Mass Spectrometry Data Research Articles

Feature-based molecular networking with MS2LDA to profile compounds in Lanbuzheng based on ultra-high-performance liquid chromatography-quadrupole Exactive Orbitrap high-resolution mass spectrometry.

Lanbuzheng (Geum japonicum Thunb. var. chinense Bolle), a plant found in Southwest China, is a traditional Chinese medicine that promotes hematopoiesis and antioxidant functions. Many of its chemical constituents remain unknown, posing challenges both to understanding its pharmacological mechanisms and to conducting quality control research. In this work, ultra-high performance liquid chromatography coupled with quadrupole Exactive Orbitrap high-resolution mass spectroscopy was used for profiling the composition of Lanbuzheng. Using positive ion mass spectrometry data enriched from Lanbuzheng extract, feature-based molecular networking (FBMN) was constructed and associated with Mass2Motifs substructures using MS2LDA. Prediction and validation of unknown constituents of Lanbuzheng using a custom-built compound library, SIRIUS, and network annotation propagation, achieved a semi-automated annotation of the molecular network. Based on the custom-built library comprising 206 compounds and the FBMN clustering results, the constituents in Lanbuzheng primarily include tannins, triterpenes, flavonoids, and phenolics. Using only 65 pre-identified compounds as references, 210 unknown compounds were annotated in various polarity regions of Lanbuzheng. Results of the current work indicate that molecular networks enable the efficient annotation of compounds in complex systems, laying the groundwork for the preliminary identification of pharmacologically active constituents of Lanbuzheng.

On the PhyloQuant protein expression profiles approach to the taxonomic problem.

Inferring evolutionary relationships among organisms has been a fundamental problem in evolutionary biology. The current phylogenetic molecular methods serve as the baseline model to test new evolutionary hypotheses with taxonomic purposes. Leishmaniinae trypanosomatids subfamily includes protozoan parasites of clinical importance to humans. They have an intricate taxonomic history defined by morphological elements, host range, and molecular phylogenies. Unraveling the increasing diversity of this group has shown limitations in reconstructing the true evolutionary relationships among Trypanosomatidae species. Toward the goal of inferring evolutionary relationships that help to resolve phylogenetic and taxonomic controversies among parasites of the subfamily Leishmaniinae, Mule etal. propose the method PhyloQuant as a valuable approach, based on differential protein expression obtained from high throughput mass spectrometry data. Employing a pioneering methodological approach, Mule etal. assess the taxonomic problem for species hypothesis within Leishmaniinae, from quantitative phenetic protein expression profiles, in contrast to the standard multilocus phylogenetic approaches.

Reproducible MS/MS library cleaning pipeline in matchms

Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries.Graphical

A 1,8-Naphthalimide-based Tripodal Fluorescent Chemosensor to Selectively Detect Copper Ions.

A 1,8-naphthalimide-based tripodal fluorescent ligand (L3) was synthesized through the copper (I) catalyzed Huisgen azide-alkyne cycloaddition reaction of 2-(2-azidoethyl)-6-morpholino-1H-benzo[de]isoquinoline-1,3(2H)-dione with triproparagylamine. Naphthalimide acts as the fluorophore while the triazole and amine nitrogens chelate the metal ion. L3 showed a selective fluorescence turn-off for Cu(II) over other metal ions in aqueous acetonitrile solution. A Job's plot, Benesi-Hildbrand plot and high-resolution mass spectrometry data confirm a 1:1 binding stoichiometry with a binding constant of 7.8 х105 M- 1 while addition of disodium EDTA demonstrates its reversibility. The structure and stability of the complex was supported by theoretical calculations. The limit of detection for Cu(II) was calculated to be 0.3 µM which is considerably lower than WHO recommended Cu(II) limit in drinking water.

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows.

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Postacquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: (1) mapping the IM axis to the LC axis for IM-MS data, (2) converting the drift time vs m/z space to CCS/z vs m/z space, and (3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. These new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

How mass spectrometry can be exploited to study AMPK.

