicators, which could not fully reflect the quality of LJF. OBJECTIVES: In the present study, we aimed to develop a methodology for comprehensively evaluating the quality of LJF from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition. MATERIALS AND METHODS: The HPLC method was developed for fingerprint analysis and was used to determine the contents of 19 components of LJF. To distinguish between samples and identify differential components, similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis were performed. RESULTS: The HPLC fingerprint was established. Using the developed method, the contents of 19 components recognized in the fingerprint analysis were determined. Samples from different origins could be effectively distinguished. CONCLUSIONS: HPLC fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition is an efficient method for evaluating LJF.