Prediction Of Protein-protein Interactions Research Articles

The Inhibitory Effects of Maclurin on Fatty Acid Synthase and Adipocyte Differentiation

Obesity is a complex health condition characterized by excessive adipose tissue accumulation, leading to significant metabolic disturbances such as insulin resistance and cardiovascular diseases. Fatty acid synthase (FAS), a key enzyme in lipogenesis, has been identified as a potential therapeutic target for obesity due to its role in adipocyte differentiation and lipid accumulation. This study employed a multidisciplinary approach involving in silico and in vitro analyses to investigate the anti-adipogenic properties of maclurin, a natural phenolic compound derived from Morus alba. Using SwissDock software (ChEMBL version 23), we predicted protein interactions and demonstrated a high probability (95.6%) of maclurin targeting FAS, surpassing the interaction rates of established inhibitors like cerulenin. Docking simulations revealed maclurin’s superior binding affinity to FAS, with a binding score of −7.3 kcal/mol compared to −6.7 kcal/mol for cerulenin. Subsequent in vitro assays confirmed these findings, with maclurin effectively inhibiting FAS activity in a concentration-dependent manner in 3T3-L1 adipocytes, without compromising cell viability. Furthermore, maclurin treatment resulted in significant reductions in lipid accumulation and the downregulated expression of critical adipogenic genes such as PPARγ, C/EBPα, and FAS, indicating the suppression of adipocyte differentiation. Maclurin shows potential as a novel FAS inhibitor with significant anti-adipogenic effects, offering a promising therapeutic avenue for the treatment and prevention of obesity.

Integrated metabolomic and transcriptomic analysis reveals biosynthesis mechanism of flavone and caffeoylquinic acid in chrysanthemum

BackgroundChrysanthemum morifolium ‘HangBaiJu’, a popular medicinal and edible plant, exerts its biological activities primarily through the presence of flavones and caffeoylquinic acids (CQAs). However, the regulatory mechanism of flavone and CQA biosynthesis in the chrysanthemum capitulum remains unclear.ResultsIn this study, the content of flavones and CQAs during the development of chrysanthemum capitulum was determined by HPLC, revealing an accumulation pattern with higher levels at S1 and S2 and a gradual decrease at S3 to S5. Transcriptomic analysis revealed that CmPAL1/2, CmCHS1/2, CmFNS, CmHQT, and CmHCT were key structural genes in flavones and CQAs biosynthesis. Furthermore, weighted gene co-expression correlation network analysis (WGCNA), k-means clustering, correlation analysis and protein interaction prediction were carried out in this study to identify transcription factors (TFs) associated with flavone and CQA biosynthesis, including MYB, bHLH, AP2/ERF, and MADS-box families. The TFs CmERF/PTI6 and CmCMD77 were proposed to act as upstream regulators of CmMYB3 and CmbHLH143, while CmMYB3 and CmbHLH143 might form a complex to directly regulate the structural genes CmPAL1/2, CmCHS1/2, CmFNS, CmHQT, and CmHCT, thereby controlling flavone and CQA biosynthesis.ConclusionsOverall, these findings provide initial insights into the TF regulatory network underlying flavones and CQAs accumulation in the chrysanthemum capitulum, which laid a theoretical foundation for the quality improvement of C. morifolium ‘HangBaiJu’ and the high-quality development of the industry.

Effectiveness and efficiency: label-aware hierarchical subgraph learning for protein-protein interaction

