Complete Genome Sequence of Ralstonia syzygii subsp. indonesiensis Strain LLRS-1, Isolated from Wilted Tobacco in China.

Here, we present the complete genome sequence and annotation of Ralstonia syzygii subsp. indonesiensis strain LLRS-1, which caused bacterial wilt on flue-cured tobacco in Yunnan Province, southwest China. Strain LLRS-1 is the first R. syzygii strain identified to be pathogenic to tobacco in China. The completely assembled genome of strain LLRS-1 consists of a 3,648,314-bp circular chromosome and a 2,046,405-bp megaplasmid with 5,190 protein-coding genes, 55 transfer RNAs, 28 small RNAs, 3 structural RNAs (5S, 16S, and 23S), and a G+C content of 67.05%.

Midkine Inhibits Cholesterol Efflux by Decreasing ATP-Binding Membrane Cassette Transport Protein A1 via Adenosine Monophosphate-Activated Protein Kinase/Mammalian Target of Rapamycin Signaling in Macrophages.

BACKGROUND: Midkine (MK), a heparin-binding protein, participates in multiple cellular processes, such as immunity, cellular growth and apoptosis. Overwhelming evidence indicates that MK plays an important role in various pathological processes, including chronic inflammation, autoimmunity, cancer, and infection. Recent studies demonstrated that MK may be involved in the development of atherosclerosis, yet the mechanism has not been fully explored. Therefore, this study aims to investigate the effect and mechanism of MK on macrophage cholesterol efflux.Methods and Results:Using Oil Red O staining, NBD-cholesterol fluorescence labeling and enzymatic methods, it observed that MK markedly promoted macrophage lipid accumulation. Liquid scintillation counting (LSC) showed that MK decreased cholesterol efflux. Moreover, cell immunofluorescence, western blotting and quantitative real-time polymerase chain reaction (qRT-PCR) showed that MK downregulated ATP-binding membrane cassette transport protein A1 (ABCA1) expression. Functional promotion of ABCA1 expression attenuated the inhibitory effects of MK on cholesterol efflux, which reduced lipid accumulation. Additionally, intervention of adenosine monophosphate activated protein (AMPK)-mammalian target of rapamycin (mTOR) signaling molecule by the AMPK activator, AICAR, increased p-AMPK and ABCA1 expression, decreased p-mTOR expression and promoted cholesterol efflux, resulting in an obvious reduction in intracellular lipid content. CONCLUSIONS: These data suggest that MK reduces the expression of ABCA1, inhibits the efflux of cholesterol and promotes the accumulation of lipids in RAW264.7 macrophages, and AMPK-mTOR signaling is involved in MK-mediated regulation of cholesterol metabolism in RAW264.7 macrophages.

Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD.

Agricultural greenhouses (AGs) are an important facility for the development of modern agriculture. Accurately and effectively detecting AGs is a necessity for the strategic planning of modern agriculture. With the advent of deep learning algorithms, various convolutional neural network (CNN)-based models have been proposed for object detection with high spatial resolution images. In this paper, we conducted a comparative assessment of the three well-established CNN-based models, which are Faster R-CNN, You Look Only Once-v3 (YOLO v3), and Single Shot Multi-Box Detector (SSD) for detecting AGs. The transfer learning and fine-tuning approaches were implemented to train models. Accuracy and efficiency evaluation results show that YOLO v3 achieved the best performance according to the average precision (mAP), frames per second (FPS) metrics and visual inspection. The SSD demonstrated an advantage in detection speed with an FPS twice higher than Faster R-CNN, although their mAP is close on the test set. The trained models were also applied to two independent test sets, which proved that these models have a certain transability and the higher resolution images are significant for accuracy improvement. Our study suggests YOLO v3 with superiorities in both accuracy and computational efficiency can be applied to detect AGs using high-resolution satellite images operationally.

The diagnostic performance of gadoxetic acid disodium-enhanced magnetic resonance imaging and contrast-enhanced multi-detector computed tomography in detecting hepatocellular carcinoma: a meta-analysis of eight prospective studies.

