scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.

Subcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellular localization, very few of these computational methods have been designed to predict subcellular localization with multiple localization annotations, and their generalization performance could be improved. In this study, the prediction model MSlocPRED was constructed to identify multi-label mRNA subcellular localization. First, the preprocessed Dataset 1 and Dataset 2 are transformed into the form of images. The proposed MDNDO-SMDU resampling technique is then used to balance the number of samples in each category in the training dataset. Finally, deep transfer learning was used to construct the predictive model MSlocPRED to identify subcellular localization for 16 classes (Dataset 1) and 18 classes (Dataset 2). The results of comparative tests of different resampling techniques show that the resampling technique proposed in this study is more effective in preprocessing for subcellular localization. The prediction results of the datasets constructed by intercepting different NC end (Both the 5' and 3' untranslated regions that flank the protein-coding sequence and influence mRNA function without encoding proteins themselves.) lengths show that for Dataset 1 and Dataset 2, the prediction performance is best when the NC end is intercepted by 35 nucleotides, respectively. The results of both independent testing and five-fold cross-validation comparisons with established prediction tools show that MSlocPRED is significantly better than established tools for identifying multi-label mRNA subcellular localization. Additionally, to understand how the MSlocPRED model works during the prediction process, SHapley Additive exPlanations was used to explain it. The predictive model and associated datasets are available on the following github: https://github.com/ZBYnb1/MSlocPRED/tree/main.

ILYCROsite: Identification of lysine crotonylation sites based on FCM-GRNN undersampling technique.

Protein lysine crotonylation is an important post-translational modification that regulates various cellular activities. For example, histone crotonylation affects chromatin structure and promotes histone replacement. Identification and understanding of lysine crotonylation sites is crucial in the field of protein research. However, due to the increasing amount of non-histone crotonylation sites, existing classifiers based on traditional machine learning may encounter performance limitations. In order to address this problem, a novel deep learning-based model for identifying crotonylation sites is presented in this study, given the unique advantages of deep learning techniques for sequence data analysis. In this study, an MLP-Attention-based model was developed for the identification of crotonylation sites. Firstly, three feature extraction strategies, namely Amino Acid Composition, K-mer, and Distance-based residue features extraction strategy, were used to encode crotonylated and non-crotonylated sequences. Then, in order to balance the training dataset, the FCM-GRNN undersampling algorithm combining fuzzy clustering and generalized neural network approaches was introduced. Finally, to improve the effectiveness of crotonylation site identification, we explored various classification algorithms, and based on the relevant experimental performance comparisons, the multilayer perceptron (MLP) combined with the superimposed self-attention mechanism was finally selected to construct the prediction model ILYCROsite. The results obtained from independent testing and five-fold cross-validation demonstrated that the model proposed in this study, ILYCROsite, had excellent performance. Notably, on the independent test set, ILYCROsite achieves an AUC value of 87.93 %, which is significantly better than the existing state-of-the-art models. In addition, SHAP (Shapley Additive exPlanations) values were used to analyze the importance of features and their impact on model predictions. Meanwhile, in order to facilitate researchers to use the prediction model constructed in this study, we developed a prediction program to identify the crotonylation sites in a given protein sequence. The data and code for this program are available at: https://github.com/wmqskr/ILYCROsite.

Advancing microRNA target site prediction with transformer and base-pairing patterns.

MicroRNAs (miRNAs) are short non-coding RNAs involved in various cellular processes, playing a crucial role in gene regulation. Identifying miRNA targets remains a central challenge and is pivotal for elucidating the complex gene regulatory networks. Traditional computational approaches have predominantly focused on identifying miRNA targets through perfect Watson-Crick base pairings within the seed region, referred to as canonical sites. However, emerging evidence suggests that perfect seed matches are not a prerequisite for miRNA-mediated regulation, underscoring the importance of also recognizing imperfect, or non-canonical, sites. To address this challenge, we propose Mimosa, a new computational approach that employs the Transformer framework to enhance the prediction of miRNA targets. Mimosa distinguishes itself by integrating contextual, positional and base-pairing information to capture in-depth attributes, thereby improving its predictive capabilities. Its unique ability to identify non-canonical base-pairing patterns makes Mimosa a standout model, reducing the reliance on pre-selecting candidate targets. Mimosa achieves superior performance in gene-level predictions and also shows impressive performance in site-level predictions across various non-human species through extensive benchmarking tests. To facilitate research efforts in miRNA targeting, we have developed an easy-to-use web server for comprehensive end-to-end predictions, which is publicly available at http://monash.bioweb.cloud.edu.au/Mimosa.

