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Proteomics. 2018 Jan;18(2). doi: 10.1002/pmic.201700238. Epub 2017 Dec 27.

Proteomic Characterization of Caenorhabditis elegans Larval Development.

Author information

1
Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen, Denmark.
2
Department of Anatomy and Developmental Biology, Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Monash University, Melbourne, Australia.
3
Cancer Division, Garvan Institute of Medical Research and The Kinghorn Cancer Centre, St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, Australia.

Abstract

The nematode Caenorhabditis elegans is widely used as a model organism to study cell and developmental biology. Quantitative proteomics of C. elegans is still in its infancy and, so far, most studies have been performed on adult worm samples. Here, we used quantitative mass spectrometry to characterize protein level changes across the four larval developmental stages (L1-L4) of C. elegans. In total, we identified 4130 proteins, and quantified 1541 proteins that were present across all four stages in three biological replicates from independent experiments. Using hierarchical clustering and functional ontological analyses, we identified 21 clusters containing proteins with similar protein profiles across the four stages, and highlighted the most overrepresented biological functions in each of these protein clusters. In addition, we used the dataset to identify putative larval stage-specific proteins in each individual developmental stage, as well as in the early and late developmental stages. In summary, this dataset provides system-wide analysis of protein level changes across the four C. elegans larval developmental stages, which serves as a useful resource for the C. elegans research community. MS data were deposited in ProteomeXchange (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the primary accession identifier PXD006676.

KEYWORDS:

Caenorhabditis elegans; animal proteomics; bioinformatics; development; mass spectrometry

PMID:
29178193
DOI:
10.1002/pmic.201700238

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