Metabolome and Genome Analysis of a Novel Endophytic Fungus Aureobasidium pullulans KB3: Discovery of Polyketones and Polyketone Biosynthesis Pathway.

Endophytic fungi associated with plants may contain undiscovered bioactive compounds. Under standard laboratory conditions, most undiscovered compounds are inactive, whereas their production could be stimulated under different cultivation conditions. In this study, six endophytic fungi were isolated from the bark of Koelreuteria paniculata in Quancheng Park, Jinan City, Shandong Province, one of which was identified as a new subspecies of Aureobasidium pullulans, named A. pullulans KB3. Additionally, metabolomic tools were used to screen suitable media for A. pullulans KB3 fermentation, and the results showed that peptone dextrose medium (PDM) was more beneficial to culture A. pullulans KB3 for isolation of novel compounds. Sphaerolone, a polyketone compound, was initially isolated from A. pullulans KB3 via scaled up fermentation utilizing PDM. Additionally, the whole-genome DNA of A. pullulans KB3 was sequenced to facilitate compound isolation and identify the biosynthesis gene clusters (BGCs). This study reports the multi-omics (metabolome and genome) analysis of A. pullulans KB3, laying the foundation for discovering novel compounds of silent BGCs and identifying their biosynthesis pathway.

[Non-targeted metabolomics of spleen and liver metabolism in mice treated with Pruni Semen processed with different methods].

Ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was employed to investigate the impacts of Pruni Semen processed with different methods(raw and fried) on the liver and spleen metabolism in mice. A total of 24 male mice were randomly assigned to three groups: raw Pruni Semen group, fried Pruni Semen group, and control(deionized water) group. Mice in the three groups were orally administrated with 0.01 g·mL~(-1) Pruni Semen decoction or deionized water for one week. After that, the liver and spleen tissues were collected, and liquid chromatography-mass spectrometry(LC-MS)-based metabolomic analysis was carried out to investigate the impact of Pruni Semen on the liver and spleen metabolism in mice. Compared with thte control group, the raw Pruni Semen group showed up-regulation of 11 metabolites and down-regulation of 57 metabolites in the spleen(P<0.05), as well as up-regulation of 15 metabolites and down-regulation of 58 metabolites in the liver(P<0.05). The fried Pruni Semen group showed up-regulation of 31 metabolites and down-regulation of 10 metabolites in the spleen(P<0.05), along with up-regulation of 26 metabolites and down-regulation of 61 metabolites in the liver(P<0.05). The differential metabolites identified in the raw Pruni Semen group were primarily associated with alanine, aspartate, and glutamate metabolism, purine metabolism, amino sugar and nucleotide sugar metabolism, and D-glutamine and D-glutamate metabolism. The differential metabolites identified in the fried Pruni Semen group predominantly involved riboflavin metabolism, amino sugar and nucleotide sugar metabolism, purine metabolism, alanine, aspartate, and glutamate metabolism, D-glutamine and D-glutamate metabolism, and glutathione metabolism. The findings suggest that both raw and fried Pruni Semen have the potential to modulate the metabolism of the liver and spleen in mice by influencing the glutamine and glutamate metabolism.

Associations Among Social Jet Lag, Sleep-Related Characteristics, and Burnout of Nurses in Tertiary Hospitals.

To investigate the status of social jet lag (SJL) through sociodemographic factors among clinical nurses and examine the correlation with burnout. There has been relatively little research on the possible factors resulting in SJL among nurses in China and its role in burnout. A multicenter cross-sectional study recruited 596 nurses from 7 Chinese hospitals. Online questionnaires were delivered to assess sociodemographics, shift work, SJL, chronotypes, and the burnout of nurses. Nurses had severe levels of SJL. The number of children, forms of employment, specialty area, length of professional service, and chronotypes were the main predictors of SJL. Moreover, SJL affected burnout (emotional exhaustion and deindividuation), and reducing the nurses' SJL could relieve their burnout. Additional evidence-based interventions indicate that reducing the SJL is essential as the nurses are suffering severe job burnout.