The study of protein-protein interactions (PPIs) holds immense significance in understanding various biological activities, as well as in drug discovery and disease diagnosis. Existing deep learning methods for PPI prediction, including graph neural networks (GNNs), have been widely employed as the solutions, while they often experience a decline in performance in the real world. We claim that the topological shortcut is one of the key problems contributing negatively to the performance, according to our analysis. By modeling the PPIs as a graph with protein as nodes and interactions as edge types, the prevailing models tend to learn the pattern of nodes’ degrees rather than intrinsic sequence-structure profiles, leading to the problem termed topological shortcut. The huge data growth of PPI leads to intensive computational costs and challenges computing devices, causing infeasibility in practice. To address the discussed problems, we propose a label-aware hierarchical subgraph learning method (laruGL-PPI) that can effectively infer PPIs while being interpretable. Specifically, we introduced edge-based subgraph sampling to effectively alleviate the problems of topological shortcuts and high computing costs. Besides, the inner-outer connections of PPIs are modeled as a hierarchical graph, together with the dependencies between interaction types constructed by a label graph. Extensive experiments conducted across various scales of PPI datasets have conclusively demonstrated that the laruGL-PPI method surpasses the most advanced PPI prediction techniques currently available, particularly in the testing of unseen proteins. Also, our model can recognize crucial sites of proteins, such as surface sites for binding and active sites for catalysis.

TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.

Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty-aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnA's uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.

African Swine Fever Virus Protein-Protein Interaction Prediction.

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction.

Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.

The identification, evolutionary analysis, and immune roles of Rab family members in red swamp crayfish, Procambarus clarkii

The Rab GTPase constitutes the largest family of small GTPases that regulate intracellular trafficking. Different eukaryotes possess varying numbers of Rab paralogs. However, limited knowledge exists regarding the evolutionary pattern of Rab family in most major eukaryotic supergroups. This study cloned 24 Rab genes from transcriptome data of Procambarus clarkii haemocytes. The multiple sequence alignment and phylogenetic tree analysis revealed a relatively high degree of conservation for PcRab. Furthermore, PcRab exhibited similarities in motif composition with all members showing presence of G, PM, RabF, and RabSF motifs. The tertiary structure indicated that PcRab proteins mainly consisted of α-helices and β-strands, and most PcRab proteins shared similar tertiary structures, and it was indicated that they have similar protein characteristics. Protein-protein interaction prediction identified a total of 20 interacting proteins involved in vesicle trafficking, phagocytosis, and signal transduction with 193 interactions. Expression analysis showed wide expression patterns for PcRab in P. clarkii organs. Upon infection by white spot syndrome virus and Aeromonas veronii, significant induction was observed for PcRab gene expression levels, indicating their involvement in pathogen response mechanisms. The present study represents the pioneering effort in comprehensively identifying and cloning the Rab family genes in crustacean, followed by a systematic investigation into their evolutionary patterns and immune response upon pathogen infection. The results provided valuable insights for further investigation into the molecular mechanism underlying the response of P. clarkii to pathogen infection.

LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions.

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer, RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower-quality display significant structural biases at the level of tertiary motifs (TERMs) towards having fewer structural matches in non-antibody containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that scarcity of interfacial geometry data in the structural database may currently limit application of machine learning to the prediction of antibody-antigen interactions.

Democratizing protein language models with parameter-efficient fine-tuning

Proteomics has been revolutionized by large protein language models (PLMs), which learn unsupervised representations from large corpora of sequences. These models are typically fine-tuned in a supervised setting to adapt the model to specific downstream tasks. However, the computational and memory footprint of fine-tuning (FT) large PLMs presents a barrier for many research groups with limited computational resources. Natural language processing has seen a similar explosion in the size of models, where these challenges have been addressed by methods for parameter-efficient fine-tuning (PEFT). In this work, we introduce this paradigm to proteomics through leveraging the parameter-efficient method LoRA and training new models for two important tasks: predicting protein-protein interactions (PPIs) and predicting the symmetry of homooligomer quaternary structures. We show that these approaches are competitive with traditional FT while requiring reduced memory and substantially fewer parameters. We additionally show that for the PPI prediction task, training only the classification head also remains competitive with full FT, using five orders of magnitude fewer parameters, and that each of these methods outperform state-of-the-art PPI prediction methods with substantially reduced compute. We further perform a comprehensive evaluation of the hyperparameter space, demonstrate that PEFT of PLMs is robust to variations in these hyperparameters, and elucidate where best practices for PEFT in proteomics differ from those in natural language processing. All our model adaptation and evaluation code is available open-source at https://github.com/microsoft/peft_proteomics. Thus, we provide a blueprint to democratize the power of PLM adaptation to groups with limited computational resources.