AMP-activated protein kinase (AMPK) is a key regulator of metabolism and a recognised target for the treatment of metabolic diseases such as Type 2 diabetes (T2D). Here, we review how mass spectrometry (MS) can be used to study short-term control by AMPK via protein phosphorylation and long-term control due to changes in protein expression. We discuss how MS can quantify AMPK subunit levels in tissues from different species. We propose hydrogen-deuterium exchange (HDX)-MS to investigate molecular mechanisms of AMPK activation and thermoproteomic profiling (TPP) to assess off-target effects of pharmacological AMPK activators/inhibitors. Lastly, because large MS data sets are generated, we consider different approaches that can be used for their interpretation.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

Unveiling the fungal diversity and associated secondary metabolism on black apples.

Black apples are the result of late-stage microbial decomposition after falling to the ground. This phenomenon is highly comparable from year to year, with the filamentous fungus Monilinia fructigena most commonly being the first invader, followed by Penicillium expansum. Motivated by the fact that only little chemistry has been reported from apple microbiomes, we set out to investigate the chemical diversity and potential ecological roles of secondary metabolites (SMs) in a total of 38 black apples. Metabolomics analyses were conducted on either whole apples or small excisions of fungal biomass derived from black apples. Annotation of fungal SMs in black apple extracts was aided by the cultivation of 15 recently isolated fungal strains on 9 different substrates in a One Strain Many Compounds (OSMAC) approach, leading to the identification of 3,319 unique chemical features. Only 6.4% were attributable to known compounds based on analysis of high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS/MS) data using spectral library matching tools. Of the 1,606 features detected in the black apple extracts, 32% could be assigned as fungal-derived, due to their presence in the OSMAC-based training data set. Notably, the detection of several antifungal compounds indicates the importance of such compounds for the invasion of and control of other microbial competitors on apples. In conclusion, the diversity and abundance of microbial SMs on black apples were found to be much higher than that typically observed for other environmental microbiomes. Detection of SMs known to be produced by the six fungal species tested also highlights a succession of fungal growth following the initial invader M. fructigena.IMPORTANCEMicrobial secondary metabolites constitute a significant reservoir of biologically potent and clinically valuable chemical scaffolds. However, their usefulness is hampered by rapidly developing resistance, resulting in reduced profitability of such research endeavors. Hence, the ecological role of such microbial secondary metabolites must be considered to understand how best to utilize such compounds as chemotherapeutics. Here, we explore an under-investigated environmental microbiome in the case of black apples; a veritable "low-hanging fruit," with relatively high abundances and diversity of microbially produced secondary metabolites. Using both a targeted and untargeted metabolomics approach, the interplay between metabolites, other microbes, and the apple host itself was investigated. This study highlights the surprisingly low incidence of known secondary metabolites in such a system, highlighting the need to study the functionality of secondary metabolites in microbial interactions and complex microbiomes.

Quantifying the Diversity of an Atmospheric Aerosol Population in an Arctic Oil Field on a Single‐Particle Level

AbstractAs the Arctic rapidly warms, sea ice extent is decreasing and oil and gas extraction activities are expanding. Local combustion emissions affect the Arctic atmospheric aerosol chemical mixing state (the distribution of chemical species across the aerosol population), which impacts climate‐relevant properties. Bulk and single‐particle measurements of submicron aerosols were conducted at Oliktok Point, Alaska within the North Slope of Alaska oil fields. In this work, we quantify aerosol diversity using online single‐particle mass spectrometry data (32,880 individual particles), offline single‐particle microscopy data (20,912 individual particles), and online bulk aerosol mass spectrometry and aethalometer data. This method was used to derive individual particle mass fractions for both refractory and non‐refractory material within distinct particle types. Single‐particle, average single‐particle, and bulk population diversities (Di, Dα, Dγ, respectively) and mixing state indices (χ) were calculated for the data set. Calculated Di values were generally low (2.2 ± 0.6), as individual particle masses were dominated by a few chemical species of interest. Aged aerosol particles (those internally mixed with nitrate and/or sulfate) exhibited higher Di values (>3) compared to recently emitted (fresh) aerosol particles. During oil field plume periods, Dα values approached three due to the abundance of diesel combustion particles, which were rich in sulfate, black carbon, and organic aerosol. Overall, the submicron aerosol population within the Arctic oil fields was found to be relatively externally mixed (χ < 50%), due to the constant local emissions within oil fields combining with background aerosol and locally emitted sea spray aerosol at the coastal site.

