Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.

Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins (4,648) showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems.

Genetic and Epigenetic Inheritance at Telomeres.

Transgenerational inheritance can occur at telomeres in distinct contexts. Deficiency for telomerase or telomere-binding proteins in germ cells can result in shortened or lengthened chromosome termini that are transmitted to progeny. In human families, altered telomere lengths can result in stem cell dysfunction or tumor development. Genetic inheritance of altered telomeres as well as mutations that alter telomeres can result in progressive telomere length changes over multiple generations. Telomeres of yeast can modulate the epigenetic state of subtelomeric genes in a manner that is mitotically heritable, and the effects of telomeres on subtelomeric gene expression may be relevant to senescence or other human adult-onset disorders. Recently, two novel epigenetic states were shown to occur at C. elegans telomeres, where very low or high levels of telomeric protein foci can be inherited for multiple generations through a process that is regulated by histone methylation.Together, these observations illustrate that information relevant to telomere biology can be inherited via genetic and epigenetic mechanisms, although the broad impact of epigenetic inheritance to human biology remains unclear.