AIM: The purpose of this study was to determine the relative diagnostic benefit of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) over contrast-enhanced multi-detector computed tomography (CEMDCT) for the detection of hepatocellular carcinoma (HCC). METHODS: Two investigators searched multiple databases from inception to January 8, 2019, for studies comparing Gd-EOB-DTPA-enhanced MRI with CEMDCT in adults suspected of HCC. Two reviewers independently selected studies and extracted data. RESULTS: Eight studies were included enrolling 498 patients. MRI showed significantly higher sensitivity than CT (0.85 vs. 0.68). There was no significant difference in the specificity of MRI and CT (0.94 vs. 0.93). The negative likelihood ratio and positive likelihood ratio of MRI and CT were not significantly different (0.16 vs. 0.15 and 14.7 vs. 11.2, respectively). The summary receiver operating characteristics (SROC) of MRI was higher than that of CT at 0.96 vs. 0.91. In the subgroup analysis with a lesion diameter below 2 cm, the sensitivity of MRI was significantly higher than that of CT (0.79 vs. 0.46). CONCLUSION: Gd-EOB-DTPA-enhanced MRI showed higher sensitivity and overall diagnostic accuracy than CEMDCT especially for hepatocellular carcinoma lesions smaller than 2 cm. KEY POINTS: • Gd-EOB-DTPA-enhanced MRI can detect small lesions of hepatocellular carcinoma. • Gd-EOB-DTPA-enhanced MRI showed higher sensitivity and overall diagnostic accuracy than CEMDCT in patients with hepatocellular carcinoma. • Eight prospective studies showed that Gd-EOB-DTPA-enhanced MRI provides greater diagnostic confidence.

An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China.

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010⁻2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.

Genome-wide investigation of the genes involved in nicotine metabolism in Pseudomonas putida J5 by Tn5 transposon mutagenesis.

Pseudomonas putida J5 is an efficient nicotine-degrading bacterial strain isolated from the tobacco rhizosphere. We successfully performed a comprehensive whole-genome analysis of nicotine metabolism-associated genes by Tn5 transposon mutagenesis in P. putida J5. A total of 18 mutants with unique insertions screened from 16,324 Tn5-transformants failed to use nicotine as the sole carbon source. Flanking sequences of the Tn5 transposon were cloned with a shotgun method from all of the nicotine-growth-deficient mutants. The potentially essential products of mutated gene were classified as follows: oxidoreductases, protein and metal transport systems, proteases and peptidases, transcriptional and translational regulators, and unknown proteins. Bioinformatic analysis of the Tn5 insertion sites indicated that the nicotine metabolic genes were separated and widely distributed in the genome. One of the mutants, M2022, was a Tn5 insert into a gene encoding a homolog of 6-hydroxy-L-nicotine oxidase, the second enzyme of nicotine metabolism in Arthrobacter nicotinovorans. Genetic and biochemical analysis confirmed that three open reading frames (ORFs) from an approximately 13-kb fragment recovered from the mutant M2022 were responsible for the transformation of nicotine to 3-succinoyl-pyridine via pseudooxynicotine and 3-succinoyl semialdehyde-pyridine, the first three steps of nicotine degradation. Further research on these mutants and the Tn5-inserted genes will help us characterize nicotine metabolic processes in P. putida J5.

The contribution of Pir protein family to yeast cell surface display.

Proteins with internal repeats (Pir) in the Baker's yeast are located on the cell wall and include four highly homologous members. Recently, Pir proteins have become increasingly used as anchor proteins in yeast cell surface display systems. These display systems are classified into three types: N-terminal fusion, C-terminal fusion, and inserted fusion. In addition to the GPI (glycosylphosphatidyl inositol) and the FL/FS anchor proteins, these three Pir-based systems significantly increase the choices for target proteins to be displayed. Furthermore, Pir proteins can also be used as a fusion partner for target proteins to be effectively secreted into culture medium. Here, we summarize the development and application of Pir proteins as anchor proteins.

Retrieval and analysis of atmospheric XCO2 using ground-based spectral observation.

Atmospheric CO2 column concentration (column-averaged dry air mole fractions of atmospheric carbon dioxide) data obtained by ground-based hyperspectral observation is an important source of data for the verification and improvement of the results of CO2 retrieval based on satellite hyperspectral observation. However, few studies have been conducted on atmospheric CO2 column concentration retrieval based on ground-based spectral hyperspectral observation in China. In the present study, we carried out the ground-based hyperspectral observation in Xilingol Grassland, Inner Mongolia of China by using an observation system which is consisted of an optical spectral analyzer, a sun tracker, and some other elements. The atmospheric CO2 column concentration was retrieved using the observed hyperspectral data. The effect of a wavelength shift of the observation spectra and the meteorological parameters on the retrieval precision of the atmospheric CO2 concentration was evaluated and analyzed. The results show that the mean value of atmospheric CO2 concentration was 390.9 microg x mL(-1) in the study area during the observing period from July to September. The shift of wavelength in the range between -0.012 and 0.042 nm will generally lead to 1 microg x mL(-1) deviation in the CO2 retrievals. This study also revealed that the spectral transmittance was sensitive to meteorological parameters in the wavelength range of 6 357-6 358, 6 360-6 361, and 6 363-6 364 cm(-1). By comparing the CO2 retrievals derived from the meteorological parameters observed in synchronous and non-synchronous time, respectively, with the spectral observation, it was showed that the concentration deviation caused by using the non-synchronously observed meteorological parameters is ranged from 0.11 to 4 microg x mL(-1). These results can be used as references for the further improvement of retrieving CO2 column concentration based on spectral observation.