Biofilm formation and antioxidation were responsible for the increased resistance of N. eutropha to chloramination for drinking water treatment.

Chloramination is an effective strategy for eliminating pathogens from drinking water and repressing their regrowth in water distribution systems. However, the inevitable release of NH4+ potentially promotes nitrification and associated ammonia-oxidizing bacteria (AOB) contamination. In this study, AOB (Nitrosomona eutropha) were isolated from environmental water and treated with two disinfection stages (chloramine disinfection and chloramine residues) to investigate the occurrence mechanisms of AOB in chloramination. The results showed that N. eutropha had considerable resistance to monochloramine compared to Escherichia coli, whose inactivation rate constant was 19.4-fold lower. The higher resistance was attributed to high levels of extracellular polymer substances (EPS) in AOB, which contribute to AOB surviving disinfection and entering the distribution system. In AOB response to the chloramine residues stage, the respiratory activity of N. eutropha remained at a high level after three days of continuous exposure to high chloramine residue concentrations (0.5-1.5 mg/L). Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) suggested that the mechanism of N. eutropha tolerance involved a significantly high expression of the intracellular oxidative stress-regulating (sodB, txrA) and protein-related (NE1545, NE1546) genes. Additionally, this process enhanced EPS secretion and promoted biofilm formation. Adhesion predictions based on the XDLVO theory corroborated the trend of biofilm formation. Overall, the naturally higher resistance contributed to the survival of AOB in primary disinfection; the enhanced antioxidant response of surviving N. eutropha accompanied by biofilm formation was responsible for their increased resistance to the residual chloramines.

Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes.

PURPOSE: To investigate the value of computed tomography (CT) radiomic feature analysis for the differential diagnosis between thymic epithelial tumors (TETs) and thymic cysts, and prediction of histological subtypes of TETs. MATERIALS AND METHODS: Twenty-four patients with TETs (13 low-risk and 9 high-risk thymomas, and 2 thymic carcinomas) and 12 with thymic cysts were included in this study. For each lesion, the radiomic features of a volume of interest covering the lesion were extracted from non-contrast enhanced CT images. The Least Absolute Shrinkage and Selection Operator (Lasso) method was used for the feature selection. Predictive models for differentiating TETs from thymic cysts (model A), and high risk thymomas + thymic carcinomas from low risk thymomas (model B) were created from the selected features. The receiver operating characteristic curve was used to evaluate the effectiveness of radiomic feature analysis for differentiating among these tumors. RESULTS: In model A, the selected 5 radiomic features for the model A were NGLDM_Contrast, GLCM_Correlation, GLZLM_SZLGE, DISCRETIZED_HISTO_Entropy_log2, and DISCRETIZED_HUmin. In model B, sphericity was the only selected feature. The area under the curve, sensitivity, and specificity of radiomic feature analysis were 1 (95% confidence interval [CI]: 1-1), 100%, and 100%, respectively, for differentiating TETs from thymic cysts (model A), and 0.76 (95%CI: 0.53-0.99), 64%, and 100% respectively, for differentiating high-risk thymomas + thymic carcinomas from low-risk thymomas (model B). CONCLUSION: CT radiomic analysis could be utilized as a non-invasive imaging technique for differentiating TETs from thymic cysts, and high-risk thymomas + thymic carcinomas from low-risk thymomas.

Hidden risks: Simulated leakage of domestic sewage to secondary water supply systems poses serious microbiological risks.