DSSGNN-PPI: A Protein–Protein Interactions prediction model based on Double Structure and Sequence graph neural networks

The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein–Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel prediction model for PPIs named DSSGNN-PPI (Double Structure and Sequence GNN for PPIs) is designed. Initially, a distance graph between amino acid residues is constructed. Subsequently, the distance graph is fed into an underlying graph attention network module. This enables us to efficiently learn vector representations that encode the three-dimensional structure of proteins and simultaneously aggregate key local patterns and overall topological information to obtain graph embedding that adequately represent local and global structural features. In addition, the embedding representations that reflect sequence properties are obtained. Two features are fused to construct high-level protein complex networks, which are fed into the designed gated graph attention network to extract complex topological patterns. By combining heterogeneous multi-source information from downstream structure graph and upstream sequence models, the understanding of PPIs is comprehensively enhanced. A series of evaluation results validate the remarkable effectiveness of DSSGNN-PPI framework in enhancing the prediction of multi-type interactions among proteins. The multilevel representation learning and information fusion strategies provide a new effective solution paradigm for structural biology problems. The source code for DSSGNN-PPI has been hosted on GitHub and is available at https://github.com/cstudy1/DSSGNN-PPI.

Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.

Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein-disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding. The accuracies of the proposed model using five-fold cross-validation on Yeast core and Human datasets are 96.34% and 99.30%, respectively. In the task of predicting interactions in important PPI networks, our model correctly predicted all interactions in one-core, Wnt-related, and cancer-specific networks. The experimental results on cross-species datasets, including Caenorhabditis elegans, Helicobacter pylori, Homo sapiens, Mus musculus, and Escherichia coli, also show that our feature fusion method helps increase the generalization capability of the PPI prediction model.

Cucumber PGIP2 is involved in resistance to gray mold disease

Polygalacturonase inhibitor protein (PGIP) restricts fungal growth and colonization and functions in plant immunity. Gray mold in cucumber is a common fungal disease caused by Botrytis cinerea, and is widespread and difficult to control in cucumber (Cucumis sativus L.) production. In this study, Cucumis sativus polygalacturonase-inhibiting protein 2 (CsPGIP2) was found to be upregulated in response to gray mold in cucumber. CsPGIP2 was detected in the endoplasmic reticulum, cell membrane, and cell wall after transient transformation of protoplasts and tobacco. A possible interaction between Botrytis cinerea polygalacturonase 3 (BcPG3) and CsPGIP2 was supported by protein interaction prediction and BiFC analysis. Transgenic Arabidopsis plants expressing CsPGIP2 were constructed and exhibited smaller areas of gray mold infection compared to wild type (WT) plants after simultaneous inoculation. Evans blue dye (EBD) confirmed greater damage for WT plants, with more intense dyeing than for the transgenic Arabidopsis. Interestingly, compared to WT, transgenic Arabidopsis exhibited higher superoxide dismutase (AtSOD1) expression, antioxidant enzyme activities, lignin content, net photosynthetic rate (Pn), and photochemical activity. Our results suggest that CsPGIP2 stimulates a variety of plant defense mechanisms to enhance transgenic Arabidopsis resistance against gray mold disease.

Genome-wide identification, classification and expression analysis of the heat shock transcription factor family in Garlic (Allium sativum L.)

