Summary of Study ST002741
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001706. The data can be accessed directly via it's Project DOI: 10.21228/M8642W This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002741 |
| Study Title | Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge |
| Study Summary | Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37°C. |
| Institute | University of Nebraska-Lincoln |
| Last Name | Alvarez |
| First Name | Sophie |
| Address | 1901 Vine St |
| salvarez@unl.edu | |
| Phone | 4024724575 |
| Submit Date | 2023-06-19 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | abf, d |
| Analysis Type Detail | GC-MS |
| Release Date | 2023-08-10 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN004571 |
|---|---|
| Analysis type | MS |
| Chromatography type | GC |
| Chromatography system | Agilent 7890B |
| Column | Agilent HP5-MS (30m x 0.25mm, 0.25 um) |
| MS Type | EI |
| MS instrument type | Single quadrupole |
| MS instrument name | Agilent 5977A |
| Ion Mode | POSITIVE |
| Units | normalized intensity |
Chromatography:
| Chromatography ID: | CH003436 |
| Chromatography Summary: | The GC-MS analysis was carried out with an Agilent GC (Model 7890B) and MS Quadrupole (Model 5977A) (Agilent Technologies). The liquid injection was done using a PAL System RSI 85 (PAL, Lake Elmo, MN, USA). The injector temperature was 260°C; the MS transfer line was 230°C. Metabolites were separated on a 5% phenyl 95% dimethylarylene siloxane HP-5MS 30 m, 0.25 mm, 0.25 μm capillary column (Agilent Technologies), at constant flow 1.5 ml.min-1 of helium as a carrier gas. One mi-croliter of derivatized sample was injected into the injector operating in 1:5 split mode. The temperature of the column was initially set to 60°C, and increased at a rate of 10°C.min−1 to 325°C. |
| Instrument Name: | Agilent 7890B |
| Column Name: | Agilent HP5-MS (30m x 0.25mm, 0.25 um) |
| Column Temperature: | 60 |
| Flow Gradient: | N/A for GC |
| Flow Rate: | 1.5 ml/min |
| Solvent A: | N/A for GC |
| Solvent B: | N/A for GC |
| Chromatography Type: | GC |
| Solvent C: | N/A for GC |