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method.

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

Identification of diagnostic markers pyrodeath-related genes in non-alcoholic fatty liver disease based on machine learning and experiment validation.

Non-alcoholic fatty liver disease (NAFLD) poses a global health challenge. While pyroptosis is implicated in various diseases, its specific involvement in NAFLD remains unclear. Thus, our study aims to elucidate the role and mechanisms of pyroptosis in NAFLD. Utilizing data from the Gene Expression Omnibus (GEO) database, we analyzed the expression levels of pyroptosis-related genes (PRGs) in NAFLD and normal tissues using the R data package. We investigated protein interactions, correlations, and functional enrichment of these genes. Key genes were identified employing multiple machine learning techniques. Immunoinfiltration analyses were conducted to discern differences in immune cell populations between NAFLD patients and controls. Key gene expression was validated using a cell model. Analysis of GEO datasets, comprising 206 NAFLD samples and 10 controls, revealed two key PRGs (TIRAP, and GSDMD). Combining these genes yielded an area under the curve (AUC) of 0.996 for diagnosing NAFLD. In an external dataset, the AUC for the two key genes was 0.825. Nomogram, decision curve, and calibration curve analyses further validated their diagnostic efficacy. These genes were implicated in multiple pathways associated with NAFLD progression. Immunoinfiltration analysis showed significantly lower numbers of various immune cell types in NAFLD patient samples compared to controls. Single sample gene set enrichment analysis (ssGSEA) was employed to assess the immune microenvironment. Finally, the expression of the two key genes was validated in cell NAFLD model using qRT-PCR. We developed a prognostic model for NAFLD based on two PRGs, demonstrating robust predictive efficacy. Our findings enhance the understanding of pyroptosis in NAFLD and suggest potential avenues for therapeutic exploration.

The complete chloroplast genome of Rubus pinfaensis, an edible and medicinal dual-purpose plant.

Rubus pinfaensis H. Lév. & Vaniot is of great importance in the phylogeny and evolution amongst Rosaceae, genus Rubus L. plants. The chloroplast genome of R. pinfaensis was reported in this study, which is 155,523 bp in size, with an average GC content of 37.13%. The complete chloroplast genome has a typical quadripartite structure, including a large single copy (LSC) region (85,211 bp) and a small single copy (SSC) region (18,718 bp), which were separated a pair of inverted repeats (IRs, 25,797 bp). This plastome contained 129 different genes (112 unique), including 85 protein-coding genes (79 unique), 36 tRNA genes (29 unique), and 8 rRNA genes (4 unique). The chloroplast genome of R. pinfaensis has completed that will be based on the phylogeny and genomic studies in the family Rosaceae, genus Rubus L.

The Prognostic Significance of the TEAD4 in Hepatocellular Carcinoma.

Background: Abnormal expression of genes causes tumorigenesis, tumor progression, and poor prognosis in hepatocellular carcinoma (HCC). Therefore, the aims of this study were to explore the transcription enhancer domain factor 4 (TEAD4) in patients with liver cancer and its relationship with prognosis. Methods: HTSeq-FPKM data and corresponding clinical data of HCC patients were obtained from The Cancer Genome Atlas (TCGA). Difference in TEAD4 expression between normal and tumor and the correlation with clinical characteristics were analyzed by the chi-squared test based on UALCAN. HepG2 cell lines were used to study the effect of TEAD4 on HCC cell lines. The expression and clinical significance of TEAD4 in HCC were detected in clinical cases. Results: The transcription and post-transcription levels of TEAD4 were higher in HCC tumors than normal illustrated different expressed transcription of TEAD4 in gender, nodal metastasis status, tumor grades, and individual cancer stages. The high TEAD4 expression was significantly associated with tumor grades. The high expression of TEAD4 was significantly correlated to shorter 2-5 years overall survival. Inhibition of TEAD4 expression in HepG2 cells resulted in significantly decreased cell proliferation and invasion. Conclusion: TEAD4 was identified as an independent prognostic factor, and inhibition of TEAD4 expression in HepG2 cells resulted in significantly decreased cell proliferation and invasion.

Evaluation of ferritin and TfR level in plasma neural-derived exosomes as potential markers of Parkinson's disease.

