Proteomic Characterization of Caenorhabditis elegans Larval Development

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.

Organelle size scaling over embryonic development

“…Complementary methods allow for more acute, temporally regulated degradation of endogenous proteins (S. Roth, Fulcher, & Sapkota, 2019). Comprehensive transcriptomics and proteomics databases of early development in a variety of organisms provide a rich resource to identify putative developmental regulators of organelle size (Casas-Vila et al, 2017;Lucitt et al, 2008;Peshkin et al, 2015;Xia et al, 2018).…”

Identification of Non-Canonical Translation Products in C. elegans Using Tandem Mass Spectrometry

“…Despite stringent concatenation of redundancy in the allORF database, we anticipated that the sheer number of entries might increase false positives in proteomic mapping. To test this, we utilized available C. elegans raw data from Xia et al (2018) (larval - PXD006676), Narayan et al (2016) (adult - PXD004584) and Edifizi et al (2017) (stressed - PXD005649) and re-analysed over 120 raw files of 240 min gradient runs each against our allORF database using PEAKS. These datasets are interesting from a discovery perspective because they cover a range of physiological changes within the worm: the larval dataset contains samples of all 4 larval stages (L1-L4), the adult dataset includes day 1, day 5 and day 10 adult worms, and stressed samples were subjected to UV-irradiation or starvation.…”

“… Numbers of identified proteins (grouped) in larval Xia et al, 2018 , adult Narayan et al, 2016 and stressed Edifizi et al, 2017 C. elegans LC-MS/MS datasets, with at least 1 unique peptide at 1% FDR. (A) Total number of unique identifications (B) non-canonical proteins as predicted by Openprot and sORFs.org .…”

“…For meta-analysis of proteomic datasets of larval, adult and Stress C. elegans , raw files were accessed from ProteomeXchange ( http://www.proteomexchange.org/ ) via their respective IDs (PXD006676 ( Xia et al, 2018 ), PXD004584 ( Narayan et al, 2016 ), PXD005649 ( Edifizi et al, 2017 )). The raw files were then analysed on PEAKS Studio X (Bioinformatics Solutions Inc., Canada) with precursor tolerance set to 10 ppm and a fragment tolerance of 0.02 Da with fully tryptic digestion and 2 allowed missed cleavages.…”

Mesenchymal stromal cell activation by breast cancer secretomes in bioengineered 3D microenvironments

“…In total, we identified 2,128 proteins from all biological replicates and cell lines. To identify proteins expressed uniquely in one secretome or another, we classified proteins as present in a secretome, provided label-free quantitation intensity values were greater than zero in at least two biological replicates, and we classified proteins as not expressed in a secretome if the intensity value was equal to zero across all three replicates, which are the criteria we have used previously (68). To quantify differential abundance of proteins between samples, we filtered the dataset to include only those proteins identified in at least two replicates in all cancer cell secretomes analysed, and any missing values were imputed in the Perseus software package (69), using the normal distribution with a width set to 0.3 and a down-shift of 1.8.…”