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.

Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention.

The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.

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.

Genome-wide identification, expression analysis of WRKY transcription factors in Citrus ichangensis and functional validation of CiWRKY31 in response to cold stress

BackgroundIchang papeda (Citrus ichangensis), a wild perennial plant of the Rutaceae family, is a cold-hardy plant. WRKY transcription factors are crucial regulators of plant growth and development as well as abiotic stress responses. However, the WRKY genes in C. ichangensis (CiWRKY) and their expression patterns under cold stress have not been thoroughly investigated, hindering our understanding of their role in cold tolerance.ResultsIn this study, a total of 52 CiWRKY genes identified in the genome of C. ichangensis were classified into three main groups and five subgroups based on phylogenetic analysis. Comprehensive analyses of motif features, conserved domains, and gene structures were performed. Segmental duplication plays a significant role in the CiWRKY gene family expansion. Cis-acting element analysis revealed the presence of various stress-responsive elements in the promoters of the majority of CiWRKYs. Gene ontology (GO) analysis and protein-protein interaction predictions indicate that the CiWRKYs exhibit crucial roles in regulation of both development and stress response. Expression profiling analysis demonstrates that 14 CiWRKYs were substantially induced under cold stress. Virus-induced gene silencing (VIGS) assay confirmed that CiWRKY31, one of the cold-induced WRKYs, functions positively in regulation of cold tolerance.ConclusionSequence and protein properties of CiWRKYs were systematically analyzed. Among the 52 CiWRKY genes 14 members exhibited cold-responsive expression patterns, and CiWRKY31 was verified to be a positive regulator of cold tolerance. These findings pave way for future investigations to understand the molecular functions of CiWRKYs in cold tolerance and contribute to unravelling WRKYs that may be used for engineering cold tolerance in citrus.

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.

Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm.

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.

CSPG4P12 polymorphism served as a susceptibility marker for esophageal cancer in Chinese population

BackgroundChondroitin sulfate proteoglycan 4 pseudogene 12 (CSPG4P12) has been implicated in the pathogenesis of various cancers. This study aimed to evaluate the association of the CSPG4P12 polymorphism with esophageal squamous cell carcinoma (ESCA) risk and to explore the biological impact of CSPG4P12 expression on ESCA cell behavior.MethodsA case-control study was conducted involving 480 ESCA patients and 480 healthy controls to assess the association between the rs8040855 polymorphism and ESCA risk. The CSPG4P12 rs8040855 genotype was identified using the TaqMan-MGB probe method. Logistic regression model was used to evaluate the association of CSPG4P12 SNP with the risk of ESCA by calculating the odds ratios (OR) and 95% confidence intervals (95%CI ). The effects of CSPG4P12 overexpression on cell proliferation, migration, and invasion were examined in ESCA cell lines. Co-expressed genes were identified via the CBioportal database, with pathway enrichment analyzed using SangerBox. The binding score of CSPG4P12 to P53 was calculated using RNA protein interaction prediction (RPISeq). Additionally, Western Blot analysis was performed to investigate the impact of CSPG4P12 overexpression on the P53/PI3K/AKT signaling pathway.ResultsThe presence of at least one rs8040855 G allele was associated with a reduced susceptibility to ESCA compared to the CC genotype (OR = 0.51, 95%CI = 0.28–0.93, P = 0.03). Stratification analysis revealed that the CSPG4P12 rs8040855 C allele significantly decreased the risk of ESCA among younger individuals (≤ 57 years) and non-drinkers (OR = 0.31, 95%CI = 0.12–0.77, P = 0.01; OR = 0.42, 95%CI=0.20–0.87, P = 0.02, respectively). CSPG4P12 expression was found to be downregulated in ESCA tissues compared to adjacent normal tissues. Overexpression of CSPG4P12 in ESCA cells inhibited their proliferation, migration, and invasion capabilities. Furthermore, Western Blot analysis indicated that CSPG4P12 overexpression led to a reduction in PI3K and p-AKT protein expression levels. P53 silencing rescues the inhibitory effect of CSPG4P12 on p-AKT.ConclusionThe CSPG4P12 rs8040855 variant is associated with reduced ESCA risk and the overexpression of CSPG4P12 inhibited the migration and invasion of ESCA cells by P53/PI3K/AKT pathway. These findings suggest that CSPG4P12 may serve as a novel biomarker for ESCA susceptibility and a potential target for therapeutic intervention.

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA’s BioNeMo platform, as well as homology modeling using Innophore’s CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.

An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction.

In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.

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.

Protein-Protein Interfaces: A Graph Neural Network Approach.

Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computational deep learning approaches represent an affordable and efficient solution to tackle these problems. Since protein structure can be summarized as a graph, graph neural networks (GNNs) represent the ideal deep learning architecture for the task. In this work, PPI prediction is modeled as a node-focused binary classification task using a GNN to determine whether a generic residue is part of the interface. Biological data were obtained from the Protein Data Bank in Europe (PDBe), leveraging the Protein Interfaces, Surfaces, and Assemblies (PISA) service. To gain a deeper understanding of how proteins interact, the data obtained from PISA were assembled into three datasets: Whole, Interface, and Chain, consisting of data on the whole protein, couples of interacting chains, and single chains, respectively. These three datasets correspond to three different nuances of the problem: identifying interfaces between protein complexes, between chains of the same protein, and interface regions in general. The results indicate that GNNs are capable of solving each of the three tasks with very good performance levels.

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.