Project description:Drosophila melanogaster is a well-studied genetic model organism with several large-scale transcriptome resources. Here we investigate 7,952 proteins during the fly life cycle from embryo to adult and also provide a high-resolution temporal time course proteome of 5,458 proteins during embryogenesis. We use our large scale data set to compare transcript/protein expression, uncovering examples of extreme differences between mRNA and protein abundance. In the embryogenesis proteome, the time delay in protein synthesis after transcript expression was determined. For some proteins, including the transcription factor lola, we monitor isoform specific expression levels during early fly development. Furthermore, we obtained firm evidence of 268 small proteins, which are hard to predict by bioinformatics. We observe peptides originating from non-coding regions of the genome and identified Cyp9f3psi as a protein-coding gene. As a powerful resource to the community, we additionally created an interactive web interface (http://www.butterlab.org) advancing the access to our data.
Project description:Drosophila melanogaster is a well-studied genetic model organism with several large-scale transcriptome resources. Here we investigate 7,952 proteins during the fly life cycle from embryo to adult and also provide a high-resolution temporal time course proteome of 5,458 proteins during embryogenesis. We use our large scale data set to compare transcript/protein expression, uncovering examples of extreme differences between mRNA and protein abundance. In the embryogenesis proteome, the time delay in protein synthesis after transcript expression was determined. For some proteins, including the transcription factor lola, we monitor isoform specific expression levels during early fly development. Furthermore, we obtained firm evidence of 268 small proteins, which are hard to predict by bioinformatics. We observe peptides originating from non-coding regions of the genome and identified Cyp9f3psi as a protein-coding gene. As a powerful resource to the community, we additionally created an interactive web interface (http://www.butterlab.org) advancing the access to our data.
Project description:Even though proteins are produced from mRNA, the correlation between mRNA levels and protein abundances is moderate in most studies, occasionally attributed to complex post-transcriptional regulation. To address this, we generated a paired transcriptome/proteome time course dataset with 14 time points during Drosophila embryogenesis. Despite a limited mRNA-protein correlation (ρ = 0.54), mathematical models describing protein translation and degradation explain 84% of protein time-courses based on the measured mRNA dynamics without assuming complex post-transcriptional regulation, and allow for classification of most proteins into four distinct regulatory scenarios. By performing an in-depth characterization of the putatively post-transcriptionally regulated genes, we postulated that the RNA-binding protein Hrb98DE is involved in post-transcriptional control of sugar metabolism in early embryogenesis and partially validated this hypothesis using Hrb98DE knockdown. In summary, we present a systems biology framework for the identification of post-transcriptional gene regulation for large-scale time-resolved transcriptome and proteome data.