Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Optimization of 13C stable isotope labeling for the study of tricarboxylic cycle intermediates in mouse models

ABSTRACT: Data for "Optimization of 13C stable isotope labeling for the study of tricarboxylic cycle intermediates in infected mouse models"

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Laura-Isobel McCall

PROVIDER: MSV000097457 | MassIVE | Mon Mar 31 22:10:00 BST 2025

REPOSITORIES: MassIVE

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Mixing of Escherichia coli cells labeled with 13C glucose with unlabeled E. coli cells for benchmarking of protein-based stable isotope probing proteomics (Protein-SIP)

Project description:This dataset is part of a study aimed at developing algorithms for the quantification of stable isotope content in microorganisms after labeling them with stable isotope-labeled substrates. In this dataset Escherichia coli cultures were labeled with different percentages (1% or 10%) of either single-carbon 13C glucose (13C2) or fully-labeled 13C glucose (13C1-6). Labeled cells were subsequently mixed with unlabeled E. coli cells in fixed ratios (50%, 90%, 95%, 99%). Cultures of E. coli were grown in M9 minimal medium in which a percentage of the glucose was replaced with 13C2 or 13C1-6 glucose for >10 generations to achieve close to complete labeling of cells. Triplicate cultures were grown for each percentage. Please note that the unlabeled glucose that was used of course had a natural content of 13C of around 1.1%, thus the 0% added label samples have an actual 13C content of 1.1% and all added label is on top of this value. We included a tab delimited table with this submission providing details on all raw files.

2022-12-30 | PXD024287 | Pride

Limitation in Protein-SIP - Exploring the limits of robust detection of incorporation of 13C by mass spectrometry in protein-based stable isotope probing (protein-SIP)

Project description:Mass spectra of Ps. fluorescens grown on partially 13C-labeled galactose. Peptide and protein identification of 1-DE bands.

2014-08-26 | PRD000456 | Pride

Mock microbial community with spike-ins of 13C-labeled Escherichia coli cells for benchmarking of protein-based stable isotope probing proteomics (Protein-SIP)

Project description:This dataset is part of a study aimed at developing algorithms for the quantification of stable isotope content in microorganisms in microbial communities after labeling them with stable isotope-labeled substrates. For this dataset Escherichia coli cultures were labeled with different percentages (1, 5 and 10%) of fully labeled 13C glucose (13C1-6) and spiked-in into a mock microbial community consisting of 32 species of bacteria, archaea, eukaryote and bacteriophages (UNEVEN Community described in Kleiner et al. 2017 Nat Communications 8(1):1558). The community also contained unlabeled E. coli cells and labeled/unlabeled E. coli cells in the spike-in sample were at a 1:1 ratio. Cultures of E. coli were grown in M9 minimal medium in which a percentage of the glucose was replaced with 13C1-6 glucose for >10 generations to achieve close to complete labeling of cells. The following percentages of 13C1-6 glucose were added 1, 5 and 10%. Triplicate cultures were grown for each percentage. Please note that the unlabeled glucose that was used of course had a natural content of 13C of around 1.1%, thus the 0% added label samples have an actual 13C content of 1.1% and all added label is on top of this value. We included a tab delimited table with this submission providing details on all raw files.

2022-12-30 | PXD024174 | Pride

Diagnosing underdetermination in stable isotope mixing models.

Project description:Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdetermination of mixing space remains problematic, i.e., the estimated relative contributions are incompletely identifiable. Here we propose a statistical method to quantitatively diagnose underdetermination in Bayesian SIMMs, and demonstrate the applications of our method (named β-dependent SIMM) using two motivated examples. Using a simulation example, we showed that the proposed method can rigorously quantify the expected underdetermination (i.e., intervals of β-dependent posterior) of relative contributions. Moreover, the application to the published field data highlighted two problematic aspects of the underdetermination: 1) ordinary SIMMs was difficult to quantify underdetermination of each source, and 2) the marginal posterior median was not necessarily consistent with the joint posterior peak in the case of underdetermination. Our study theoretically and numerically confirmed that β-dependent SIMMs provide a useful diagnostic tool for the underdetermined mixing problem. In addition to ordinary SIMMs, we recommend reporting the results of β-dependent SIMMs to obtain a biologically feasible and sound interpretation from stable isotope data.

| S-EPMC8486109 | biostudies-literature

Labeling of Bacillus subtilis and Escherichia coli to saturation with different percentages of fully 13C labeled glucose for benchmarking of protein-based stable isotope probing proteomics (Protein-SIP)

