Dataset Information

Quantitative variability of 342 plasma proteins in a human twin population

ABSTRACT: The human plasma proteome is clinically highly significant and has been studied extensively because it is thought that its molecular makeup provides a window into the health state of an individual. However, neither the quantitative variability of the plasma proteome nor the origins of the variation are known. To determine the relative contributions of heritability, environmental and longitudinal factors to plasma proteome variability we systematically decomposed the biological variance of 1904 peptides defining 342 unique plasma protein profiles from 232 plasma samples that were collected with 2-7 year intervals from monozygotic (MZ) and dizygotic (DZ) twins. The data were collected via SWATH mass spectrometry (SWATH-MS), an emerging technology characterized by high degree of reproducibility and quantitative accuracy. The data indicate abundance variability is an important feature for different proteins among population, that the abundances of about 20% of plasma protein are considerably heritable, and that the degrees of genetic control and aging effects vary across specific biological processes. Moreover, we identified 13 cis- SNPs significantly influencing the abundance level of specific plasma proteins. These results substantially extend the understanding of the impact of heritability on the human proteomic dynamics and therefore have implications for the effective design of plasma-based biomarker studies.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Dr Ruedi Aebersold

PROVIDER: MSV000079937 | MassIVE | Wed Jul 20 01:29:00 BST 2016

SECONDARY ACCESSION(S): PXD001064

REPOSITORIES: MassIVE

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Publications

Quantitative variability of 342 plasma proteins in a human twin population.

Liu Yansheng Y Buil Alfonso A Collins Ben C BC Gillet Ludovic C J LC Blum Lorenz C LC Cheng Lin-Yang LY Vitek Olga O Mouritsen Jeppe J Lachance Genevieve G Spector Tim D TD Dermitzakis Emmanouil T ET Aebersold Ruedi R

Molecular systems biology 20150204 1

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic ...[more]

PMID: 25652787

Similar Datasets

Project description:Proteome wide protein quantification has become facile in the last years supported by label-free discovery proteomics approaches and powerful, easy to use software. We set out to independently evaluate this highly peptide centric technology focusing on the performance of some of the currently most popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant and Progenesis QIP. The sample to sample variability introduced into protein abundance estimates by the technology itself, thereby affecting the validity of changes in protein abundance reported to be biological in nature, was determined. Using the same experimental design we were interested in the potential of shotgun proteomics to uncover biological contributions to changes in protein abundance in essentially isogenic plants grown under the same environmental conditions. The accuracy, precision, limit of quantification, intra-analysis repeatability and other performance metrics of protein quantification were determined with the softwares’ ready-to-run, default parameters and some modified settings. MaxQuant and Progenesis QIP each had decided advantages and exhibited direct proportionality between measured and actual protein abundance. The inherent variability in protein quantification in measurements of Arabidopsis thaliana proteomes was substantially higher than the biological sample to sample variability. Strikingly, correlation between Arabidopsis proteins quantified with MaxQuant and Progenesis QIP was poor due to differential protein inference. Both of these points warrant caution in the interpretation of shotgun proteomics studies. The abundance of around 99% of proteins was constant in repeated sampling of the Arabdiopsis thaliana Col-0 inbred accession; however a handful showed substantial quantitative variability. The function stress/stimulus response was highly significantly overrepresented for these proteins. Heritable information beyond genetic polymorphism that affects gene expression levels has recently been documented for these functions. We speculate on the potential of discovery proteomics to measure gene expression levels as a conduit of the epigenotype shaping quantitative traits.

Project description:Background: Long non-coding RNAs (lncRNAs) are increasingly implicated as gene regulators and may ultimately be more numerous than protein-coding genes in the human genome. Despite large numbers of reported lncRNAs, reference annotations are likely incomplete due to their lower and tighter tissue-specific expression compared to mRNAs. An unexplored factor potentially confounding lncRNA identification is inter-individual expression variability. Here, we characterize lncRNA natural expression variability in human primary granulocytes. Results: We annotate granulocyte lncRNAs and mRNAs in RNA-seq data from ten healthy individuals, identifying multiple lncRNAs absent from reference annotations, and use this to investigate three known features (higher tissue-specificity, lower expression, and reduced splicing efficiency) of lncRNAs relative to mRNAs. Expression variability was examined in seven individuals sampled three times at one or more than one month intervals. We show that lncRNAs display significantly more inter-individual expression variability compared to mRNAs. We confirm this finding in 2 independent human datasets by analyzing multiple tissues from the GTEx project and lymphoblastoid cell lines from the GEUVADIS project. Using the latter dataset we also show that including more human donors into the transcriptome annotation pipeline allows identification of an increasing number of lncRNAs, but minimally affects mRNA gene number. Conclusions: A comprehensive annotation of lncRNAs is known to require an approach that is sensitive to low and tight tissue-specific expression. Here we show that increased inter-individual expression variability is an additional general lncRNA feature to consider when creating a comprehensive annotation of human lncRNAs or proposing their use as prognostic or disease markers. We used PolyA+ RNA-seq data from human primary granulocytes of 10 healthy individuals to de novo annotate lncRNAs and mRNAs in this cell type and ribosomal depleted (total) RNA-seq data from seven of these individuals sampled three times to analyze lncRNA amd mRNA expression variability