There are continuous reports about the pollution of the secondary water supply systems (SWSSs), among which domestic sewage leakage is the most serious. In this study, a pilot experiment lasting 70 days was conducted to explore the changes in physicochemical water quality and the microbial profiles in SWSSs polluted by different doses of domestic sewage through qPCR and high-throughput sequencing methods. The results showed that when domestic sewage entered the simulated water storage tank, a large amount of organic matter brought by domestic sewage quickly consumed chlorine disinfectants. High pollution levels (pollution index ≥ 1/1000) were accompanied by significant increases in turbidity and ammonia nitrogen concentration (p < 0.05) and by abnormal changes in sensory properties. Although different microbial community structures were found only at high pollution levels, qPCR results showed that the abundance of the bacterial 16S rRNA gene and some pathogenic gene markers in the polluted tank increased with the pollution level, and the specific gene marker of pathogens could be detected even at imperceptible pollution levels. In particular, the high detection frequency and abundance of Escherichia coli and Enterococcus faecails in polluted tank water samples demonstrated that they can be used for early warning. Moreover, it seems that the microorganisms that came with the domestic sewage lost their cultivability soon after entering SWSSs but could recover their activities during stagnation. In addition, the biofilm biomass in the polluted tank with high pollution levels increased faster at the initial stage, while after a longer contact time, it tended to remain at the same level as the control tank. This study emphasized the high microbial risk introduced by sewage water leakage even at imperceptible levels and could provide scientific suggestions for early warning and prevention of pollution to SWSSs.

Data mining from process monitoring of typical polluting enterprise.

With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R2) with values of 0.8510, 0.9565, 0.9561, 0.9677, and 0.9061 for chemical oxygen demand (COD), potential of hydrogen (pH), electrical conductivity (EC), flue gas emission (FGE), and non-methane hydrocarbon concentration (NMHC) respectively. For the first time, the variable importance measure (VIM)-assisted BP-ANN was employed to investigate the internal and external correlations between wastewater and exhaust gas treatment, thereby enhancing the interpretability of mapping features in the BP-ANN model. The predicted errors for pH and FGE have been demonstrated to fall within the range of - 0.62 ~ 0.30 and - 0.21 ~ 0.15 m3/s, respectively, with average relative errors of 1.05% and 9.60%, which is advantageous in detecting anomalous data and forecasting pollution indicator values. Our approach successfully addresses the challenge of segregating data analysis for wastewater disposal and exhaust gas disposal in the process monitoring of polluting enterprises, while also unearthing potential variables that significantly contribute to the BP-ANN model, thereby facilitating the selection and extraction of characteristic variables.

An anoikis-related gene signature predicts prognosis and reveals immune infiltration in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms. Materials and methods: We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs. Results: A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine. Conclusion: The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.

Insights into the associations of copper and zinc with nitrogen metabolism during manure composting with shrimp shell powder.

The application of shrimp shell powder (SSP) in manure composting can promote the maturation of compost and reduce the associated environmental risk. This study investigated the response of adding SSP at different levels (CK: 0, L: 5%, M: 10%, and H: 15%) on heavy metal resistance genes (MRGs), nitrogen functional genes, enzymes, and microorganisms. SSP inhibited nitrification and denitrification via decreasing the abundances of functional genes and key enzymes related to Cu, Zn, and MRGs. The nitrate reductase and nitrous-oxide reductase in the denitrification pathway were lower under H. Phylogenetic trees indicated that Burkholderiales sp. had strong relationships with OTU396 and OTU333, with important roles in the nitrogen cycle and plant growth. Redundancy analysis and structural equation modeling showed the complex response between heavy metal and nitrogen that bio-Cu and bio-Zn had positive significantly relationships with nirK-type and amoA-type bacteria, and amoA-type bacteria might be hotspot of cueO.

Self-trapped exciton emission and piezochromism in conventional 3D lead bromide perovskite nanocrystals under high pressure.

Developing single-component materials with bright-white emission is required for energy-saving applications. Self-trapped exciton (STE) emission is regarded as a robust way to generate intrinsic white light in halide perovskites. However, STE emission usually occurs in low-dimensional perovskites whereby a lower level of structural connectivity reduces the conductivity. Enabling conventional three-dimensional (3D) perovskites to produce STEs to elicit competitive white emission is challenging. Here, we first achieved STEs-related emission of white light with outstanding chromaticity coordinates of (0.330, 0.325) in typical 3D perovskites, Mn-doped CsPbBr3 nanocrystals (NCs), through pressure processing. Remarkable piezochromism from red to blue was also realized in compressed Mn-doped CsPbBr3 NCs. Doping engineering by size-mismatched Mn dopants could give rise to the formation of localized carriers. Hence, high pressure could further induce octahedra distortion to accommodate the STEs, which has never occurred in pure 3D perovskites. Our study not only offers deep insights into the photophysical nature of perovskites, it also provides a promising strategy towards high-quality, stable white-light emission.

