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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376/m_MTBLS14376_LC-MS_negative_hilic_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376/m_MTBLS14376_LC-MS_positive_hilic_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376/s_MTBLS14376.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376/a_MTBLS14376_LC-MS_negative_hilic.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376/a_MTBLS14376_LC-MS_positive_hilic.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14376And then peaks were matched with the mzCloud,mzVault and MassList database to obtain the accurate qualitative and relativequantitative results.Statistical analyses were performed using the statistical software R(R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6),When data were not normally distributed, standardize according to the formula:sample raw quantitation value / (The sum of sample metabolite quantitation value /The sum of QC1 sample metabolite quantitation value ) to obtain relative peak areas;And compounds whose CVs of relative peak areas in QC samples were greater than30% were removed, and finally the metabolites' identification and relativequantification results were obtained.MetaboLightsPublicLiquid Chromatography MS - positive - hilicLiquid Chromatography MS - negative - hilicThe samples were placed in the EP tubes and resuspended with prechilled 80%methanol by well vortex. Then the samples were melted on ice and whirled for 30 s.After the sonification for 6 min, they were centrifuged at 5,000 rpm, 4°C for 1 min.The supernatant was freeze-dried and dissolvedwith 10% methanol. Finally, thesolution was injected into the LC-MS/MS system analysisDecoding the Human Gut Bacterial Plasmids in Colorectal Cancer.JP LeeHuzhou Central HospitalFeces samplemass spectrometry assayThe samples were placed in the EP tubes and resuspended with prechilled 80%methanol by well vortex. Then the samples were melted on ice and whirled for 30 s.After the sonification for 6 min, they were centrifuged at 5,000 rpm, 4°C for 1 min.The supernatant was freeze-dried and dissolvedwith 10% methanol. Finally, thesolution was injected into the LC-MS/MS system analysisHomo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS14376Han Shuwen. Huzhou Central Hospital. 378964875@qq.com.Yang Xi. 378964875@qq.com.The raw data files generated by UHPLC-MS/MS were processed using the CompoundDiscoverer 3.3 (CD3.3, ThermoFisher) to perform peak alignment, peak picking, andquantitation for each metabolite. The main parameterswere set as follows:peak areawas corrected with the first QC, actual mass tolerance, 5ppm; signal intensitytolerance, 30%; and minimum intensity, et al. After that, peak intensities werenormalized to the total spectral intensity.The normalized data was used to predict themolecular formula based on additive ions, molecular ion peaks and fragment ions.Group378964875@qq.comCell or bacteria sampleThe samples were placed in the EP tubes and resuspended with prechilled 80%methanol by well vortex. Then the samples were melted on ice and whirled for 30 s.After the sonification for 6 min, they were centrifuged at 5,000 rpm, 4°C for 1 min.The supernatant was freeze-dried and dissolvedwith 10% methanol. Finally, thesolution was injected into the LC-MS/MS system analysisMetabolomicsThermo Scientific Vanquish UHPLC SystemMetabolomicsThermo Fisher Scientific (China)Feces samplecolorectal canceruntargeted analysisHomo sapiensCompound Discovererexperimental blankThermo Scientific Q Exactive HFThermo Scientific Vanquish UHPLC SystemMetabolomicsThermo Fisher Scientific (China)Feces samplecolorectal canceruntargeted analysisHomo sapiensCompound Discovererexperimental blankThermo Scientific Q Exactive HFThe samples were placed in the EP tubes and resuspended with prechilled 80%methanol by well vortex. Then the samples were melted on ice and whirled for 30 s.After the sonification for 6 min, they were centrifuged at 5,000 rpm, 4°C for 1 min.The supernatant was freeze-dried and dissolvedwith 10% methanol. Finally, thesolution was injected into the LC-MS/MS system analysisfalseDecoding the Human Gut Bacterial Plasmids in Colorectal CancerGut plasmids show heightened sensitivity to gut microenvironmental changes compared to their bacterial hosts. To explore their significance in colorectal cancer (CRC), we analyzed metagenomic data from 863 participants (312 CRC, 387 high-risk, 164 low-risk). Plasmid and bacterial profiles were characterized, along with trace elements and metabolites. Differential analysis, functional gene assessment (ARG, MGE, MRG, VFGB), random forest modeling, and structural equation modeling (SEM) were applied. In terms of overall abundance, plasmids in both the high-risk and CRC groups exhibited a decreasing trend. Gut plasmids significantly influenced the functional genes (ARG, MGE, MRG, VFGB) of their bacterial hosts. Six key bacterial hosts (Enterobacterales, Burkholderiales, Hyphomicrobiales, Lactobacillales, Bacteroidales, Campylobacterales) and 12 plasmid markers were identified. The plasmid-based model effectively predicted CRC risk. SEM revealed that trace elements (e.g., Ni), metabolites (e.g., 5-Hydroxytryptophol), and host bacteria (e.g., Campylobacterales, Enterobacterales) predominantly exerted negative effects on most plasmids, whereas Ni exhibited a positive influence on plasmids NZ_CP013564.1, NZ_CP024312.1, and NZ_CP48284.1. We characterized the composition of gut plasmids and their bacterial hosts, explored the impacts of gut plasmids on bacterial functionality, and mapped multi-omics interaction networks linking plasmids, hosts, and metabolic features.2026-04-262026-04-25MTBLS14376