Understanding proteome quantification in an interactive learning module on Google Cloud Platform.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on protein quantification in an interactive format that uses appropriate cloud resources for data access and analyses. Quantitative proteomics is a rapidly growing discipline due to the cutting-edge technologies of high resolution mass spectrometry. There are many data types to consider for proteome quantification including data dependent acquisition, data independent acquisition, multiplexing with Tandem Mass Tag reporter ions, spectral counts, and more. As part of the NIH NIGMS Sandbox effort, we developed a learning module to introduce students to mass spectrometry terminology, normalization methods, statistical designs, and basics of R programming. By utilizing the Google Cloud environment, the learning module is easily accessible without the need for complex installation procedures. The proteome quantification module demonstrates the analysis using a provided TMT10plex data set using MS3 reporter ion intensity quantitative values in a Jupyter notebook with an R kernel. The learning module begins with the raw intensities, performs normalization, and differential abundance analysis using limma models, and is designed for researchers with a basic understanding of mass spectrometry and R programming language. Learners walk away with a better understanding of how to navigate Google Cloud Platform for proteomic research, and with the basics of mass spectrometry data analysis at the command line. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.

Unraveling anti-aging mystery of green tea in C. elegans: Chemical truth and multiple mechanisms

Tea drinking impacts aging and aging-related diseases. However, knowledge of anti-aging molecules other than the major catechins in complex tea extracts remains limited. Here we used Caenorhabditis elegans to analyze the longevity effects of tea extracts and constituents comprehensively. We found that the hot water extract of green tea prolonged lifespan and heathspan. Further, the MeOH fraction prolonged lifespan significantly longer than other fractions. Correlation analysis between mass spectroscopic data and anti-aging activity suggests that ester-type catechins (ETCs) are the major anti-aging components, including 4 common ETCs, 6 phenylpropanoid-substituted ester-type catechins (PSECs), 5 cinnamoylated catechins (CCs), 7 ester-type flavoalkaloids (ETFs), and 4 cinnamoylated flavoalkaloids (CFs). CFs (200 μM) are the strongest anti-aging ETCs (with the longest 73% lifespan extension). Green tea hot water extracts and ETCs improved healthspan by enhancing stress resistance and reducing ROS accumulation. The mechanistic study suggests that they work by multiple pathways. Moreover, ETCs modulated gut microbial homeostasis, increased the content of short-chain fatty acids, and reduced fat content. Altogether, our study provides new evidence for the anti-aging benefits of green tea and insights into a deep understanding of the chemical truth and multi-target mechanism.

In Vitro and In Vivo Human Metabolism of Ostarine, a Selective Androgen Receptor Modulator and Doping Agent.