Responses of bacterial communities and antibiotic resistance genes to nano-cellulose addition during pig manure composting.

Treatment with exogenous additives during composting can help to alleviate the accumulation of antibiotic resistance genes (ARGs) caused by the direct application of pig manure to farmland. In addition, nano-cellulose has an excellent capacity for adsorbing pollutants. Thus, the effects of adding 300, 600, and 900 mg/kg nano-cellulose to compost on the bacterial communities, mobile genetic elements (MGEs), and ARGs were determined in this study. After composting, treatment with nano-cellulose significantly reduced the relative abundance of ARGs, which was lowest in the compost product with 600 mg/kg added nano-cellulose. Nano-cellulose inhibited the rebound in ARGs from the cooling period to the maturity period, and weakened the selective pressure of heavy metals on microorganisms by passivating bio-Cu. The results also showed that MGEs explained most of the changes in the abundances of ARGs, and MGEs had direct effects on ARGs. The addition of 600 mg/kg nano-cellulose reduced the abundances of bacterial genera associated with ermQ, tetG, and other genes, and the number of links (16) between ARGs and MGEs was lowest in the treatment with 600 mg/kg added nano-cellulose. Therefore, adding 600 mg/kg nano-cellulose reduced the abundances of ARGs by affecting host bacteria and MGEs. The results obtained in this study demonstrate the positive effect of nano-cellulose on ARG pollution in poultry manure, where adding 600 mg/kg nano-cellulose was most effective at reducing the abundances of ARGs.

Identification of iron ore brands by multi-component analysis and chemometric tools.

Identification of iron ore brand is one of the most important precautions against fraud in the international iron ore trade. However, the identification of iron ore brand can be sophisticated, due to fact that the role played by multi-component in iron ore brand identification was unclear. This study aims to establish an objective approach to identify iron ore brands based on their multi-component content. A total of 1469 batches of iron ore samples, covering 16 commonly consumed iron ore brands from 3 countries, were analyzed for multi-component content. It was investigated that 10 primary, minor, and trace chemical components varied significantly in contents according to different iron ore brands. This prospective relationship between the multi-component contents and the iron ore brand was then used to place 16 brands into 12 groups and 8 brands of them were correctly identified by a flowchart. Furthermore, chemometric tools such as linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were applied to construct models to simultaneously discriminate 16 iron ore brands. Both the training and test results proved that LDA performed best in this circumstance. In the LDA method, MgO, Fe, SiO2, and P are the feature components contributing the most to the identification of 16 brands of iron ore. Based on the findings, the multi-components are distinct variables to establish an internationally recognized model of iron ore brand identification.

Interpretable deep learning-assisted laser-induced breakdown spectroscopy for brand classification of iron ores.

Brand classification of iron ores using laser-induced breakdown spectroscopy (LIBS) combined with artificial neural networks can quickly realize the compliance verification and guarantee the interests of both trading partners. However, its practical application is impeded by complex pretreatments and unexplained feature learning problems. According to the LIBS data characteristics of iron ores, a convolutional neural network (CNN) is designed to predict 16 types of brand iron ores from Australia, Brazil, and South Africa. The accuracies of the calibration set and the prediction set with five-fold cross-validation (5-CV) were 99.86% and 99.88%, and the value of loss function was 0.0356. Meanwhile, the established CNN method was also compared with common machine learning methods using raw spectra as input variables, and it outperformed other methods. For the first time, this work interprets the CNN's effectiveness layer by layer in self-adaptively extracting LIBS features through t-distributed stochastic neighbor embedding (t-SNE) and the quantitative data of major chemical components in iron ores. Our approach shows that deep learning assisted LIBS is able to significantly reduce manual factors in preprocessing and feature selection and has broad application prospects in the brand classification of iron ores.