BackgroundThe heat shock transcription factor (HSF) plays a crucial role in the regulatory network by coordinating responses to heat stress as well as other stress signaling pathways. Despite extensive studies on HSF functions in various plant species, our understanding of this gene family in garlic, an important crop with nutritional and medicinal value, remains limited. In this study, we conducted a comprehensive investigation of the entire garlic genome to elucidate the characteristics of the AsHSF gene family.ResultsIn this study, we identified a total of 17 AsHSF transcription factors. Phylogenetic analysis classified these transcription factors into three subfamilies: Class A (9 members), Class B (6 members), and Class C (2 members). Each subfamily was characterized by shared gene structures and conserved motifs. The evolutionary features of the AsHSF genes were investigated through a comprehensive analysis of chromosome location, conserved protein motifs, and gene duplication events. These findings suggested that the evolution of AsHSF genes is likely driven by both tandem and segmental duplication events. Moreover, the nucleotide diversity of the AsHSF genes decreased by only 0.0002% from wild garlic to local garlic, indicating a slight genetic bottleneck experienced by this gene family during domestication. Furthermore, the analysis of cis-acting elements in the promoters of AsHSF genes indicated their crucial roles in plant growth, development, and stress responses. qRT-PCR analysis, co-expression analysis, and protein interaction prediction collectively highlighted the significance of Asa6G04911. Subsequent experimental investigations using yeast two-hybridization and yeast induction experiments confirmed its interaction with HSP70/90, reinforcing its significance in heat stress.ConclusionsThis study is the first to unravel and analyze the AsHSF genes in garlic, thereby opening up new avenues for understanding their functions. The insights gained from this research provide a valuable resource for future investigations, particularly in the functional analysis of AsHSF genes.

Co-training based prediction of multi-label protein–protein interactions

Prediction of protein–protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein–Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Molecular evolution of Phytocyanin gene and analysis of expression at different coloring periods in apple (Malus domestica)

BackgroundPC (phytocyanin) is a class of copper-containing electron transfer proteins closely related to plant photosynthesis, abiotic stress responses growth and development in plants, and regulation of the expression of some flavonoids and phenylpropanoids, etc., however, compared with other plants, the PC gene family has not been systematically characterized in apple.ResultsA total of 59 MdPC gene members unevenly distributed across 12 chromosomes were identified at the genome-wide level. The proteins of the MdPC family were classified into four subfamilies based on differences in copper binding sites and glycosylation sites: Apple Early nodulin-like proteins (MdENODLs), Apple Uclacyanin-like proteins (MdUCLs), Apple Stellacyanin-like proteins (MdSCLs), and Apple Plantacyanin-like proteins (MdPLCLs). Some MdPC members with similar gene structures and conserved motifs belong to the same group or subfamily. The internal collinearity analysis revealed 14 collinearity gene pairs among members of the apple MdPC gene. Interspecific collinearity analysis showed that apple had 31 and 35 homologous gene pairs with strawberry and grape, respectively. Selection pressure analysis indicated that the MdPC gene was under purifying selection. Prediction of protein interactions showed that MdPC family members interacted strongly with the Nad3 protein. GO annotation results indicated that the MdPC gene also regulated the biosynthesis of phenylpropanoids. Chip data analysis showed that (MdSCL3, MdSCL7 and MdENODL27) were highly expressed in mature fruits and peels. Many cis-regulatory elements related to light response, phytohormones, abiotic stresses and flavonoid biosynthetic genes regulation were identified 2000 bp upstream of the promoter of the MdPC gene, and qRT-PCR results showed that gene members in Group IV (MdSCL1/3, MdENODL27) were up-regulated at all five stages of apple coloring, but the highest expression was observed at the DAF13 (day after fruit bag removal) stage. The gene members in Group II (MdUCL9, MdPLCL3) showed down-regulated or lower expression in the first four stages of apple coloring but up-regulated and highest expression in the DAF 21 stage.ConclusionHerein, one objective of these findings is to provide valuable information for understanding the structure, molecular evolution, and expression pattern of the MdPC gene, another major objective in this study was designed to lay the groundwork for further research on the molecular mechanism of PC gene regulation of apple fruit coloration.

