Proteomic Technologies Research Articles

Quantitative Analysis of Deer Bone Hydroethanolic Extract Using Label-Free Proteomics: Investigating Its Safety and Promoting Effect on Mouse Embryonic Osteoblastic Progenitor Cell Proliferation

Background: Deer bone is rich in proteins and free amino acids, offering high nutritional value and benefits such as strengthening bones and antioxidant properties. However, the development and utilization of deer bone resources are limited, and the safety evaluation of health foods is incomplete. Methods: We established a hydrogen ethanol extraction method for deer bone and analyzed the components of the deer bone hydroethanolic extract (DBHE) using liquid chromatography–tandem mass spectrometry (LC-MS/MS), gas chromatography–mass spectrometry (GC-MS), and inductively coupled plasma mass spectrometry (ICP-MS). Results: Using Label-free proteomics technology, we identified 69 proteins and 181 peptides. We also quantified 16 amino acids, 22 fatty acids, and 17 inorganic elements. Finally, we evaluated the safety of DBHE both in vitro and in vivo. The results indicated that DBHE did not exhibit any toxic effects at the doses we tested and can promote the proliferation of mouse embryonic osteoblastic progenitor cells (MC3T3-E1), demonstrating potential efficacy against osteoporosis and arthritis. Conclusions: This study provides a theoretical basis for the quality control, processing, and resource development of deer bone.

Analysis of GCRV Pathogenesis and Therapeutic Measures Through Proteomic and Metabolomic Investigations in GCRV-Infected Tissues of Grass Carp (Ctenopharyngodon idella)

Hemorrhagic disease caused by grass carp reovirus (GCRV) infection is a major problem affecting the grass carp aquaculture industry. Therefore, inhibiting the spread of GCRV infection is of great economic significance. Herein, we sequenced five tissues (gill, liver, intestine, kidney, and muscle) from grass carp before and after GCRV infection using data-independent acquisition proteomic and untargeted metabolomic technologies, and quantitatively identified 10,808 proteins and 4040 metabolites. Then, we analyzed the differentially expressed proteins (DEPs) and metabolites (DEMs) before and after GCRV infection in the five tissues. Gene ontology analysis revealed that the five tissue DEPs were enriched in metabolic, including carbohydrate and lipid metabolic processes. Chemical taxonomy analysis showed that the categories of DEMs mainly included carbohydrates and lipids, such as fatty acids, glycerophospholipids, steroids, and their derivatives. Both the proteomic and the metabolomic data showed that GCRV affected the carbohydrate and lipid metabolism in the host. Shared pathway analysis was performed at both the protein and metabolic levels, showing significant enrichment of the glycolysis and pentose phosphate pathways (p < 0.001). Further analysis of glycolysis and pentose phosphate pathway inhibitors revealed that these two pathways are important for GCRV replication. As the kidney was the most affected among the five tissues, we analyzed the butanoate metabolism in the kidney, which revealed that most of the differentially expressed proteins and differently expressed metabolites in the butanoate metabolism were related to the TCA cycle. Further investigation showed that fumaric acid, an intermediate product in the TCA cycle, significantly inhibited GCRV replication in the CIK cells (p < 0.001), and that this inhibitory effect may be related to its induction of interferon system activation. The addition of fumaric acid to feed increased the survival rate of juvenile grass carp by 19.60% during GCRV infection, and protected the tissues of those infected with GCRV, making it a potential anti-GCRV feed additive. Our results provide new perspectives on GCRV pathogenesis and antiviral strategies for grass carp.

The next frontier in multiple sclerosis therapies: Current advances and evolving targets.

Recent advancements in research have significantly enhanced our comprehension of the intricate immune components that contribute to multiple sclerosis (MS) pathogenesis. By conducting an in-depth analysis of complex molecular interactions involved in the immunological cascade of the disease, researchers have successfully identified novel therapeutic targets, leading to the development of innovative therapies. Leveraging pioneering technologies in proteomics, genomics, and the assessment of environmental factors has expedited our understanding of the vulnerability and impact of these factors on the progression of MS. Furthermore, these advances have facilitated the detection of significant biomarkers for evaluating disease activity. By integrating these findings, researchers can design novel molecules to identify new targets, paving the way for improved treatments and enhanced patient care. Our review presents recent discoveries regarding the pathogenesis of MS, highlights their genetic implications, and proposes an insightful approach for engaging with newer therapeutic targets in effectively managing this debilitating condition.

Unravelling the Significance of Seed Proteomics: Insights into Seed Development, Function, and Agricultural Applications.

