Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy

Evidence that conserved essential genes are enriched for pro-longevity factors

At the cellular level, many aspects of aging are conserved across species. This has been demonstrated by numerous studies in simple model organisms like Saccharomyces cerevisiae, Caenorhabdits elegans, and Drosophila melanogaster. Because most genetic screens examine loss of function mutations or decreased expression of genes through reverse genetics, essential genes have often been overlooked as potential modulators of the aging process. By taking the approach of increasing the expression level of a subset of conserved essential genes, we found that 21% of these genes resulted in increased replicative lifespan in S. cerevisiae. This is greater than the ~ 3.5% of genes found to affect lifespan upon deletion, suggesting that activation of essential genes may have a relatively disproportionate effect on increasing lifespan. The results of our experiments demonstrate that essential gene overexpression is a rich, relatively unexplored means of increasing eukaryotic lifespan.

Yeast genetic interaction screens in the age of CRISPR/Cas

The ease of performing both forward and reverse genetics in Saccharomyces cerevisiae, along with its stable haploid state and short generation times, has made this budding yeast the consummate model eukaryote for genetics. The major advantage of using budding yeast for reverse genetics is this organism's highly efficient homology-directed repair, allowing for precise genome editing simply by introducing DNA with homology to the chromosomal target. Although plasmid- and PCR-based genome editing tools are quite efficient, they depend on rare spontaneous DNA breaks near the target sequence. Consequently, they can generate only one genomic edit at a time, and the edit must be associated with a selectable marker. However, CRISPR/Cas technology is efficient enough to permit markerless and multiplexed edits in a single step. These features have made CRISPR/Cas popular for yeast strain engineering in synthetic biology and metabolic engineering applications, but it has not been widely employed for genetic screens. In this review, we critically examine different methods to generate multi-mutant strains in systematic genetic interaction screens and discuss the potential of CRISPR/Cas to supplement or improve on these methods.

Achieving a Predictive Understanding of Antimicrobial Stress Physiology through Systems Biology

The dramatic spread and diversity of antibiotic-resistant pathogens has significantly reduced the efficacy of essentially all antibiotic classes, bringing us ever closer to a postantibiotic era. Exacerbating this issue, our understanding of the multiscale physiological impact of antimicrobial challenge on bacterial pathogens remains incomplete. Concerns over resistance and the need for new antibiotics have motivated the collection of omics measurements to provide systems-level insights into antimicrobial stress responses for nearly 20 years. Although technological advances have markedly improved the types and resolution of such measurements, continued development of mathematical frameworks aimed at providing a predictive understanding of complex antimicrobial-associated phenotypes is critical to maximize the utility of multiscale data. Here we highlight recent efforts utilizing systems biology to enhance our knowledge of antimicrobial stress physiology. We provide a brief historical perspective of antibiotic-focused omics measurements, highlight new measurement discoveries and trends, discuss examples and opportunities for integrating measurements with mathematical models, and describe future challenges for the field.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features

We propose an integrated machine learning process to predict gene essentiality in Escherichia coli K-12 MG1655 metabolism that outperforms known methods.