Introduction: Early diagnosis of Parkinson's disease (PD) remains challenging. It has been suggested that abnormal brain iron metabolism leads to excessive iron accumulation in PD, although the mechanism of iron deposition is not yet fully understood. Ferritin and transferrin receptor (TfR) are involved in iron metabolism, and the exosome pathway is one mechanism by which ferritin is transported and regulated. While the blood of healthy animals contains a plentiful supply of TfR-positive exosomes, no studies have examined ferritin and TfR in plasma neural-derived exosomes. Methods: Plasma exosomes were obtained from 43 patients with PD and 34 healthy controls. Neural-derived exosomes were isolated with anti-human L1CAM antibody immunoabsorption. Transmission electron microscopy and western blotting were used to identify the exosomes. ELISAs were used to quantify ferritin and TfR levels in plasma neural-derived exosomes of patients with PD and controls. Receivers operating characteristic (ROC) curves were applied to map the diagnostic accuracy of ferritin and TfR. Independent predictors of the disease were identified using logistic regression models. Results: Neural-derived exosomes exhibited the typical exosomal morphology and expressed the specific exosome marker CD63. Ferritin and TfR levels in plasma neural-derived exosomes were significantly higher in patients with PD than controls (406.46 ± 241.86 vs. 245.62 ± 165.47 ng/µg, P = 0.001 and 1728.94 ± 766.71 vs. 1153.92 ± 539.30 ng/µg, P < 0.001, respectively). There were significant positive correlations between ferritin and TfR levels in plasma neural-derived exosomes in control group, PD group and all the individuals (rs = 0.744, 0.700, and 0.752, respectively). The level of TfR was independently associated with the disease (adjusted odds ratio 1.002; 95% CI 1.000-1.003). ROC performances of ferritin, TfR, and their combination were moderate (0.730, 0.812, and 0.808, respectively). However, no relationship was found between the biomarkers and disease progression. Conclusion: It is hypothesized that ferritin and TfR in plasma neural-derived exosomes may be potential biomarkers for PD, and that they may participate in the mechanism of excessive iron deposition in PD.

Corrigendum: Evaluation of ferritin and TfR level in plasma neural-derived exosomes as potential markers of Parkinson's disease.

[This corrects the article DOI: 10.3389/fnagi.2023.1216905.].

Comparison of homology models and crystal structures of cuticle-degrading proteases from nematophagous fungi: structural basis of nematicidal activity.

Cuticle-degrading proteases secreted by nematophagous fungi can degrade nematode cuticle during infection. Alkaline proteases from nematode-parasitic fungi show stronger nematicidal activity in vitro than neutral proteases from nematode-trapping fungi. Sequence alignment of these proteases revealed that the active-site residues were much conserved. Disulfide bridges in alkaline proteases not only contribute to the thermal stability of enzyme structure but also increase the flexibility of S1 and S4 pockets located at the substrate-binding site. Molecular electrostatic potential surfaces of these proteases change gradually from negative to positive while arranging in the order from neutral to alkaline proteases, possibly contributing to the distinct extent of substrate (nematode cuticle) attraction by proteases. The differences in flexibility of substrate-binding site and in electrostatic surface potential distribution between neutral and alkaline cuticle-degrading proteases are associated with the changes of their catalytic activities and nematicidal activities with fungal species. Our results indicate that nematode-parasitic and nematode-trapping fungi have evolved for distinct adaptation under selective pressure.

Culture-independent methods for studying environmental microorganisms: methods, application, and perspective.

Since the application of molecular methods, culture-independent methods (CIMs) have been developed to study microbial communities from various environments. In the past 20 years, several methods based on the direct amplification and analyses of the small subunit ribosomal RNA gene have been developed to directly study environmental microorganisms. These methods include denaturing/temperature gradient gel electrophoresis, single-strand-conformation polymorphism, restriction fragment length polymorphism, terminal restriction fragment length polymorphism, and quantitative polymerase chain reaction (PCR). Similarly, non-PCR-based molecular techniques, such as microarray and fluorescence in situ hybridization have also been adopted. In recent years, several novel fields of investigation such as metagenomics, metatranscriptomics, metaproteomics, and single-cell genomics were developed, largely propelled by the innovation and application of next-generation sequencing methods. Several single-cell-based technologies such as Raman microspectroscopy and nano-scale secondary ion mass spectrometry are also increasingly used in the fields of microbial ecology and environmental microbiology. The application of these methods has revolutionized microbiology by allowing scientists to directly analyze natural microbial communities in situ, including their genes, transcripts, proteins, and metabolites and how their interactions impact their distribution patterns. In this review, we present an up-to-date review on different CIMs and their applications, our focuses are on the comparison of different CIMs and their application in the analyses of microbial diversities and communities.

