Dr. Levi Waldron, professor at the CUNY Graduate School of Public Health and Health Policy and international colleagues recently published an article on the control of gene expression. The work was published in the journal PLoS Computational Biology.
[Photo: Dr. Levi Waldron]
Gene expression is a dynamic program by which the information stored in the genome is rendered functional by production and degradation of two types of macromolecules, RNAs and proteins. Messenger RNAs (mRNAs) are templates for proteins; therefore there is an expectation for correspondence between the quantities of mRNAs and proteins. Genome-wide studies indicate a marked discrepancy between them, when considering their steady-state levels or their variations across different conditions. The research team employed linear regression approaches with paired mRNA/protein datasets to develop a model predicting the protein level of a gene from both the mRNA level and the protein levels of RNA binding proteins inferred to bind the mRNA untranslated regions. The results of their analyses restricted the utility of RNA binding proteins to improve accuracy of predicted protein abundance to a small fraction of the total modelled genes, and identified a novel association of the improvement induced by RNA binding proteins with the presence of upstream translation sites. This finding suggests a new avenue of experimental studies aimed at exploring the hypothesis that RNA binding proteins could influence protein abundance by changing the preference for certain translation initiation sites.