Ostarine (enobasarm) is a selective androgen receptor modulator with great therapeutic potential. However, it is also used by athletes to promote muscle growth and enhance performances without the typical adverse effects of anabolic steroids. Ostarine popularity increased in recent years, and it is currently the most abused "other anabolic agent" (subclass S1.2. of the "anabolic agents" class S1) from the World Anti-Doping Agency's (WADA) prohibited list. Several cases of liver toxicity were recently reported in regular users. Detecting ostarine or markers of intake in biological matrices is essential to document ostarine use in doping. Therefore, we sought to investigate ostarine metabolism to identify optimal markers of consumption. The substance was incubated with human hepatocytes, and urine samples from six ostarine-positive cases were screened. Analyses were performed via liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) and software-assisted data mining, with in silico metabolite predictions. Ten metabolites were identified with hydroxylation, ether cleavage, dealkylation, O-glucuronidation, and/or sulfation. The production of cyanophenol-sulfate might participate in the mechanism of ostarine liver toxicity. We suggest ostarine-glucuronide (C25H22O9N3F3, diagnostic fragments at m/z 118, 185, and 269) and hydroxybenzonitrile-ostarine-glucuronide (C25H22O10N3F3, diagnostic fragments at m/z 134, 185, and 269) in non-hydrolyzed urine and ostarine and hydroxybenzonitrile-ostarine (C19H14O4N3F3, diagnostic fragments at m/z 134, 185, and 269) in hydrolyzed urine as markers to document ostarine intake in doping.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

SynONIM: A Comprehensive Database of Synthetic Oligonucleotide Modifications and Impurities to Aid in Their Characterization by Mass Spectrometry.

Given the resurgence of oligonucleotides in the biotherapeutic space, there is a profound focus on their characterization by mass spectrometry. These therapeutic moieties commonly employ synthetic modifications to aid in increasing efficacy and stability; however, these modifications can also increase the complexity of mass spectrometry data analysis. Additionally, various stress conditions can affect both the observed level and type of impurities stemming from the variety of utilized modifications. Within the oligonucleotide analytical development community, a clear desire exists for a unified database of synthetic oligonucleotide modifications and impurities where information regarding structure, mass, and shorthand nomenclature can be contained. To address this, the authors have prepared an online database and webtool of synthetic oligonucleotide impurities and modifications, SynONIM, to centrally locate information key to the mass spectrometry community. SynONIM can be queried by elemental composition lost or gained, mass shift, shorthand notation, nucleotide location, and species origin.

A Carrier-Based Quantitative Proteomics Method Applied to Biomarker Discovery in Pericardial Fluid

Data-dependent liquid chromatography tandem mass spectrometry is challenged by the large concentration range of proteins in plasma and related fluids. We adapted the SCoPE method from single-cell proteomics to pericardial fluid, where a myocardial tissue carrier was used to aid protein quantification. The carrier proteome and patient samples were labeled with distinct isobaric labels, which allowed separate quantification. Undepleted pericardial fluid from patients with type 2 diabetes mellitus and/or heart failure undergoing heart surgery was analyzed with either a traditional liquid chromatography tandem mass spectrometry method or with the carrier proteome. In total, 1398 proteins were quantified with a carrier, compared to 265 without, and a higher proportion of these proteins were of myocardial origin. The number of differentially expressed proteins also increased nearly four-fold. For patients with both heart failure and type 2 diabetes mellitus, pathway analysis of upregulated proteins demonstrated the enrichment of immune activation, blood coagulation, and stress pathways. Overall, our work demonstrates the applicability of a carrier for enhanced protein quantification in challenging biological matrices such as pericardial fluid, with potential applications for biomarker discovery. Mass spectrometry data are available via ProteomeXchange with identifier PXD053450.

Scanning Gas Diffusion Electrode Setup for Real-Time Analysis of Catalyst Layers.

