Project description:In this study, an enhanced structure-guided molecular networking (E-SGMN) method was used, which is specifically tailored for the Orbitrap Astral mass spectrometer.
Reverse phase liquid chromatography (RPLC) analysis. ACQUITY BEH C8 column (100 mm×2.1 mm, 1.7 ?m, Waters, Milford, MA, USA) was used for the LC system in positive (ESI+) ionization modes, respectively. The temperature was set to 50°C, and the gradient elution flow rate was set at 0.35 mL/min. The mobile phase consisted of water (A) and acetonitrile with 0.1% formic acid (B) in ESI+ ionization mode. The initial gradient started with 5% B for 1 min, followed by a linear increase to 100% B within 23 min, and then maintained for an additional 4 min. Finally, the gradient was reduced to 5% B for system equilibration.
Astral MS analysis. Mass spectrometry was performed on Orbitrap Astral (Thermo Fisher Scientific, Rockford, IL, USA) in full scan MS/ddMS2 mode. For the full scan properties of the Orbitrap detector, the MS1 scan range was 85-1250 m/z. Orbitrap resolution was 120,000. RF lens was set as 50%. Full-scan orbitrap MS1 was performed in parallel with fast and sensitive DDA MS2 (top 30) on the Astral mass analyzer, with an MS/MS acquisition rate of 150 Hz. The isolation window was 1 m/z. Normalized collision energy type was used in our work, and the HCD collision energy was set as 15%, 30%, 45% and 80%, respectively. The MS2 scan range was 50-1250 m/z. The normalized AGC target and injection time were 10% and 5 ms, respectively. The spray voltages were 3.5 kV and 3.0 kV in positive and negative ionization modes, respectively. The aux gas heater temperature and capillary temperature were 350°C and 320°C, respectively. The sheath gas and aux gas were 45 and 10 (in arbitrary units), respectively.
Project description:This is a dataset used for the orchestration of molecular networking which led the discovery of polyacetylated 18-norspirostanol saponins from Trillium tschonoskii.
Project description:Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry (HR-MS) in combination with fragment analysis (MS/MS) has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectra pose challenges to downstream analysis, given their complexity and size. Here we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. In total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlopidine. The molecular networking approach also generated networks of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline. The approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome. Keywords: Antihypertensive drugs, Drug metabolism, Fragmentation, High-resolution mass spectrometry, Metabolomics, Urine.
Project description:Example dataset for Methods in Molecular Biology Chapter - Feature Based Molecular Networking for Metabolite Annotation. This dataset includes the LC-MS/MS raw data (Bruker .d file format and centroided mzXML file format), metadata table used for the ste-by-step instructions, a batch file for MZmine2 data processing, and all resulting files from the MZmine2 and GNPS processing.
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RybB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RybB in a rne131 ΔrybB strain. RybB (without MS2) was used as control
Project description:Despite the overwhelming information about sRNAs, one of the biggest challenges in the sRNA field is characterizing sRNA targetomes. Thus, we develop a novel method to identify RNAs that interact with a specific sRNA, regardless of the type of regulation (positive or negative) or targets (mRNA, tRNA, sRNA). This method is called MAPS: MS2 affinity purification coupled with RNA sequencing. As proof of principle, we identified RNAs bound to RyhB, a well-characterized E. coli sRNA. Identification of RNAs co-purified with MS2-RyhB in a rne131 ?ryhB strain. RyhB (without MS2) was used as control
Project description:This study intends to explore the clinicopathological characteristics and survival prognosis of locally recurrent colorectal cancer patients with different treatment modes by retrospectively analyzing the medical records of locally recurrent colorectal cancer patients who received hospitalization in our center. Transcriptome sequencing and public databases were used to screen for molecular markers related to locally recurrent colorectal cancer and to explore molecular markers’ regulatory role in the progression of locally recurrent colorectal cancer.
Project description:MetDNA3 Astral datasets were generated using the high-resolution Thermo Fisher Orbitrap Astral mass spectrometry platform and annotated with MetDNA3, which integrates a Knowledge- and Data-driven Two-layer Networking strategy for accurate metabolite annotation in untargeted metabolomics. This dataset collection covers a variety of sample types, including: NIST plasma, NIST urine, Mouse liver. These datasets provide a valuable resource for the development, benchmarking, and validation of metabolomics annotation algorithms across diverse biological matrices.
Project description:Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.