Emergency Information Communication Structure by Using Multimodel Fusion and Artificial Intelligence Algorithm.

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in the public domain. By observing the current situation of many countries and regions, we can find that various types of public crises often occur in many countries and regions in the world, which have severely affected people's daily life, lives, and property. Through long-term research and analysis, it can be known that the emergency management mechanism currently established in China has certain shortcomings. The communication problem of emergency information is likely to cause the emergency work to not proceed smoothly. In addition, problems in the communication channels of emergency information are likely to cause problems in the cooperation of various departments when people carry out emergency management work, and the efficiency of the government in dealing with problems will also be reduced in real scenarios. In order to improve the efficiency of emergency information management, this paper aims at the various problems existing and facing in the construction of emergency management system. On this basis, the integration of various relevant emergency information management plan models is analyzed and sorted out, and based on the research and integration of the development of artificial intelligence algorithms. The main research results of emergency information management at home and abroad are comprehensively studied and evaluated. Finally, a QG algorithm based on more model fusion is developed. In the process of analysis, this article uses artificial intelligence algorithms to build a prediction model of multiple modes and collects the data needed to build the model by random extraction. Through the analysis of different data sets, it is used as the basic training data for prediction. Through comprehensive analysis, the model constructed in this paper can promote the sharing of emergency information among departments to a certain extent.

Observation on the Effect of Intelligent Machine-Assisted Surgery and Perioperative Nursing.

Orthopedic surgery and care during the perioperative period are the key to the treatment of orthopedic diseases, which can quickly and effectively treat orthopedic diseases and can quickly recover during the perioperative period. Therefore, this paper focuses on the observation of the effect of intelligent machine-assisted surgery and perioperative care, combined with smart wearable devices and C-arm camera calibration; the details of the bone surgery are assisted by the machine, and then the recognition ability is accelerated by writing into the digital bone bank. Based on machine vision, CNN training and learning are designed to design a machine-assisted perioperative nursing method. This paper also designed a bone surgery test experiment and perioperative adverse event data analysis, combined with the data obtained from the experiment, designed a comparison experiment with traditional surgery and perioperative nursing. The experimental results show that the success rate of machine-assisted surgery is increased by nearly 2%-15% compared with traditional surgery; and the rehabilitation degree of machine-assisted perioperative nursing is 15.83% higher than that of traditional perioperative nursing.

The complete chloroplast genome of Rubus setchuenensis, an edible and medicinal dual-purpose wild plant.

Rubus setchuenensis Bureau et Franch. is important in phylogeny and evolution amongst genus Rubus L. (Rosaceae) plants. The chloroplast genome of R. setchuenensis reported in this study is 156,231 bp in size, with an average GC content of 37.19%. The complete chloroplast genome has a typical quadripartite structure, including a large single copy (LSC) region (85,829 bp) and a small single copy (SSC) region (18,860 bp), which are separated by a pair of inverted repeats (IRs, 25,771 bp). This plastome contains 129 different genes, including 85 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. The phylogenetic analysis of 20 chloroplast genomes from genera Fragaria, Rosa and Rubus of the family Rosaceae suggested that R. setchuenensis clustered into one clade with the other three species of section Malachobatus Focke, and then grouped with four species of section Idaeobatus Focke, while species from Fragaria and Rosa were classified into a group, separately.

Overexpression of a cuticle-degrading protease Ver112 increases the nematicidal activity of Paecilomyces lilacinus.

Due to their ability to degrade the proteins in nematode cuticle, serine proteases play an important role in the pathogenicity of nematophagous fungi against nematodes. The serine protease Ver112 was identified from the nematophagous fungus Lecanicillium psalliotae capable of degrading the nematode cuticle and killing nematodes effectively. In this study, the gene ver112 was introduced into the commercial biocontrol fungal agent Paecilomyces lilacinus by the restriction enzyme-mediated integration transformation. Compared to the wild strain, the transformant P. lilacinus 112 showed significantly greater protease activity, with nematicidal activities increased by 79% and 96% to Panagrellus redivivus and Caenorhabditis elegans at the second day, respectively. The crude protein extract isolated from the culture filtrate of P. lilacinus 112 also showed 20-25% higher nematicidal activity than that of the wild-type strain. Reverse transcription PCR results showed that the expression of gene ver112 in P. lilacinus 112 was correlated to protease activity of the culture filtrate. Our results demonstrated the first successful transfer of a virulence gene from one nematophagous fungus to another nematophagous fungus, and improved the pathogenicity of the recipient fungus against pest nematodes.