Electrostatic mis-interactions cause overexpression toxicity of proteins in E. coli

Gene therapy and gene correction: targets, progress, and challenges for treating human diseases

The field of gene therapy has made significant strides over the last several decades toward the treatment of previously untreatable genetic disease. Gene therapy techniques have been aimed at mitigating disease features of recessive and dominant disorders, as well as several cancers and other diseases. While there have been numerous disease targets of gene therapy trials, only four therapies have reached FDA and/or EMA approval for clinical use. Gene correction using CRISPR-Cas9 is an extension of gene therapy that has received considerable attention in recent years and boasts many possible uses beyond classical gene therapy approaches. While there is significant therapeutic potential using gene therapy and gene correction strategies, a number of hurdles remain to be overcome before they become more common in clinical use, particularly with regards to safety and efficacy. As research progresses in this exciting field, it is likely that these therapies will become first-line treatments and will have tremendous positive impacts on the lives of patients with genetic disorders.

Transient protein-protein interactions perturb E. coli metabolome and cause gene dosage toxicity

Gene dosage toxicity (GDT) is an important factor that determines optimal levels of protein abundances, yet its molecular underpinnings remain unknown. Here, we demonstrate that overexpression of DHFR in E. coli causes a toxic metabolic imbalance triggered by interactions with several functionally related enzymes. Though deleterious in the overexpression regime, surprisingly, these interactions are beneficial at physiological concentrations, implying their functional significance in vivo. Moreover, we found that overexpression of orthologous DHFR proteins had minimal effect on all levels of cellular organization - molecular, systems, and phenotypic, in sharp contrast to E. coli DHFR. Dramatic difference of GDT between 'E. coli's self' and 'foreign' proteins suggests the crucial role of evolutionary selection in shaping protein-protein interaction (PPI) networks at the whole proteome level. This study shows how protein overexpression perturbs a dynamic metabolon of weak yet potentially functional PPI, with consequences for the metabolic state of cells and their fitness.

Evaluating the fitness cost of protein expression in Saccharomyces cerevisiae

Protein metabolism is one of the most costly processes in the cell and is therefore expected to be under the effective control of natural selection. We stimulated yeast strains to overexpress each single gene product to approximately 1% of the total protein content. Consistent with previous reports, we found that excessive expression of proteins containing disordered or membrane-protruding regions resulted in an especially high fitness cost. We estimated these costs to be nearly twice as high as for other proteins. There was a ten-fold difference in cost if, instead of entire proteins, only the disordered or membrane-embedded regions were compared with other segments. Although the cost of processing bulk protein was measurable, it could not be explained by several tested protein features, including those linked to translational efficiency or intensity of physical interactions after maturation. It most likely included a number of individually indiscernible effects arising during protein synthesis, maturation, maintenance, (mal)functioning, and disposal. When scaled to the levels normally achieved by proteins in the cell, the fitness cost of dealing with one amino acid in a standard protein appears to be generally very low. Many single amino acid additions or deletions are likely to be neutral even if the effective population size is as large as that of the budding yeast. This should also apply to substitutions. Selection is much more likely to operate if point mutations affect protein structure by, for example, extending or creating stretches that tend to unfold or interact improperly with membranes.

How do eubacterial organisms manage aggregation-prone proteome?

Eubacterial genomes vary considerably in their nucleotide composition. The percentage of genetic material constituted by guanosine and cytosine (GC) nucleotides ranges from 20% to 70%.  It has been posited that GC-poor organisms are more dependent on protein folding machinery. Previous studies have ascribed this to the accumulation of mildly deleterious mutations in these organisms due to population bottlenecks. This phenomenon has been supported by protein folding simulations, which showed that proteins encoded by GC-poor organisms are more prone to aggregation than proteins encoded by GC-rich organisms. To test this proposition using a genome-wide approach, we classified different eubacterial proteomes in terms of their aggregation propensity and chaperone-dependence using multiple machine learning models. In contrast to the expected decrease in protein aggregation with an increase in GC richness, we found that the aggregation propensity of proteomes increases with GC content. A similar and even more significant correlation was obtained with the GroEL-dependence of proteomes: GC-poor proteomes have evolved to be less dependent on GroEL than GC-rich proteomes. We thus propose that a decrease in eubacterial GC content may have been selected in organisms facing proteostasis problems.

Coupling between noise and plasticity in E. coli

Expression levels of genes vary not only between different environmental conditions (“plasticity”) but also between genetically identical cells in constant environment (“noise”). Intriguingly, these two measures of gene expression variability correlate positively with each other in yeast. This coupling was found to be particularly strong for genes with specific promoter architecture (TATA box and high nucleosome occupancy) but weak for genes in which high noise may be detrimental (e.g., essential genes), suggesting that noise–plasticity coupling is an evolvable trait in yeast and may constrain evolution of gene expression and promoter usage. Recently, similar genome-wide data on noise and plasticity have become available for Escherichia coli, providing the opportunity to study noise–plasticity correlation and its mechanism in a prokaryote, which follows a fundamentally different mode of transcription regulation than a eukaryote such as yeast. Using these data, I found significant positive correlation between noise and plasticity in E. coli. Furthermore, this coupling was highly influenced by the following: level of expression; essentiality and dosage sensitivity of genes; regulation by specific nucleoid-associated proteins, transcription factors, and sigma factors; and involvement in stress response. Many of these features are analogous to those found to influence noise–plasticity coupling in yeast. These results not only show the generality of noise–plasticity coupling across phylogenetically distant organisms but also suggest that its mechanism may be similar.