Seeds are essential for plant reproduction, ensuring species survival and dispersal while adapting to diverse environments throughout a plant's life. Proteomics has emerged as a powerful tool for deciphering the complexities of seed growth, germination, and stress responses. Advanced proteomic technologies enable the analysis of protein changes during germination, dormancy, and ageing, enhancing our understanding of seed lifespan and vitality. Recent studies have revealed detailed insights into metabolic processes and storage protein profiles across various plant species. This knowledge is crucial for improving seed storage, conserving quality, and maintaining viability. Additionally, it contributes to sustainable agriculture by identifying stress-responsive proteins and signalling pathways that can mitigate stress and enhance farming practices. This review highlights significant advancements in seed proteomics over the past decade, discussing critical discoveries related to storage proteins, protein interactions, and proteome modifications due to stress. It illustrates how these insights transform seed biology, boosting productivity, food security, and environmentally friendly practices. The review also identifies existing knowledge gaps and provides direction for future research, underscoring the need for continued interdisciplinary collaboration in this dynamic field.

Pathogenesis of thoracic ossification of the ligamentum flavum

Thoracic ossification of the ligamentum flavum (TOLF) is characterized by ectopic ossification of the ligamentum flavum in the thoracic spine and is considered the main cause of thoracic spinal stenosis and spinal cord disease. Osteoblast specific transcription factor Osterix (Osx) is required for bone formation, and there is no bone formation or ossification without Osx. Surgical intervention is recognized as the only effective method for TOLF treatment with set of complications. However, underlying mechanisms of TOLF are not well understood. This paper summarizes the pathogenesis of TOLF. Some relevant factors have been discussed, such as mechanical stress, genetic susceptibility genes, endocrine and trace element metabolism abnormalities, which may associate with TOLF. More recent studies using proteomics technology and RNA sequencing approach have discovered that some new factors participate in TOLF by upregulation of Osx gene expression including inflammatory factors. TOLF is a unique disease involving multiple factors. On the other hand, studies on TOLF pathogenic mechanism may provide new ideas for finding possible upstream regulatory factors of Osx and further developing novel drugs to stimulate new bone formation to treat osteoporosis.

Unveiling Aquatic Organism Health Through eProteins: A Contemporary Perspective

Environmental DNA (eDNA) analysis has transformed our understanding of aquatic ecosystems, but traditional methods often lack insights into organismal health. The emerging field of environmental protein (eProtein) analysis offers a novel approach to monitoring the physiological status of aquatic organisms. Recent advancements in proteomic technologies have enabled the detection and characterization of stress-responsive proteins in water samples, providing valuable insights into organismal health and environmental stressors. This perspective paper explores the potential of eProtein analysis for monitoring aquatic organism health, disease dynamics and reproductive cycles. Methodological advancements in protein extraction and mass spectrometry have enhanced the sensitivity and specificity of eProtein analysis, facilitating comprehensive molecular profiling and biomarker identification. By integrating eProtein analysis into aquaculture management and environmental monitoring, researchers can proactively manage fish health, mitigate disease outbreaks and safeguard aquatic ecosystems. Future research directions include refining extraction methods, establishing standardized protocols and leveraging interdisciplinary collaborations to maximize the potential of eProtein analysis for aquatic research and conservation.

The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends.

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods

Background and aimsAutism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention. Materials and MethodsSequential window acquisition of all theoretical spectra-mass spectrometry (SWATH-MS)-based proteomics technology and 30 DSM-V defined ASD cases versus age- and sex-matched controls were initially evaluated, and candidate biomarkers were screened using machine learning methods. Candidate biomarkers were validated by targeted proteomics multiple reaction monitoring (MRM) analysis using an independent group of 30 ASD cases vs. controls. ResultsFifty-one differentially expressed proteins (DEPs) were identified by SWATH analysis. They were associated with the immune response, complements and coagulation cascade pathways, and apolipoprotein-related metabolic pathways. Machine learning analysis screened 10 proteins as biomarker combinations (TFRC, PPBP, APCS, ALDH1A1, CD5L, SPARC, FGG, SHBG, S100A9, and PF4V1). In the MRM analysis, four proteins (PPBP, APCS, FGG, and PF4V1) were significantly different between the groups, and their combination as a screening indicator showed high potential (AUC = 0.8087, 95 % confidence interval 0.6904–0.9252, p < 0.0001). ConclusionsOur study provides data that suggests that a few plasma proteins have potential use in screening for ASD.

