The data matrix obtained by searching database was uploaded to the Majorbio cloud platform (https://cloud.majorbio.com) for data analysis. Fistly, the data matrix was pre-processed, as follows: At least 80% of the metabolic features detected in any set of samples were retained. After filtering, the minimum value in the data matrix was selected to fill the missing value and each metabolic signature was normalized to the sum. To reduce the errors caused by sample preparation and instrument instability, the response intensities of the sample mass spectrometry peaks were normalized using the sum normalization method, to obtain the normalized data matrix. Meanwhile, the variables of QC samples with relative standard deviation (RSD) > 30% were excluded and log10 logarithmicized, to obtain the final data matrix for subsequent analysis.Perform variance analysis on the matrix file after data preprocessing. The R package “ropls”(Version 1.6.2) was used to perform principal component analysis (PCA) and orthogonal least partial squares discriminant analysis (OPLS-DA), and 7-cycle interactive validation evaluating the stability of the model. The metabolites with VIP>1, p<0.05 were determined as significantly different metabolites based on the Variable importance in the projeciton (VIP) obtained by the OPLS-DAmodel and the p-value generated by student’s t test. Differential metabolites among two groups were mapped into their biochemical pathways through metabolic enrichment and pathway analysis based on KEGG database (http://www. genome.jp/kegg/). These metabolites could be classified according to the pathways they involved or the functions they performed. Enrichment analysis was used to analyze a group of metabolites in a function node whether appears or not. The principle was that the annotation analysis of a single metabolite develops into an annotation analysis of a group of metabolites. Python packages “scipy.stats” (https://docs.scipy.org/doc/scipy/ ) was used to perform enrichment analysis to obtain the most relevant biological pathways for experimental treatments
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - negative - reverse-phase","Liquid Chromatography MS - positive - reverse-phase"],"chromatography_protocol":["Chromatographic separation was performed on an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm; Waters, Milford, USA). Mobile phase A: 95% water + 5% acetonitrile (0.1% formic acid); Mobile phase B: 47.5% acetonitrile + 47.5% isopropanol + 5% water (0.1% formic acid). Injection volume: 3 μL; column temperature: 40℃.
"],"publication":["Biocontrol potential and antifungal mechanism of three native endophytic Bacillus."],"submitter_affiliation":["Henan Normal University"],"submitter_name":["Chaochuang Li"],"organism_part":["Whole Organism"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["After cultivation, the culture was centrifuged to collect the supernatant, then filtered through a 0.22-micron microporous membrane, rapidly frozen in liquid nitrogen, and freeze-dried using a freeze-drying machine (Beijing Sihuan, China). This resulted in powder samples. During metabolite extraction, each freeze-dried sample was accurately weighed at 20 ± 5 milligrams and transferred to a 2-milliliter centrifuge tube containing 6-millimeter grinding beads. Then, 400 microliters of extraction solution (methanol: water = 4:1, v/v) was added. The sample was ground using a frozen tissue grinder at −10°C, at a frequency of 50 Hz, for 6 minutes, followed by ultrasonic extraction at 5°C and 40 kHz for 30 minutes. Subsequently, the sample was centrifuged at −20°C for 30 minutes and then centrifuged at 4°C at 13,000 grams for 15 minutes to collect the supernatant.
"],"organism":["blank","Bacillus","Brevibacillus"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS14657"],"author":["Chaochuang Li. Henan Normal University. leezc90@163.com.","Houmin Wang.","Longlong Ma. m17539610163@163.com."],"data_transformation_protocol":["The pretreatment of LC/MS raw data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included: sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix, deredundant and peak pooled. At the same time, the metabolites were identified by searching database, and the main databases (HMDB, Metlin, NIST, MassBank, Lipidblast, GNPS, etc) and the self-compiled Majorbio Database (MJDB) of Majorbio Biotechnology Co., Ltd. (Shanghai, China)
"],"study_factor":["Group"],"submitter_email":["leezc90@163.com"],"sample_collection_protocol":["Endophytic bacterial strains were cultured in liquid Luria-Bertani (LB) medium at 37℃ with shaking at 180 rpm for 36 h.
"],"omics_type":["Metabolomics"],"study_design":["Metabolomics","blank","Thermo Scientific Orbitrap Exploris 240","untargeted analysis","Bacillus","quality control sample","Brevibacillus","Thermo Scientific HPLC","chinese yam","Whole Organism","endophytic Bacillus","experimental sample"],"curator_keywords":["Metabolomics","blank","Thermo Scientific Orbitrap Exploris 240","untargeted analysis","Bacillus","quality control sample","Brevibacillus","Thermo Scientific HPLC","chinese yam","endophytic Bacillus","Whole Organism","experimental sample"],"mass_spectrometry_protocol":["m/z, 70-1050; Sheath gas flow rate, 60 arb; Aux gas flow rate, 20 arb; Heater temp, 350℃; Capillary temp, 320℃; Spray voltage(+), 3400V; Spray voltage(-), -3000V; S-Lens RF Level, 60; Normalized collision energy(%), 0,40,60; Resolution (Full MS), 60000; Resolution (MS2), 15000
"],"additional_accession":[]},"is_claimable":false,"name":"Biocontrol potential and antifungal mechanism of three native endophytic Bacillus","description":"Chinese yam tubers are nutrient-rich but highly susceptible to mechanical damage during harvest, which facilitates pathogenic fungal infection at wound sites. This study aimed to identify pathogenic fungi from postharvest yam wounds and screen native endophytic bacteria with antifungal activity for postharvest preservation. Here, three pathogenic fungi, Aspergillus niger DoCHT1, A. flavus DoCHT2, and Clonostachys chloroleuca DoCHT3, were isolated from infected yam wounds. In parallel, three endophytic bacterial strains, namely Bacillus E Do22 and E Do23, and Brevibacillus E Do24, exhibited strong antagonistic activity against all three pathogens.
","dates":{"publication":"2026-10-06","submission":"2026-06-02"},"accession":"MTBLS14657","cross_references":{}}