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism.

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

MOTIVATION: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNA's secondary structure. RESULTS: In this study, we propose Allocator, a multi-view parallel deep learning framework that seamlessly integrates the RNA sequence-level and structure-level information, enhancing the prediction of mRNA multi-localization. The Allocator models equip four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating multilayer perceptron and multi-head self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocator's superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. AVAILABILITY AND IMPLEMENTATION: The webserver of Allocator is available at http://Allocator.unimelb-biotools.cloud.edu.au; the source code and datasets are available on GitHub (https://github.com/lifuyi774/Allocator) and Zenodo (https://doi.org/10.5281/zenodo.13235798).

microRNA maintains nutrient homeostasis in the symbiont-host interaction.

Endosymbionts provide essential nutrients for hosts, promoting growth, development, and reproduction. However, the molecular regulation of nutrient transport from endosymbiont to host is not well understood. Here, we used bioinformatic analysis, RNA-Sequencing, luciferase assays, RNA immunoprecipitation, and in situ hybridization to show that a bacteriocyte-distributed MRP4 gene (multidrug resistance-associated protein 4) is negatively regulated by a host (aphid)-specific microRNA (miR-3024). Targeted metabolomics, microbiome analysis, vitamin B6 (VB6) supplements, 3D modeling/molecular docking, in vitro binding assays (voltage clamp recording and microscale thermophoresis), and functional complementation of Escherichia coli were jointly used to show that the miR-3024/MRP4 axis controls endosymbiont (Serratia)-produced VB6 transport to the host. The supplementation of miR-3024 increased the mortality of aphids, but partial rescue was achieved by providing an external source of VB6. The use of miR-3024 as part of a sustainable aphid pest-control strategy was evaluated by safety assessments in nontarget organisms (pollinators, predators, and entomopathogenic fungi) using virus-induced gene silencing assays and the expression of miR-3024 in transgenic tobacco. The supplementation of miR-3024 suppresses MRP4 expression, restricting the number of membrane channels, inhibiting VB6 transport, and ultimately killing the host. Under aphids facing stress conditions, the endosymbiont titer is decreased, and the VB6 production is also down-regulated, while the aphid's autonomous inhibition of miR-3024 enhances the expression of MRP4 and then increases the VB6 transport which finally ensures the VB6 homeostasis. The results confirm that miR-3024 regulates nutrient transport in the endosymbiont-host system and is a suitable target for sustainable pest control.

Schistosomicidal effects of histone acetyltransferase inhibitors against Schistosoma japonicum juveniles and adult worms in vitro.