Detection of >400 Cluster of Differentiation Biomarkers and Pathway Proteins in Single Immune Cells by Cyclic Multiplex In Situ Tagging for Single-Cell Proteomic Studies.

The identification and characterization of immune cell subpopulations are critical to reveal cell development throughout life and immune responses to environmental factors. Next-generation sequencing technologies have dramatically advanced single-cell genomics and transcriptomics for immune cell classification. However, gene expression is often not correlated with protein expression, and immunotyping is mostly accepted in protein format. Current single-cell proteomic technologies are either limited in multiplex capacity or not sensitive enough to detect the critical functional proteins. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology to simultaneously measure >400 proteins, a scale of >10 times than similar technologies. Such an ultrahigh multiplexity is achieved by reiterative staining of the single cells coupled with a MIST array for detection. This technology has been thoroughly validated through comparison with flow cytometry and fluorescence immunostaining techniques. Both peripheral blood mononuclear cells (PBMCs) and T cells are analyzed by the CycMIST technology, and almost the entire spectrum of cluster of differentiation (CD) surface markers has been measured. The landscape of fluctuation of CD protein expression in single cells has been uncovered by our technology. Further study found T cell activation signatures and protein-protein networks. This study represents the highest multiplexity of single immune cell marker measurement targeting functional proteins. With additional information from intracellular proteins of the same single cells, our technology can potentially facilitate mechanistic studies of immune responses under various disease conditions.

Screening and expression validation of key proteins for secondary hair follicle growth in cashmere goats based on iTRAQ quantitative proteomics technology.

The growth of secondary hair follicles (SHFs) in cashmere goats has periodic changes, including telogen, anagen, and catagen, during which proteins play important roles as the executor of life activities. In this study, the skin tissues of cashmere goats at three different growth stages of SHFs were collected for proteome sequencing and validation experiments. Through protein differential expression analysis and time series analysis, FKBP prolyl isomerase 10 (FKBP10) and fibrillin 2 (FBN2) were screened as the key proteins for SHF cycle growth of cashmere goats, and albumin (ALB), collagen type I alpha 1 chain (COL1A1) and elastin (ELN) were predicted to be their interacting proteins. The results of quantitative real-time PCR (qRT-PCR), western blot, and immunohistochemistry experiments showed that the mRNA and protein expression levels of FKBP10, FBN2, COL1A1, ELN and ALB were higher in anagen and lower in telogen. They were all highly expressed in the outer root sheath of SHFs in anagen. FKBP10, FBN2, COL1A1, ELN, and ALB can promote the growth of SHFs in cashmere goats. This study lays the foundation for analyzing the growth cycle regulatory mechanism of SHFs in cashmere goats, and provides new ideas for further improving cashmere yield and quality.

From Proteomics to Diagnosis: Biomarker Discovery in Tuberculosis Research.

Tuberculosis (TB) is a leading cause of death from a single infectious disease worldwide. Early and accurate diagnosis is advantageous for timely detection and prompt treatment, thereby reducing the risk of disease transmission, which is essential for effective TB control. Biomarkers provide a valuable resource for TB diagnosis. Proteomic technologies have emerged as a powerful tool in biomarker discovery. In this perspective, we explore how proteomic technologies contribute to the discovery of TB diagnostic biomarkers. We also address the challenges and discuss prospective methods to augment the performance of biomarkers in diagnosing TB.

Proteomic analysis and experimental validation reveal the blood–brain barrier protective of Huanshaodan in the treatment of SAMP8 mouse model of Alzheimer’s disease

