Comparative Functional Analysis of the Caenorhabditis elegans and Drosophila melanogaster Proteomes

Abstract

The nematode Caenorhabditis elegans is a popular model system in genetics, not least because a majority of human disease genes are conserved in C. elegans. To generate a comprehensive inventory of its expressed proteome, we performed extensive shotgun proteomics and identified more than half of all predicted C. elegans proteins. This allowed us to confirm and extend genome annotations, characterize the role of operons in C. elegans, and semiquantitatively infer abundance levels for thousands of proteins. Furthermore, for the first time to our knowledge, we were able to compare two animal proteomes (C. elegans and Drosophila melanogaster). We found that the abundances of orthologous proteins in metazoans correlate remarkably well, better than protein abundance versus transcript abundance within each organism or transcript abundances across organisms; this suggests that changes in transcript abundance may have been partially offset during evolution by opposing changes in protein abundance.

Predicted Effector Molecules in the Salivary Secretome of the Pea Aphid (Acyrthosiphon pisum): A Dual Transcriptomic/Proteomic Approach

“…44 Comparisons of the transcriptomes and proteomes of Caenorhabditis elegans and D. melanogaster have demonstrated that a higher degree of correlation exists between the proteomes of these distantly related organisms than for the transcriptome and proteome of the same organism. 45 Thus variation between Journal of Proteome Research ARTICLE the expressed transcript and the translated protein will most likely be the norm rather than the exception. However the aim of our study was to report an initial catalogue of putative secreted salivary gland proteins based on transcriptomic and proteomic resources that are currently available.…”

Inference and Validation of Protein Identifications

“…Many approaches start off from a static list of peptide spectrum matches obtained from a database search (21)(22)(23)(24)(25)(26). Probabilistic approaches revisit the peptide spectrum matches and rescore these based on presence or absence of sibling matches pointing to the same protein (27)(28)(29)(30).…”

“…Each approach addresses this issue by defining different notions of a protein identification. A first class of protein inference approaches maps peptide spectrum matches back to a set of ambiguous protein entries that are either defined by a priori grouping protein isoforms or reporting one representative variant for each set of isoforms (21)(22)(23)(24)(25). This a priori grouping effectively disambiguates the protein database and therefore allows for unambiguously mapping peptide spectrum matches to the respective groups.…”

“…Successful deep sequencing projects for various model organisms have achieved substantial proteome coverage by resorting to well curated protein databases featuring low redundancy (22)(23)(24)57). These studies cover around 50% of the respective sequence databases, indicating a reasonable tradeoff between constraining the size of the protein database while retaining sufficient diversity for comprehensive discov-…”

Proteomic identification of germline proteins inCaenorhabditis elegans

“…The lack of significant overlap between our dataset and that of Reinke et al likely reflects in part fundamental differences between the germline proteome and the germline transcriptome; RNA expression levels do not always correlate well with protein abundance. 11,24 However, the extensive and significant overlap between our data set and the germline SAGE dataset argues against this as sole explanation. Instead, we suggest that the lack of overlap between out data set and that of Reinke et al likely also reflects differences in sensitivity-our analysis was specifically targeted toward abundant proteins, while microarray analysis can evaluate expression differences for low-abundance transcripts as well (as discussed in ref.…”