Enhanced removal of antibiotic resistance genes and mobile genetic elements during swine manure composting inoculated with mature compost.

Livestock manure is a major source of antibiotic resistance genes (ARGs) that enter the environment. This study assessed the effects of inoculation with mature compost (MC) on the fates of ARGs and the bacterial community during swine manure composting. The results showed that MC prolonged the thermophilic period and promoted the decomposition of organic matter, which was due to the rapid growth and reproduction of thermophilic bacteria (Bacillus, Thermobifida, and Thermobacillus). MC significantly reduced the relative abundances of ARGs (1.02 logs) and mobile genetic elements (MGEs) (1.70 logs) after composting, especially sulfanilamide resistance genes. The total ARGs removal rate was 1.11 times higher in MC than the control. Redundancy analysis and structural equation modeling showed that horizontal gene transfer mediated by MGEs (ISCR1 and intI1) was the main direct factor related to the changes in ARGs during composting, whereas the C/N ratio and pH were the two most important indirect factors. Network analysis showed that members of Firmicutes comprising Romboutsia, Clostridisensu_stricto_1, and Terrisporobacter were the main bacterial hosts of ARGs and MGEs. MC reduced the risk of ARGs transmission by decreasing the abundances of bacterial hosts. Thus, MC is a promising strategy for reducing the proliferation risk of ARGs.

Pressure-Induced Emission toward Harvesting Cold White Light from Warm White Light.

The pressure-induced emission (PIE) behavior of halide perovskites has attracted widespread attention and has potential application in pressure sensing. However, high-pressure reversibility largely inhibits practical applications. Here, we describe the emission enhancement and non-doping control of the color temperature in two-dimensional perovskite (C6 H5 CH2 CH2 NH3 )2 PbCl4 ((PEA)2 PbCl4 ) nanocrystals (NCs) through high-pressure processing. A remarkable 5 times PIE was achieved at a mild pressure of 0.4 GPa, which was highly associated with the enhanced radiative recombination of self-trapped excitons. Of particular importance is the retention of the 1.6 times emission of dense (PEA)2 PbCl4 NCs upon the complete release of pressure, accompanied by a color change from "warm" (4403 K) to "cold" white light with 14295 K. The irreversible pressure-induced structural amorphization, which facilitates the remaining local distortion of inorganic Pb-Cl octahedra with respect to the steric hindrance of organic PEA+ cations, should be greatly responsible for the quenched high-efficiency photoluminescence.

Effects of phosphogypsum and medical stone on nitrogen transformation, nitrogen functional genes, and bacterial community during aerobic composting.

This study explored the effects of adding phosphogypsum (PPG), medical stone (MS), and both (PPM) during composting on nitrogen transformation, nitrogen functional genes, the bacterial community, and their relationships with NH3 and N2O emissions. Adding MS and PPM reduced NH3 emissions by 25.78-68.37% and N2O emissions by 19.00-42.86%. PPG reduced NH3 emissions by 59.74% but slightly increased N2O emissions by 8.15%. MS was strongly correlated with the amoA-dominated nitrification process. PPG and PPM had strong correlations with nirS- and nirK-dominated, and nosZ-dominated denitrification processes, respectively. PPM promoted nitrification and denitrification processes more than PPG and MS. Different functional bacteria had key roles in nitrification and denitrification during different composting stages. Firmicutes probably contributed to the conversion and release of nitrogen in the thermophilic period, whereas Proteobacteria, Chloroflexi, Bacteroidetes, and other phyla might have played important roles in the cooling and maturation periods. PPM obtained the greatest reductions in NH3 and N2O release via the regulation of environmental variables, nitrogen functional genes, and the bacterial community. Overall, these results provide insights at a molecular level into the effects of PPG and MS on nitrogen transformation and NH3 and N2O emissions during composting.

Effects of shrimp shell powder on antibiotic resistance genes and the bacterial community during swine manure composting.