BackgroundHuanshaodan (HSD) is a Chinese Herbal Compound which has a definite clinical effect on Alzheimer’s disease (AD), however, the underlying mechanism remains unclear. The aim of this study is to preliminarily reveal the mechanism of HSD in the treatment of AD model of SAMP8 mice.MethodsChemical composition of HSD and its drug-containing serum were identified by Q-Orbitrap high resolution liquid mass spectrometry. Six-month-old SAMP8 mice were treated with HSD and Donepezil hydrochloride by gavage for 2 months, and Wogonin for 28 days. Behavioral test was performed to test the learning and memory ability of mice. Immunofluorescence (IF) or Western-blot methods were used to detect the levels of pSer404-tau and β-amyloid (Aβ) in the brain of mice. Hematoxylin–eosin (H&E) staining and Transmission electron microscopy (TEM) assay was applied to observe the pathological changes of neurons. Proteomic technology was carried out to analyze and identify the protein network of HSD interventions in AD. Then the pathological process of the revealed AD-related differential proteins was investigated by IF, Q-PCR, Western-blot, Fluorescence in situ hybridization (FISH) and 16S rRNA sequencing methods.ResultsThe results showed that HSD and Wogonin, one of the components in its drug-containing serum, can effectively improve the cognitive impairments of SAMP8 mice, protect hippocampal neurons and synapses, and reduce the expression of pSer404-tau and Aβ. HSD and Wogonin reduced the levels of fibrinogen β chain (FGB) and γ chain (FGG), the potential therapeutic targets revealed by proteomics analysis, reduced the colocalization of FGB and FGG with Aβ, ionized calcium binding adaptor molecule 1 (Iba-1), glial fibrillary acidic protein (GFAP), increased level of and myelin basic protein (MBP). Meanwhile, HSD and Wogonin increased ZO-1 and Occludin levels, improved brain microvascular injury, and reduced levels of bacteria/bacterial DNA and lipopolysaccharide (LPS) in the brain of mice. In addition, 16S rRNA sequencing indicated that HSD regulated the structure of intestinal microbiota of mice.ConclusionThe effects of HSD on AD may be achieved by inhibiting the levels of fibrinogen and the interactions on glia cells in the brain, and by modulating the structure of intestinal microbiota and improving the blood–brain barrier function.

Quantitative proteomics reveal the protective effects of Qiang Jing decoction against oligoasthenospermia via modulating spermatogenesis related-proteins.

According to the current situation, the treatment of oligoasthenospermia with Western medicine is still full of challenges. Qiang Jing decoction (QJD) is used for treating male infertility as a traditional Chinese medicine (TCM) prescription. In this study. we will try to uncover the molecular mechanism of QJDs in treating oligoasthenospermia based on the proteomics technology. In this study, sperm quality from oligoasthenospermia model, QJD treatment, and control rats were analyzed. Tandem mass tag (TMT) quantitative proteomics was used for the comparative analysis of protein abundance in rat testis of different treatment. The therapeutic effect of QJDs on spermatogenesis function by improving sperm quality was confirmed in the present experiment. In addition, our results showed that the majority of proteins related to spermatogenesis were significantly downregulated in oligoasthenospermia model rats compared to the control group, whereas these proteins' expression levels were recovered after the QJD treatment. In the meantime, immunohistochemistry was used to determine PI3K/AKT (phosphoinositide 3 kinase/serine-threonine kinase) signaling pathway-related protein expression profiles. In brief, we think QJD improved sperm density and quality by activating spermatogenesis-related protein expression and PI3K/AKT signaling pathway. The results of this study may help to establish a foundation for further functional studies of key protein-mediated for improving sperm quality and alleviating oligoasthenospermia.

Pancreatic cancer is feeling the heat.

Pancreatic adenocarcinoma (PDAC) is considered an immunologically 'cold' tumor that fails to attract or support effector T cells. Most PDACs are resistant to immune checkpoint blockade due to the complex signaling pathways that exist within its tumor microenvironment. Recent advances in genomic and proteomic technology advances are finally uncovering the complex inflammatory cellular and intercellular signals that require modulation and reprogramming. The goal is to 'turn up the heat' on PDACs with combination immunotherapies that incorporate T cell activating agents and immune modulatory agents, and successfully eradicate tumors. Here, we discuss progress and promising new research that is moving the field toward this goal.

Data-Independent Acquisition Proteome Technology for Analysis of Antifungal and Anti-aflatoxigenic Properties of Eugenol to Aspergillus flavus.

Several toxicogenic Aspergilli, such as Aspergillus flavus and A. parasiticus, could biosynthesize aflatoxin B1 (AFB1) and other mycotoxins. Chemical fungicides are commonly used to control fungal contamination, but chemical residues may pose significant risks to human health and environmental stability. Consequently, natural antifungal and aflatoxin-inhibiting agents could be sustainable alternatives. Eugenol has been used as an inhibitor of aflatoxins (AFs), which is a common essential oil. Nevertheless, the definite mechanism by which eugenol exerts its inhibitory effect on Aspergillus remains unclear. This research demonstrates that eugenol significantly suppressed fungi growth and AF production as the dose increased (40.9 to 100%). With the proteomics approach, the inhibition pathway of eugenol was investigated. The production of proteins involved in cell wall integrity was notably reduced under eugenol treatment, indicating that eugenol destroys the cell wall integrity of A. flavus. Furthermore, exposure to eugenol downregulated several fungal developmental regulators and subsequently inhibited A. flavus development. Energy metabolism in A. flavus is closely related to its secondary metabolism. Under eugenol treatment, the synthesis of proteins relevant to the pentose phosphate pathway was significantly enhanced, leading to a decrease in the availability of acetyl-CoA, a precursor for AF biosynthesis. Simultaneously, the valine, leucine, and isoleucine biosynthesis pathways were enhanced, further reducing the content of acetyl-CoA. This might be the primary factor in the inhibition of AF biosynthesis by eugenol. Ribosome biogenesis was the most dysregulated pathway based on KEGG data, indicating that eugenol disturbed ribosome biogenesis and affected its normal function in A. flavus. In conclusion, eugenol inhibits the cellular integrity, energy metabolism, and protein synthesis and then suppresses A. flavus development and AF biosynthesis, which provides a clearer grasp of the inhibitory mechanism meaningful for A. flavus and AF contamination control.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery.

