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:
Project:
Project ID: | PR001706 |
Project DOI: | doi: 10.21228/M8642W |
Project Title: | Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge |
Project 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, Lincoln, Nebraska, 68588, USA |
Email: | salvarez@unl.edu |
Phone: | 4024724575 |
Subject:
Subject ID: | SU002918 |
Subject Type: | Cultured cells |
Subject Species: | Lentilactobacillus kefiri |
Species Group: | Bacteria |
Factors:
Subject type: Cultured cells; Subject species: Lentilactobacillus kefiri (Factor headings shown in green)
mb_sample_id | local_sample_id | Treatment |
---|---|---|
SA301644 | 930P | 30C |
SA301645 | 1030P | 30C |
SA301646 | 1130P | 30C |
SA301647 | 730P | 30C |
SA301648 | 830P | 30C |
SA301649 | 630P | 30C |
SA301650 | 230P | 30C |
SA301651 | 430P | 30C |
SA301652 | 330P | 30C |
SA301653 | 530P | 30C |
SA301654 | 837P | 37C |
SA301655 | 937P | 37C |
SA301656 | 1037P | 37C |
SA301657 | 737P | 37C |
SA301658 | 137P | 37C |
SA301659 | 237P | 37C |
SA301660 | 337P | 37C |
SA301661 | 437P | 37C |
SA301662 | 537P | 37C |
SA301663 | 637P | 37C |
Showing results 1 to 20 of 20 |
Collection:
Collection ID: | CO002911 |
Collection Summary: | All samples were initially inoculated with a mixed culture of L. kefiri and L. kefiranofaciens inoculum blended from 3-day cultures. 20 mL MRS medium was inocu-lated with 0.5 mL of the combined inoculum and incubated for 3 days, one set of 10 at 30°C, and another set of 10 at 37°C. For controls, L. kefiri and L. kefiranofaciens were inoculated in triplicate for each temperature. The tubes were removed from the incubator after 3 days, and 0.1 mL of each sample was placed in a 96 well plate for OD (600 nm) analysis. Samples were then centrifuged (3000×g, 4°C, 10 min), and the supernatant was removed and frozen for further analysis. The pellet was washed in fresh MRS (pH 5.5), divided in three aliquots, re-pelleted in microcentrifuge tubes, and frozen at -80°C for subsequent transcriptomic, proteomic, and metabolomic analysis. |
Sample Type: | Bacterial cells |
Treatment:
Treatment ID: | TR002927 |
Treatment Summary: | All samples were initially inoculated with a mixed culture of L. kefiri and L. kefiranofaciens inoculum blended from 3-day cultures. 20 mL MRS medium was inoculated with 0.5 mL of the combined inoculum and incubated for 3 days, one set of 10 at 30°C, and another set of 10 at 37°C. For controls, L. kefiri and L. kefiranofaciens were inoculated in triplicate for each temperature. The tubes were removed from the incubator after 3 days, and 0.1 mL of each sample was placed in a 96 well plate for OD (600 nm) analysis. Samples were then centrifuged (3000×g, 4°C, 10 min), and the supernatant was removed and frozen for further analysis. The pellet was washed in fresh MRS (pH 5.5), divided in three aliquots, re-pelleted in microcentrifuge tubes, and frozen at -80°C for subsequent metabolomic analysis. |
Sample Preparation:
Sampleprep ID: | SP002924 |
Sampleprep Summary: | Cell pellets were washed 3 times with cold PBS to remove any cell media left after collection. The cell pellets were then extracted using cold 100% methanol and spiked with 40 μL of 10 pinitol (internal standard). A quality control (QC) sample was prepared by mixing the same amount of each sample into one. The supernatants were then dried down using a speed-vac and then resuspended in 20 mg/mL methoxyamine hydrochloride reagent prepared in pure pyridine and incubated for 2 hr at 37 °C on a platform shaker at 1000 rpm. Next, for derivatization, the MSTFA +1% TMCS deri-vatization (ThermoFisher) was added to each sample, incubated for 30 min at 37°C on a platform shaker at 1000 rpm followed by a centrifugation for 10 min at 16,000 g prior to transferring the mixture to GC vials for injection into GC-MS. |
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 |
MS:
MS ID: | MS004317 |
Analysis ID: | AN004571 |
Instrument Name: | Agilent 5977A |
Instrument Type: | Single quadrupole |
MS Type: | EI |
MS Comments: | The data was analyzed using MS-Dial (version 4.9) for peak detection, deconvolution, alignment, quantification, normalization, and identification. Putative identification of the metabolites was based on the Kovats retention index (RI) and the matching score of the mass spectra with the libraries. Two libraries were used, a local library made from running authentic standards with Kovats RI, and a public spectrum library, the curated Kovats RI with a total of 28,220 compounds (last edited August 21th, 2022, which includes the Fiehn, RIKEN and MoNA databases). The peaks were manually curated reviewed for peak shape, chromatogram alignment integrity and MS/MS match, and the final list of compounds with RI similarities >95% were report-ed. The data was normalized based on the internal standard spiked in the samples during extraction and using LOWESS (locally weighted scatterplot smoothing) for QC-batch normalization. |
Ion Mode: | POSITIVE |