BACKGROUND: Schistosomiasis is a relatively neglected parasitic disease that afflicts more than 250 million people worldwide, for which the control strategy relies mainly on mass treatment with the only available drug, praziquantel (PZQ). This approach is not sustainable and is a priority for developing novel drug candidates for the treatment and control of schistosomiasis. METHODOLOGYS/PRINCIPAL FINDINGS: In our previous study, we found that DW-3-15, a kind of PZQ derivative, could significantly downregulate the expression of the histone acetyltransferase of Schistosoma japonicum (SjHAT). In this study, several commercially available HAT inhibitors, A485, C646 and curcumin were screened in vitro to verify their antischistosomal activities against S. japonicum juveniles and adults. Parasitological studies and scanning electron microscopy were used to study the primary action characteristics of HAT inhibitors in vitro. Quantitative real-time PCR was employed to detect the mRNA level of SjHAT after treatment with different HAT inhibitors. Our results demonstrated that curcumin was the most effective inhibitor against both juveniles and adults of S. japonicum, and its schistosomicidal effects were time- and dose dependent. However, A485 and C646 had limited antischistosomal activity. Scanning electron microscopy demonstrated that in comparison with DW-3-15, curcumin caused similar tegumental changes in male adult worms. Furthermore, both curcumin and DW-3-15 significantly decreased the SjHAT mRNA level, and curcumin dose-dependently reduced the SjHAT expression level in female, male and juvenile worms. CONCLUSIONS: Among the three commercially available HATs, curcumin was the most potent against schistosomes. Both curcumin and our patent compound DW-3-15 markedly downregulated the expression of SjHAT, indicating that SjHAT may be a potential therapeutic target for developing novel antischistosomal drug candidates.

Inhibiting NF-κB inducing kinase improved the motor performance of ALS animal model.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a typical neurodegenerative disorder typically characterized by inflammation activation. However, the relationship between non-canonical NF-κB (ncNF-κB) pathway activation and ALS progression is not clear. METHODS: We tested the ncNF-κB pathway in the ALS animal model including hSOD1-G93A transgenic mice and TBK1 deletion mice.We treated age-matched SOD1-G93A mice with B022 (a NIK inhibitor) to investigate the role of NIK in the ALS animal model. We also established a new mice model by crossing SOD1-G93A mice with NIK+/- mice to further evaluate the interrelationship between the NIK and the disease progression in ALS animal model. RESULTS: In this study, we found the ncNF-κB pathway was activated in SOD1-G93A animal model and TBK1 deletion model. Inhibition of NIK activity by small molecule B022 significantly improved the motor performance of the ALS animal model. However, NIK deletion enhanced the mutant SOD1 toxicity by inflammatory infiltration. CONCLUSION: TBK1 deletion and mutant SOD1 shared the common pathological feature possibly via effects on NIK activation and inhibitor of NIK could be a novel strategy for treating ALS.

Widely Targeted Metabolomic Analysis Reveals the Improvement in Panax notoginseng Triterpenoids Triggered by Arbuscular Mycorrhizal Fungi via UPLC-ESI-MS/MS.

Panax notoginseng is a highly valued perennial medicinal herb in China and is widely used in clinical treatments. The main purpose of this study was to elucidate the changes in the composition of P. notoginseng saponins (PNSs), which are the main bioactive substances, triggered by arbuscular mycorrhizal fungi (AMF) via ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS). A total of 202 putative terpenoid metabolites were detected, of which 150 triterpene glycosides were identified, accounting for 74.26% of the total. Correlation analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of the metabolites revealed that the samples treated with AMF (group Ce) could be clearly separated from the CK samples. In total, 49 differential terpene metabolites were identified between the Ce and CK groups, of which 38 and 11 metabolites were upregulated and downregulated, respectively, and most of the upregulated differentially abundant metabolites were mainly triterpene glycosides. The relative abundances of the two major notoginsenosides (MNs), ginsenosides Rd and Re, and 13 rare notoginsenosides (RNs), significantly increased. The differential saponins, especially RNs, were more easily clustered into one branch and had a high positive correlation. It could be concluded that the biosynthesis and accumulation of some RNs share the same pathways as those triggered by AMF. This study provides a new way to obtain more notoginsenoside resources, particularly RNs, and sheds new light on the scientization and rationalization of the use of AMF agents in the ecological planting of medicinal plants.

Mesoporous Silicon with Strontium-Powered Poly(Lactic-Co-Glycolic acid)/Gelatin-Based Dressings Facilitate Skin Tissue Repair.