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors. Unfortunately, triple-negative BCa and metastases to distant organs are largely untreatable with current medical interventions. Recent advances in sequencing and proteomic technologies have improved our understanding of the molecular changes that occur during breast cancer initiation and progression. In this era of precision medicine, researchers and clinicians aim to identify subclass-specific BCa biomarkers and develop new targets and drugs to guide treatment. Although vast amounts of omics data including single cell sequencing data, can be accessed through public repositories, there is a lack of user-friendly platforms that integrate information from multiple studies. Thus, to meet the need for a simple yet effective and integrative BCa tool for multi-omics data analysis and visualization, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB was developed to provide a unique resource for hypothesis generation and testing, as well as for the discovery of biomarkers and therapeutic targets. The platform also provides pre- and post-treatment data, which can help users identify treatment resistance markers and patient groups that may benefit from combination therapy.

From Proteomics to the Analysis of Single Protein Molecules.

Limit of detection (LoD) is a term that is used to characterize the sensitivity of an analytical method. The existing limitation of the sensitivity of analysis using modern mass spectrometry methods has been experimentally shown to be a limiting factor in the application of proteomic technologies in medicine. This article proposes a concept of a new technology that will set a new vector of development in the development of systems for solving problems of medical diagnostics and deals with theoretical and practical aspects of creating a new technology for the detection of single biomacromolecules (in particular, proteins) in biological samples. Such technology should be based on the principle of signal registration similar to that used in a Geiger counter (also known as a Geiger-Müller counter or G-M counter), a device that automatically counts the number of ionizing particles that hit it. This counter is free from probabilistic components; it registers a signal if there is at least one target molecule in the analysis chamber. Predictive medical diagnostics require technology based on methods where sensitivity allows for the detection of single marker molecules in a biological sample volume of 1-10 µL, the smallest volume of biomaterial used in laboratory diagnostics. Creation of a detector with a sensitivity of 10-18 M would allow for the detection of one molecule in 1 µL of the sample, which fundamentally makes this approach analogous to a G-M counter for solutions. To date, bioanalytical methods are limited to a sensitivity of 10-12 M (which is approximately 1 million molecules per 1 μL), which is insufficient to capture the early stages of pathological processes.

Current computational tools for protein lysine acylation site prediction.

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

Research on the mechanism of Bacillus velezensis A-27 in enhancing the resistance of red kidney beans to soybean cyst nematode based on TMT proteomics analysis.

Soybean cyst nematode (SCN) poses a significant challenge to red kidney beans cultivation, resulting in yield losses and quality deterioration. This study investigates the molecular mechanisms using Tandem Mass Tag (TMT) based proteomics technology to explore how the plant growth-promoting rhizobacterium (PGPR) Bacillus velezensis A-27 enhances the resistance of red kidney beans against SCN. The results revealed that out of 1,374 differentially expressed proteins (DEPs) in the red kidney beans roots, 734 DEPs were upregulated and 640 DEPs were downregulated in the A-27 + J2 vs J2 treatment group. KEGG analysis revealed that 14 DEPs were involved in the α-LeA metabolic pathway, crucial for the biosynthesis of jasmonic acid (JA) in plants. Quantitative real-time PCR (qRT-PCR) confirmed the upregulation of 4 key genes (PLA1, AOS, AOC, ACX) in the JA biosynthesis pathway, while enzyme-linked immunosorbent assay (ELISA) demonstrated a significant increase in JA content in the roots. The study demonstrates that B. velezensis A-27 stimulates induced systemic resistance (ISR) in red kidney beans, and induce JA biosynthesis by regulating the expression of key enzymes in the α-LeA metabolic pathway. This enhances the plant's defense against SCN, providing a theoretical foundation for the potential use of B. velezensis A-27 as a biocontrol agent for managing SCN in leguminous crops.