Project description:This dataset is part of a study aimed at developing algorithms for the quantification of stable isotope content in microorganisms after labeling them with stable isotope-labeled substrates. In this dataset Escherichia coli and Bacillus subtilis cultures were labeled with different percentages of fully labeled 13C glucose (13C6). Cultures of B. subtilis and E. coli were grown in Bacillus minimal medium or M9 minimal medium (E. coli) in which a percentage of the glucose was replaced with 13C6 glucose for >10 generations to achieve close to complete labeling of cells. The following percentages of 13C6 glucose were added 0, 0.01, 0.025, 0.1, 0.25, 1, 5 and 10%. Triplicate cultures were grown for each percentage. Please note that the unlabeled glucose that was used of course had a natural content of 13C of around 1.1%, thus the 0% added label samples have an actual 13C content of 1.1% and all added label is on top of this value. We included a tab delimited table with this submission providing details on all raw files.

2022-12-30 | PXD023693 | Pride

Mitochondrial complex I promotes kidney cancer metastasis

Project description:Most kidney cancers display metabolic dysfunction but how this relates to cancer progression in humans is unknown. We infused 13C-labeled nutrients during surgical tumour resection in over 80 patients with kidney cancer. Labeling from [U-13C]glucose varies across subtypes, indicating that the kidney environment alone cannot account for all metabolic reprogramming in these tumours. Compared to the adjacent kidney, clear cell renal cell carcinomas (ccRCC) display suppressed labelling of tricarboxylic acid (TCA) cycle intermediates in vivo and in organotypic cultures ex vivo, indicating that suppressed labeling is tissue intrinsic. Infusions of [1,2-13C]acetate and [U-13C]glutamine in patients, coupled with measurements of respiration in mitochondria isolated from kidneys and tumours, reveal electron transport chain (ETC) defects in ccRCC. However, ccRCC metastases unexpectedly have enhanced TCA cycle labeling compared to primary ccRCCs, indicating a divergent metabolic program during metastasis in patients. In mice, stimulating respiration or NADH recycling in kidney cancer cells is sufficient to promote metastasis, while inhibiting ETC complex I decreases metastasis. These findings indicate that metabolic properties and liabilities evolve during kidney cancer progression in humans, and that mitochondrial function is limiting for metastasis but not for growth at the original site.

2024-06-17 | GSE251905 | GEO

WGA protocol optimization

Project description:WGA protocol optimization

| PRJEB34940 | ENA

Genome-Resolved Proteomic Stable Isotope Probing of Soil Microbial Communities Using 13CO2 and 13C-Methanol.

Project description:Stable isotope probing (SIP) enables tracking the nutrient flows from isotopically labeled substrates to specific microorganisms in microbial communities. In proteomic SIP, labeled proteins synthesized by the microbial consumers of labeled substrates are identified with a shotgun proteomics approach. Here, proteomic SIP was combined with targeted metagenomic binning to reconstruct metagenome-assembled genomes (MAGs) of the microorganisms producing labeled proteins. This approach was used to track carbon flows from 13CO2 to the rhizosphere communities of Zea mays, Triticum aestivum, and Arabidopsis thaliana. Rhizosphere microorganisms that assimilated plant-derived 13C were capable of metabolic and signaling interactions with their plant hosts, as shown by their MAGs containing genes for phytohormone modulation, quorum sensing, and transport and metabolism of nutrients typical of those found in root exudates. XoxF-type methanol dehydrogenases were among the most abundant proteins identified in the rhizosphere metaproteomes. 13C-methanol proteomic SIP was used to test the hypothesis that XoxF was used to metabolize and assimilate methanol in the rhizosphere. We detected 7 13C-labeled XoxF proteins and identified methylotrophic pathways in the MAGs of 8 13C-labeled microorganisms, which supported the hypothesis. These two studies demonstrated the capability of proteomic SIP for functional characterization of active microorganisms in complex microbial communities.

| S-EPMC6908837 | biostudies-literature

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13C proteomics

Project description:Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13C isotope tracing in peptides to elucidate cross-fed metabolites in cocultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of 13C-based proteomics for uncovering microbial interactions.

2023-02-07 | PXD019952 | Pride

In vivo isotope tracing with 13C glucose and 13C glutamine in mouse primary hepatocytes with AT-1 overexpression and haploinsufficiency

Project description:Primary Mouse hepatocytes were introduced to U-13C glucose or U-13C glutamine for 30 min and then analyzed by GC-MS and LC-MS.

2019-06-03 | MSV000083885 | MassIVE

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data