The present study compared the effects of adding shrimp shell powder (SSP) at four levels comprising 0% (CK), 5% (L), 10% (M), and 15% (H) on the abundance of antibiotic resistance genes (ARGs) and the bacterial community succession during swine manure composting. The relative abundances of 5/11 ARGs were reduced in CK, and 7/11 in H. Moreover, the removal rate was enhanced by adding SSP. Thus, H decreased the total abundance of ARGs by 32.68%, whereas CK increased it by 6.31%. Redundancy analysis indicated that mobile genetic elements (MGEs) (46.6%) and the bacterial community (31.1%) mainly explained the changes in ARGs. H enhanced the removal of MGEs, prolonged the thermophilic phase, stabilized copper and zinc, and retained nitrogen. LEfSe analysis and non-metric multidimensional scaling indicated that the bacterial community changed in the composting process, and it was optimized by H. The abundances of the potential bacterial co-hosts (such as Lactobacillus, Corynebacterium_1, and Ornithinicoccus) of ARGs and MGEs were lower and the decomposition of organic matter was higher in H compared with CK. Thus, composting with 15% SSP can reduce the risk of ARGs and improve the practical value for agronomic application.

Microbial succession and molecular ecological networks response to the addition of superphosphate and phosphogypsum during swine manure composting.

This study assessed the effects of superphosphate (SPP) and phosphogypsum (PPG) on the bacterial and fungal community succession and molecular ecological networks during composting. Adding SPP and PPG had positive effects on the bacterial richness and diversity, negative effects on the fungal richness and diversity. The microbial diversity and richness were higher in PPG than SPP. Non-metric multidimensional scaling analysis clearly separated SPP and PPG from the control treatment with no additives. The dominant genera comprised Turicibacter, Bacillus, norank_o_SBR1031, Thermobifida, norank_f_Limnochordaceae, Truepera, Thermopolyspora, Mycothermus, Dipodascus, Thermomyces, and unclassified_p_Ascomycota. In all treatments, the major bacterial species differed clearly in the later thermophilic, cooling, and maturation composting stages, whereas the main fungal species varied significantly in the thermophilic stage. The changes in the dominant microorganisms in SPP and PPG may have inhibited or promoted the degradation of organic matter during various composting stages. Adding SPP and PPG led to more complex bacterial networks and less complex fungal networks, where SPP had more adverse effects on the fungal networks than PPG. SPP and PPG could potentially alter the co-occurrence patterns of the bacterial and fungal communities by changing the most influential species. SPP and PPG changed the composition and succession of the microbial community by influencing different physiochemical properties during various composting stages where the pH was the main explanatory factor. Overall, this study provides new insights into the effects of SPP and PPG on the microbial community and its interactions during composting.

Comparative genome analysis and mining of secondary metabolites of Paenibacillus polymyxa.

Paenibacillus polymyxa is a well-known Gram-positive biocontrol bacterium. It has been reported that many P. polymyxa strains can inhibit bacteria, fungi and other plant pathogens. Paenibacillus polymyxa employs a variety of mechanisms to promote plant growth, so it is necessary to understand the biocontrol ability of bacteria at the genome level. In the present study, thanks to the widespread availability of Paenibacillus genome data and the development of bioinformatics tools, we were able to analyze and mine the genomes of 43 P. polymyxa strains. The strain NCTC4744 was determined not to be P. polymyxa according to digital DNA-DNA hybridization and average nucleotide identity. By analysis of the pan-genome and the core genome, we found that the pan-genome of P. polymyxa was open and that there were 3,192 core genes. In a gene cluster analysis of secondary metabolites, 797 secondary metabolite gene clusters were found, of which 343 are not similar to known clusters and are expected to reveal a large number of new secondary metabolites. We also analyzed the plant growth-promoting genes that were mined and found, surpisingly, that these genes are highly conserved. The results of the present study not only reveal a large number of unknown potential secondary metabolite gene clusters in P. polymyxa, but also suggest that plant growth promotion characteristics are evolutionary adaptations of P. polymyxa to plant-related habitats.