Purpose: Functional inorganic nanomaterials (NMs) are widely exploited as bioactive materials and drug depots. The lack of a stable form of application of NMs at the site of skin injury, may impede the removal of the debridement, elevate pH, induce tissue toxicity, and limit their use in skin repair. This necessitates the advent of innovative wound dressings that overcome the above limitations. The overarching objective of this study was to exploit strontium-doped mesoporous silicon particles (PSiSr) to impart multifunctionality to poly(lactic-co-glycolic acid)/gelatin (PG)-based fibrous dressings (PG@PSiSr) for excisional wound management. Methods: Mesoporous silicon particles (PSi) and PSiSr were synthesized using a chemo-synthetic approach. Both PSi and PSiSr were incorporated into PG fibers using electrospinning. A series of structure, morphology, pore size distribution, and cumulative pH studies on the PG@PSi and PG@PSiSr membranes were performed. Cytocompatibility, hemocompatibility, transwell migration, scratch wound healing, and delineated angiogenic properties of these composite dressings were tested in vitro. The biocompatibility of composite dressings in vivo was assessed by a subcutaneous implantation model of rats, while their potential for wound healing was discerned by implantation in a full-thickness excisional defect model of rats. Results: The PG@PSiSr membranes can afford the sustained release of silicon ions (Si4+) and strontium ions (Sr2+) for up to 192 h as well as remarkably promote human umbilical vein endothelial cells (HUVECs) and NIH-3T3 fibroblasts migration. The PG@PSiSr membranes also showed better cytocompatibility, hemocompatibility, and significant formation of tubule-like networks of HUVECs in vitro. Moreover, PG@PSiSr membranes also facilitated the infiltration of host cells and promoted the deposition of collagen while reducing the accumulation of inflammatory cells in a subcutaneous implantation model in rats as assessed for up to day 14. Further evaluation of membranes transplanted in a full-thickness excisional wound model in rats showed rapid wound closure (PG@SiSr vs control, 96.1% vs 71.7%), re-epithelialization, and less inflammatory response alongside skin appendages formation (eg, blood vessels, glands, hair follicles, etc.). Conclusion: To sum up, we successfully fabricated PSiSr particles and prepared PG@PSiSr dressings using electrospinning. The PSiSr-mediated release of therapeutic ions, such as Si4+ and Sr2+, may improve the functionality of PLGA/Gel dressings for an effective wound repair, which may also have implications for the other soft tissue repair disciplines.

Catalysts of Healing: A Symphony of Synthesis and Clinical Artistry in Small-Molecule Agents for Breast Cancer Alleviation.

Breast cancer, characterized by its molecular intricacy, has witnessed a surge in targeted therapeutics owing to the rise of small-molecule drugs. These entities, derived from cutting-edge synthetic routes, often encompassing multistage reactions and chiral synthesis, target a spectrum of oncogenic pathways. Their mechanisms of action range from modulating hormone receptor signaling and inhibiting kinase activity, to impeding DNA damage repair mechanisms. Clinical applications of these drugs have resulted in enhanced patient survival rates, reduction in disease recurrence, and improved overall therapeutic indices. Notably, certain molecules have showcased efficacy in drug-resistant breast cancer phenotypes, highlighting their potential in addressing treatment challenges. The evolution and approval of small-molecule drugs have ushered in a new era for breast cancer therapeutics. Their tailored synthetic pathways and defined mechanisms of action have augmented the precision and efficacy of treatment regimens, paving the way for improved patient outcomes in the face of this pervasive malignancy. The present review embarks on a detailed exploration of small-molecule drugs that have secured regulatory approval for breast cancer treatment, emphasizing their clinical applications, synthetic pathways, and distinct mechanisms of action.

Improvement of Panax notoginseng saponin accumulation triggered by methyl jasmonate under arbuscular mycorrhizal fungi.

