Predicting bacterial promoter function and evolution from random sequences.

Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10-20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.

Complex chromosomal neighborhood effects determine the adaptive potential of a gene under selection.

How the organization of genes on a chromosome shapes adaptation is essential for understanding evolutionary paths. Here, we investigate how adaptation to rapidly increasing levels of antibiotic depends on the chromosomal neighborhood of a drug-resistance gene inserted at different positions of the Escherichia coli chromosome. Using a dual-fluorescence reporter that allows us to distinguish gene amplifications from other up-mutations, we track in real-time adaptive changes in expression of the drug-resistance gene. We find that the relative contribution of several mutation types differs systematically between loci due to properties of neighboring genes: essentiality, expression, orientation, termination, and presence of duplicates. These properties determine rate and fitness effects of gene amplification, deletions, and mutations compromising transcriptional termination. Thus, the adaptive potential of a gene under selection is a system-property with a complex genetic basis that is specific for each chromosomal locus, and it can be inferred from detailed functional and genomic data.

Cardiac glycosides induce cell death in human cells by inhibiting general protein synthesis.

BACKGROUND: Cardiac glycosides are Na(+)/K(+)-pump inhibitors widely used to treat heart failure. They are also highly cytotoxic, and studies have suggested specific anti-tumor activity leading to current clinical trials in cancer patients. However, a definitive demonstration of this putative anti-cancer activity and the underlying molecular mechanism has remained elusive. METHODOLOGY/PRINCIPAL FINDINGS: Using an unbiased transcriptomics approach, we found that cardiac glycosides inhibit general protein synthesis. Protein synthesis inhibition and cytotoxicity were not specific for cancer cells as they were observed in both primary and cancer cell lines. These effects were dependent on the Na(+)/K(+)-pump as they were rescued by expression of a cardiac glycoside-resistant Na(+)/K(+)-pump. Unlike human cells, rodent cells are largely resistant to cardiac glycosides in vitro and mice were found to tolerate extremely high levels. CONCLUSIONS/SIGNIFICANCE: The physiological difference between human and mouse explains the previously observed sensitivity of human cancer cells in mouse xenograft experiments. Thus, published mouse xenograft models used to support anti-tumor activity for these drugs require reevaluation. Our finding that cardiac glycosides inhibit protein synthesis provides a mechanism for the cytotoxicity of CGs and raises concerns about ongoing clinical trials to test CGs as anti-cancer agents in humans.