Cross-domain contrastive graph neural network for lncRNA–protein interaction prediction

Identifying lncRNA–protein interactions (LPIs) is an important biomedical task, facilitating the comprehension of the biological functions and mechanisms of lncRNAs. Many computational methods have been developed for this task, especially graph neural network (GNN)-based methods have attracted increasing attention. Typically, the LPI network involves two types of interaction domains: the interactive domain capturing the direct interaction information between lncRNAs and proteins, and the collaborative domain reflecting the collaboration information among lncRNAs or proteins. However, existing GNN-based methods only leverage one of them to obtain topological information, which cannot fully characterize lncRNAs and proteins, resulting in suboptimal node representations. Moreover, each domain contains task-irrelevant redundant information, posing a challenge in effectively integrating information from different domains. To address these issues, we propose a novel Cross-domain Contrastive Graph Neural Network (CCGNN) for predicting potential LPIs. CCGNN employs a multi-domain encoder that consists of an interactive domain encoder and two collaborative domain encoders to capture valuable information from each interaction domain. Subsequently, domain-adaptive fusion is designed to integrate information from different domains to acquire comprehensive node representations. Furthermore, cross-domain contrastive learning is devised to enrich the node representations, drawing inspiration from the information bottleneck principle by retaining as much task-relevant information as possible within each domain and minimizing mutual information between representations across different domains. Extensive experiments on four real-world datasets demonstrate the superiority of CCGNN over state-of-the-art methods, and a further case study and generalization analysis illustrate the effectiveness of CCGNN in the biomedical link prediction tasks.

In silico analysis of the wheat BBX gene family and identification of candidate genes for seed dormancy and germination

BackgroundB-box (BBX) proteins are a type of zinc finger proteins containing one or two B-box domains. They play important roles in development and diverse stress responses of plants, yet their roles in wheat remain unclear.ResultsIn this study, 96 BBX genes were identified in the wheat genome and classified into five subfamilies. Subcellular localization prediction results showed that 68 TaBBXs were localized in the nucleus. Protein interaction prediction analysis indicated that interaction was one way that these proteins exerted their functions. Promoter analysis indicated that TaBBXs may play important roles in light signal, hormone, and stress responses. qRT-PCR analysis revealed that 14 TaBBXs were highly expressed in seeds compared with other tissues. These were probably involved in seed dormancy and germination, and their expression patterns were investigated during dormancy acquisition and release in the seeds of wheat varieties Jing 411 and Hongmangchun 21, showing significant differences in seed dormancy and germination phenotypes. Subcellular localization analysis confirmed that the three candidates TaBBX2-2 A, TaBBX4-2 A, and TaBBX11-2D were nuclear proteins. Transcriptional self-activation experiments further demonstrated that TaBBX4-2A was transcriptionally active, but TaBBX2-2A and TaBBX11-2D were not. Protein interaction analysis revealed that TaBBX2-2A, TaBBX4-2A, and TaBBX11-2D had no interaction with each other, while TaBBX2-2A and TaBBX11-2D interacted with each other, indicating that TaBBX4-2A may regulate seed dormancy and germination by transcriptional regulation, and TaBBX2-2A and TaBBX11-2D may regulate seed dormancy and germination by forming a homologous complex.ConclusionsIn this study, the wheat BBX gene family was identified and characterized at the genomic level by bioinformatics analysis. These observations provide a theoretical basis for future studies on the functions of BBXs in wheat and other species.

Strictosamide promotes wound healing through activation of the PI3K/AKT pathway

Nauclea officinalis, as a Chinese medicine in Hainan province, had the effect of treating lower limb ulcers, burn infections. In this paper, we studied the effect of Strictosamide (STR), the main bioactive compound in Nauclea officinals, on wound healing and explored its internal mechanism. Firstly, the wound healing potential of STR was evaluated in a rat model, demonstrating its ability to expedite wound healing, mitigate inflammatory infiltration, and enhance collagen deposition. Additionally, immunofluorescence analysis revealed that STR up-regulated the expression of CD31 and PCNA. Subsequently, target prediction, protein-protein interaction (PPI), gene ontology (GO), and pathway enrichment analyses were used to obtain potential targets, specific biological processes, and molecular mechanisms of STR for the potential treatment of wound healing. Furthermore, molecular docking was conducted to predict the binding affinity between STR and its associated targets. Additionally, in vivo and in vitro experiments confirmed that STR could increase the expression of P-PI3K, P-AKT and P-mTOR by activating the PI3K/AKT signaling pathway. In summary, this study provided a new explanation for the mechanism by which STR promotes wound healing through network pharmacology, suggesting that STR may be a new candidate for treating wound.