Panax notoginseng is a highly valued perennial medicinal herb plant in Yunnan Province, China, and the taproots are the main medicinal parts that are rich in active substances of P. notoginseng saponins. The main purpose of this study is to uncover the physiological and molecular mechanism of Panax notoginseng saponin accumulation triggered by methyl jasmonate (MeJA) under arbuscular mycorrhizal fungi (AMF) by determining physiological indices, high-throughput sequencing and correlation analysis. Physiological results showed that the biomass and saponin contents of P. notoginseng, the concentrations of jasmonic acids (JAs) and the key enzyme activities involved in notoginsenoside biosynthesis significantly increased under AMF or MeJA, but the interactive treatment of AMF and MeJA weakened the effect of AMF, suggesting that a high concentration of endogenous JA have inhibitory effect. Transcriptome sequencing results indicated that differential expressed genes (DEGs) involved in notoginsenoside and JA biosynthesis were significantly enriched in response to AMF induction, e.g., upregulated genes of diphosphocytidyl-2-C-methyl-d-erythritol kinases (ISPEs), cytochrome P450 monooxygenases (CYP450s)_and glycosyltransferases (GTs), while treatments AMF-MeJA and salicylhydroxamic acid (SHAM) decreased the abundance of these DEGs. Interestingly, a high correlation presented between any two of saponin contents, key enzyme activities and expression levels of DEGs. Taken together, the inoculation of AMF can improve the growth and saponin accumulation of P. notoginseng by strengthening the activities of key enzymes and the expression levels of encoding genes, in which the JA regulatory pathway is a key link. This study provides references for implementing ecological planting of P. notoginseng, improving saponin accumulation and illustrating the biosynthesis mechanism.

MERITS: a web-based integrated Mycobacterial PE/PPE protein database.

Motivation: PE/PPE proteins, highly abundant in the Mycobacterium genome, play a vital role in virulence and immune modulation. Understanding their functions is key to comprehending the internal mechanisms of Mycobacterium. However, a lack of dedicated resources has limited research into PE/PPE proteins. Results: Addressing this gap, we introduce MycobactERIal PE/PPE proTeinS (MERITS), a comprehensive 3D structure database specifically designed for PE/PPE proteins. MERITS hosts 22 353 non-redundant PE/PPE proteins, encompassing details like physicochemical properties, subcellular localization, post-translational modification sites, protein functions, and measures of antigenicity, toxicity, and allergenicity. MERITS also includes data on their secondary and tertiary structure, along with other relevant biological information. MERITS is designed to be user-friendly, offering interactive search and data browsing features to aid researchers in exploring the potential functions of PE/PPE proteins. MERITS is expected to become a crucial resource in the field, aiding in developing new diagnostics and vaccines by elucidating the sequence-structure-functional relationships of PE/PPE proteins. Availability and implementation: MERITS is freely accessible at http://merits.unimelb-biotools.cloud.edu.au/.

Characterization of two Bursicon genes and their association with wing development in the brown citrus aphid, Aphis citricidus.

The tanning hormone, Bursicon, is a neuropeptide secreted by the insect nervous system that functions as a heterodimer composed of Burs-α and Burs-ß subunits. It plays a critical role in the processes of cuticle tanning and wing expansion in insects. In this study, we successfully identified the AcBurs-α and AcBurs-ß genes in Aphis citricidus. The open reading frames of AcBurs-α and AcBurs-ß were 480 and 417 bp in length, respectively. Both AcBurs-α and AcBurs-ß exhibited 11 conserved cysteine residues. AcBurs-α and AcBurs-ß were expressed during all developmental stages of A. citricidus and showed high expression levels in the winged aphids. To investigate the potential role of AcBurs-α and AcBurs-ß in wing development, we employed RNA interference (RNAi) techniques. With the efficient silencing of AcBurs-α (44.90%) and AcBurs-ß (52.31%), malformed wings were induced in aphids. The proportions of malformed wings were 22.50%, 25.84%, and 38.34% in dsAcBurs-α-, dsAcBur-ß-, and dsAcBurs-α + dsAcBur-ß-treated groups, respectively. Moreover, feeding protein kinase A inhibitors (H-89) also increased the proportion of malformed wings to 30.00%. Feeding both double-stranded RNA and inhibitors (H-89) significantly downregulated the wing development-related genes nubbin, vestigial, notch and spalt major. Silence of vestigial through RNAi also led to malformed wings. Meanwhile, the exogenous application of 3 hormones that influence wing development did not affect the expression level of AcBursicon genes. These findings indicate that AcBursicon genes plays a crucial role in wing development in A. citricidus; therefore, it represents a potential molecular target for the control of this pest through RNAi-based approaches.

