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Data Set Group2: EPFL/LISP BXD Liver, Hepatocytes, Soluable Proteins CD (Feb14) SRM
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| Specifics of this Data Set: |
| None |
| Summary: |
| Quantitative changes in transcriptome and proteome patterns relate genomic variation to specific phenotypes. Here we applied selected reaction monitoring (SRM), a targeted mass spectrometry method that supports the reliable and reproducible quantification of predetermined sets of proteins across a broad abundance range in complex samples to quantify 157 metabolic proteins in liver extracts from 40 genetically-diverse strains of the BXD mouse genetic reference population, after chow or high fat diet. We observed significant biological variation in protein levels, which were linked to transcript variation in ~30% of the cases. 14 genes mapped to quantitative trait loci (QTLs) at both the transcript and protein level, while a further 18 mapped only as transcripts (eQTLs), and 24 only as proteins (pQTLs). 79% of eQTLs were regulated by cis-mechanisms, as opposed to only 31% of pQTLs, indicating a more direct genetic connection between genes and their transcripts than between genes and their protein products. In some cases, QTLs could be linked to phenotypic changes across the BXDs. One such case indicates a novel animal model for an inborn error of metabolism that has been observed in humans; BXD mice with deficient DHTKD1 protein also exhibit 2-aminoadipic and 2-oxoadipic aciduria like seen in affected patients. Together, these findings show that quantitative, multi-layered genomic, transcriptomic, and proteomic analyses provide more power for connecting genetic variance to phenotypes in complex systems than each layer alone, and provide complementary information to identify novel regulatory networks of metabolic diseases. Note: please see associated dataset “Liver Proteome” EPFL/LISP BXD Liver, Hepatocytes, Soluable Proteins CD+HFD (Jul13) RPN” for protein data in the same animals. [NB: Data in review, but still unpublished as of Nov 2013, please contact admin.auwerx@epfl.ch for access] Note: please see associated dataset “LISP2” in BXD phenotypes for phenotype data on the same animals. [NB: Still unpublished as of Nov 2013, please contact admin.auwerx@epfl.ch for access] |
| About the cases used to generate this set of data: |
| 40 strains of the BXD family (BXD43 – BXD103) and both parental strains (C57BL/6 and DBA/2) were born and raised at the EPFL in Switzerland prior to inclusion in this study. For each strain, 10 male animals were born and then separated evenly into two cohorts at 8 weeks of age: 5 animals per strain on a chow diet (6% kcal/fat, 20% protein, 74% carbohydrate) and 5 animals per strain on high fat diet (60% kcal/fat, 20% protein, 20% carbohydrate). Animals adjusted to the diet for 8 weeks, and then an intensive phenotyping metabolic phenotyping protocol was followed from 16 to 24 weeks of age (respiration, cold tolerance, oral glucose response, VO2max exercise, voluntary exercise, basal activity). Animals were communally housed until the last 5 weeks of the experiment, when the animals could rest. Animals were fasted overnight prior to sacrifice, which occurred between 9am and 11am after isoflurane anesthesia and perfusion. The gall bladder was removed, and then the livers were immediately frozen in liquid nitrogen. |
| About the tissue used to generate this set of data: |
| Livers were later shattered in liquid nitrogen and ~100 mg fragments were taken at random from the left, right, or caudate lobes. To account for this discrepancy, all ~5 animals per cohort had their RNA prepared, and then were pooled evenly (by µg of RNA) into a single RNA sample for each cohort. These pooled RNA samples of approximately 30 µg RNA were then purified using RNEasy, then sent out for array analysis. All RIN values were > 8.0. |
| About the array platform: |
| All 81 arrays were Affymetrix Mouse Gene 1.0 ST, run together in a single batch in March/April 2013 at the University of Tennessee Health Science Center. |
| About data values and data processing: |
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| Notes: |
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SWATH MS is a novel technique that is based on data-independent acquisition (DIA) which aims to complement traditional mass spectrometry-based proteomics techniques such as shotgun and SRM methods. In principal, it allows a complete and permanent recording of all fragment ions of all peptide precursors in a biological sample and can thus potentially combine the advantages of shotgun (high throughput) with those of SRM (high reproducibility and sensitivity). The method uniquely combines a DIA methods with a innovative data analysis approach based on targeted data extraction developed in the Aebersold lab. Like in other DIA methods, the mass spectrometer cycles through precursor acquisition windows designed to cover the whole range of 400-1200 m/z - in which most of the proteotypic peptide precursors of an organism fall - within 2-4 seconds. During each cycle, the mass spectrometer will fragment all precursors from a given precursors window (e.g. 475 - 500 m/z for 25 Da windows) and record a complete, high accuracy fragment ion spectrum. The same range will be fragmented again in the next cycle, thus providing a time-resolved recording of fragment ions that elute on the chromatography. Thus the SWATH method provides highly multiplexed fragment ion spectra that are deterministically recorded over the complete chromatographic time. In the Malmstroem group, we are interested in the data-analysis challenge that is posed by DIA / SWATH data. Traditionally, DIA methods have been analyzed by trying to reconstruct the lineage of precursor and fragment ions based on their chromatographic elution profile, and then analysing the data in a workflow similar to those used in shotgun proteomics. However, these approaches suffered from low sensitivity and propagation of errors due to mis-assignment of fragment ions to precursor ions. We are thus working on automating targeted methods that are conceptually similar to SRM and allow querying the data multiple times with specific hypothesis, thus giving the researcher more control and specificity in the bioinformatic data analysis step. With these novel algorithms, it is potentially possible to explore a much larger part of the data that is present and obtain a nearly complete picture of a proteome. References
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| Experiment Type: |
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| Contributor: |
| Wu Y, Williams EG, Houten SM, Argmann CA, Wolski W, Auwerx J, Aebersold R. |
| Citation: |
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| Data source acknowledgment: |
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| Study Id: |
| 172 |
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