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.

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Study IDST002741
Study TitleIntegration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge
Study SummaryMulti-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 NameAlvarez
First NameSophie
Address1901 Vine St
Emailsalvarez@unl.edu
Phone4024724575
Submit Date2023-06-19
Raw Data AvailableYes
Raw Data File Type(s)abf, d
Analysis Type DetailGC-MS
Release Date2023-08-10
Release Version1
Sophie Alvarez Sophie Alvarez
https://dx.doi.org/10.21228/M8642W
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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
SA301644930P30C
SA3016451030P30C
SA3016461130P30C
SA301647730P30C
SA301648830P30C
SA301649630P30C
SA301650230P30C
SA301651430P30C
SA301652330P30C
SA301653530P30C
SA301654837P37C
SA301655937P37C
SA3016561037P37C
SA301657737P37C
SA301658137P37C
SA301659237P37C
SA301660337P37C
SA301661437P37C
SA301662537P37C
SA301663637P37C
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
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