Multidrug resistance of Botrytis cinerea associated with its adaptation to plant secondary metabolites.

Fungicides are an effective way to control gray mold of grapes, but the pathogen Botrytis cinerea can develop resistance, overcoming the effectiveness of a fungicide that is repeatedly applied. More importantly, the emergence of multidrug resistance (MDR) in the field, where multiple fungicides with different modes of action simultaneously lose their efficacies, is a significant concern. MDR is associated with ATP-binding cassette (ABC) transporters of the pathogen, and certain plant secondary metabolites (PSMs) stimulate the upregulation of ABC transporters, we hypothesized that the pathogen's preadaptation to PSMs might contribute to MDR development. To test this in B. cinerea, ten PSMs, namely, resveratrol, reserpine, chalcone, flavanone, eugenol, farnesol, anethene, camptothecin, salicylic acid, and psoralen, were selected based on their association with ABC transporters involved in fungicide resistance. B. cinerea strain B05.10 was continuously transferred for 15 generations on potato dextrose agar amended with a PSM (PDAP), and sensitivities to PSMs and fungicides were examined on the 5th, 10th, and 15th generations. RNA was extracted from B. cinerea from the selected generations. After 15 generations of culture transfers, an up-regulation was observed in the expression of ABC transporter-encoding genes BcatrB, BcatrD, and BcatrK using quantitative polymerase chain reaction (qPCR). This upregulation was found to contribute to MDR of B. cinerea against two or more fungicides, among azoxystrobin, boscalid, fludioxonil, difenoconazole, prochloraz, and pyrimethanil. This finding was confirmed through genetic transformation. The decreased sensitivity of B. cinerea to fungicides was confirmed as a subsequent MDR phenotype after exposure to camptothecin, flavanone, and resveratrol. Besides, transcriptome analysis also revealed the upregulation of transcription factors related to ABC expression following resveratrol exposure. This suggests that PSMs contributed to inducing preadaptation of B. cinerea, leading to subsequent MDR.IMPORTANCEThe emergence of MDR in plant pathogens is a threat to plant disease management and leads to the use of excessive fungicides. Botrytis cinerea is of particular concern because its MDR has widely emerged in the field. Understanding its genesis is the first step for controlling MDR. In this study, the contribution of PSMs to MDR has been examined. Effective management of this pathogen in agroecosystems relies on a better understanding of how it copes with phytochemicals or fungicides.

Factors associated with eating rate: a systematic review and narrative synthesis informed by socio-ecological model.

Accumulating evidence shows associations between rapid eating and overweight. Modifying eating rate might be a potential weight management strategy without imposing additional dietary restrictions. A comprehensive understanding of factors associated with eating speed will help with designing effective interventions. The aim of this review was to synthesise the current state of knowledge on the factors associated with eating rate. The socio-ecological model (SEM) was utilised to scaffold the identified factors. A comprehensive literature search of eleven databases was conducted to identify factors associated with eating rate. The 104 studies that met the inclusion criteria were heterogeneous in design and methods of eating rate measurement. We identified thirty-nine factors that were independently linked to eating speed and mapped them onto the individual, social and environmental levels of the SEM. The majority of the reported factors pertained to the individual characteristics (n = 20) including demographics, cognitive/psychological factors and habitual food oral processing behaviours. Social factors (n = 11) included eating companions, social and cultural norms, and family structure. Environmental factors (n = 8) included food texture and presentation, methods of consumption or background sounds. Measures of body weight, food form and characteristics, food oral processing behaviours and gender, age and ethnicity were the most researched and consistent factors associated with eating rate. A number of other novel and underresearched factors emerged, but these require replication and further research. We highlight directions for further research in this space and potential evidence-based candidates for interventions targeting eating rate.