The scanning gas diffusion electrode (S-GDE) half-cell is introduced as a new tool to improve the evaluation of electrodes used in electrochemical energy conversion technologies. It allows both fast screening and fundamental studies of real catalyst layers by applying coupled mass spectrometry techniques such as inductively coupled plasma mass spectrometry and online gas mass spectrometry. Hence, the proposed setup overcomes the limitations of aqueous model systems and full cell-level studies, bridging the gap between the two approaches. In this proof-of-concept work, standard fuel cell electrodes are investigated at elevated oxygen reduction reaction current densities, while dissolved Pt x+ ions in the electrolyte and gaseous CO2 in the outlet gas stream are detected to track platinum dissolution and carbon corrosion, respectively. Relevant current densities of up to 0.75 A cm-2 are demonstrated. The electrochemically active surface area, oxygen reduction reaction activity, and Pt dissolution rates are quantified and benchmarked to the values obtained in the conventional stationary GDE half-cell. Moreover, it is found that Pt dissolution is suppressed when O2 is purged into the catalyst layer. Overall, this work demonstrates the feasibility of fast fuel cell electrode screening obtaining, complementary to electrochemical, mass spectrometry data necessary in fundamental studies on structure/performance relationships under actual reaction conditions. While Pt/C, in relevance to its fuel cell application, is used in this study, the proposed setup can be applied in water electrolysis, CO2 conversion, metal-air batteries, and other neighbor technologies.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry.

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Rapid identification of various chemical components in Cinnamomi ramulus by UPLC-Q-Orbitrap-MS.

Cinnamomi ramulus (CR) is a common Chinese herbal medicine with a long history. It is often used to treat exogenous wind-cold diseases in clinic, but its chemical compositions remain to be studied. In this study, CR was extracted with 75% ethanol, and UPLC-Q-Orbitrap-MS combined with data post-processing method was used to identify the chemical components in the extract. Through this technology, the components in CR can be separated and accurately identified. A total of 61 compounds were identified, including 14 simple phenylpropanoids, 3 coumarins, 5 lignans, 14 flavonoids, 10 benzoic acids, 8 organic acids, and 7 others. This study confirmed the existence of these compounds in CR and speculated the cleavage pathways of each compound, which enriched the mass spectrometry data and cleavage rules. This study can provide a reference for CR and other research.

Exploring the nematicidal mechanisms and control efficiencies of oxalic acid producing Aspergillus tubingensis WF01 against root-knot nematodes.

Root-knot nematodes (RKN; Meloidogyne spp.) are among the highly prevalent and significantly detrimental pathogens that cause severe economic and yield losses in crops. Currently, control of RKN primarily relies on the application of chemical nematicides but it has environmental and public health concerns, which open new doors for alternative methods in the form of biological control. In this study, we investigated the nematicidal and attractive activities of an endophytic strain WF01 against Meloidogyne incognita in concentration-dependent experiments. The active nematicidal metabolite was extracted in the WF01 crude extract through the Sephadex column, and its structure was identified by nuclear magnetic resonance and mass spectrometry data. The strain WF01 was identified as Aspergillus tubingensis based on morphological and molecular characteristics. The nematicidal and attractive metabolite of A. tubingensis WF01 was identified as oxalic acid (OA), which showed solid nematicidal activity against M. incognita, having LC50 of 27.48 μg ml-1. The Nsy-1 of AWC and Odr-7 of AWA were the primary neuron genes for Caenorhabditis elegans to detect OA. Under greenhouse, WF01 broth and 200 μg ml-1 OA could effectively suppress the disease caused by M. incognita on tomatoes respectively with control efficiency (CE) of 62.5% and 70.83%, and promote plant growth. In the field, WF01-WP and 8% OA-WP formulations showed moderate CEs of 51.25%-61.47% against RKN in tomato and tobacco. The combined application of WF01 and OA resulted in excellent CEs of 66.83% and 69.34% toward RKN in tomato and tobacco, respectively. Furthermore, the application of WF01 broth or OA significantly suppressed the infection of J2s in tomatoes by upregulating the expression levels of the genes (PAL, C4H, HCT, and F5H) related to lignin synthesis, and strengthened root lignification. Altogether, our results demonstrated that A. tubingensis WF01 exhibited multiple weapons to control RKN mediated by producing OA to lure and kill RKN in a concentration-dependent manner and strengthen root lignification. This fungus could serve as an environmental bio-nematicide for managing the diseases caused by RKN.