Summary of Study ST002504
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 PR001618. The data can be accessed directly via it's Project DOI: 10.21228/M8JT6D 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 | ST002504 |
| Study Title | Lipid droplets and peroxisomes are co-regulated to drive lifespan extension in response to mono-unsaturated fatty acids |
| Study Summary | Dietary mono-unsaturated fatty acids (MUFAs) are linked to human longevity and extend lifespan in several species. But the mechanisms by which MUFAs extend lifespan remain unclear. Here we show that an organelle network involving lipid droplets and peroxisomes is critical for lifespan extension by MUFAs in C. elegans. MUFA accumulation increases lipid droplet number in fat storage tissues, and this is necessary for MUFA-induced longevity. Lipid droplet number in young or middle-aged individuals can predict remaining lifespan, consistent with a beneficial effect of lipid droplets on lifespan. Lipidomics datasets reveal that MUFA accumulation also modifies the ratio of membrane lipids and ether lipids, a signature predictive of decreased lipid oxidation. We validate that MUFAs decrease lipid oxidation in middle-aged individuals, and that this is important for MUFA-induced longevity. Intriguingly, the increase in lipid droplet number in response to MUFAs is accompanied by a concomitant increase in peroxisome number. Using a targeted screen, we identify genes involved in the co-regulation or uncoupling of this lipid droplet-peroxisome network. We find that induction of both organelles is optimal for lifespan extension. Our study uncovers an organelle network involved in lipid homeostasis and lifespan regulation and identifies a mechanism of action for MUFAs to extend lifespan, opening new avenues for lipid-based interventions to delay aging. For the manuscript only the conditions “control” and “ash-2 RNAi” are plotted |
| Institute | Stanford University |
| Last Name | Papsdorf |
| First Name | Katharina |
| Address | 290 Jane Stanford way, 94301 Palo Alto, CA, USA |
| papsdorf@stanford.edu | |
| Phone | +1 650 546 5366 |
| Submit Date | 2023-02-03 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-03-20 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN004120 | AN004121 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | Reversed phase |
| Chromatography system | Thermo Dionex Ultimate 3000 RS | Thermo Dionex Ultimate 3000 RS |
| Column | Thermo Accucore C30 (150 x 2.1mm,2.6um) | Thermo Accucore C30 (150 x 2.1mm,2.6um) |
| MS Type | ESI | ESI |
| MS instrument type | Orbitrap | Orbitrap |
| MS instrument name | Thermo Q Exactive Orbitrap | Thermo Q Exactive Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | peak area | peak area |
MS:
| MS ID: | MS003867 |
| Analysis ID: | AN004120 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | LC-MS peak extraction, alignment, quantification and annotation was performed using LipidSearch software version 4.2.21 (Thermo Fisher Scientific). Lipids were identified by matching the precursor ion mass to a database and the experimental MS/MS spectra to a spectral library containing theoretical fragmentation spectra. To reduce the risk of misidentification, MS/MS spectra from lipids of interest were validated as follows: 1) both positive and negative mode MS/MS spectra match the expected fragments, 2) the main lipid adduct forms detected in positive and negative modes agree with the lipid class identified, 3) the retention time is compatible with the lipid class identified and 4) the peak shape is acceptable. The fragmentation pattern of each lipid class was experimentally validated using lipid internal standards. Single-point internal standard calibrations were used to estimate absolute concentrations using one internal standard for each lipid class. In cases with no exact lipid standard available lipids with molecular similarity were used. Further data processing was done using an in-house analysis pipeline written in R (Version 3.6.3, available in Github at https://github.com/brunetlab). Briefly, processing for samples and spike-in standards were done in the same way. All ions for one lipid were aggregated and lipids with a signal <0 discarded from further analysis. Lipid species were quantified using the corresponding internal standard (equiSPLASH, Avanti Polar Lipids, cat# 330731) for each lipid class. Lipids with signals lower than 3x blank signal were discarded. Lipids with more than 50% of missing values were discarded, and for the remaining missing values, imputation was performed. For this, a value was randomly assigned based on the bottom 5% for the corresponding lipid. Lipids were filtered for a coefficient of variance <0.5. Each sample was divided by its corresponding protein concentration to correct for sample input variations (protein concentrations can be found at https://github.com/brunetlab/Papsdorf_etal_2023). To calculate normalized abundance, each lipid within a sample was divided by the sample median followed by multiplication with the global median. This resulted in a total of 499 filtered and normalized lipids belonging to 16 lipid classes. For a list of identified lipid ions using LipidSearch see https://github.com/brunetlab/Papsdorf_etal_2023. |
| Ion Mode: | POSITIVE |
| MS ID: | MS003868 |
| Analysis ID: | AN004121 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | LC-MS peak extraction, alignment, quantification and annotation was performed using LipidSearch software version 4.2.21 (Thermo Fisher Scientific). Lipids were identified by matching the precursor ion mass to a database and the experimental MS/MS spectra to a spectral library containing theoretical fragmentation spectra. To reduce the risk of misidentification, MS/MS spectra from lipids of interest were validated as follows: 1) both positive and negative mode MS/MS spectra match the expected fragments, 2) the main lipid adduct forms detected in positive and negative modes agree with the lipid class identified, 3) the retention time is compatible with the lipid class identified and 4) the peak shape is acceptable. The fragmentation pattern of each lipid class was experimentally validated using lipid internal standards. Single-point internal standard calibrations were used to estimate absolute concentrations using one internal standard for each lipid class. In cases with no exact lipid standard available lipids with molecular similarity were used. Further data processing was done using an in-house analysis pipeline written in R (Version 3.6.3, available in Github at https://github.com/brunetlab). Briefly, processing for samples and spike-in standards were done in the same way. All ions for one lipid were aggregated and lipids with a signal <0 discarded from further analysis. Lipid species were quantified using the corresponding internal standard (equiSPLASH, Avanti Polar Lipids, cat# 330731) for each lipid class. Lipids with signals lower than 3x blank signal were discarded. Lipids with more than 50% of missing values were discarded, and for the remaining missing values, imputation was performed. For this, a value was randomly assigned based on the bottom 5% for the corresponding lipid. Lipids were filtered for a coefficient of variance <0.5. Each sample was divided by its corresponding protein concentration to correct for sample input variations (protein concentrations can be found at https://github.com/brunetlab/Papsdorf_etal_2023). To calculate normalized abundance, each lipid within a sample was divided by the sample median followed by multiplication with the global median. This resulted in a total of 499 filtered and normalized lipids belonging to 16 lipid classes.For a list of identified lipid ions using LipidSearch see https://github.com/brunetlab/Papsdorf_etal_2023. |
| Ion Mode: | NEGATIVE |
