The evolution of metabolic enzymes in Plasmodium and trypanosomatids as compared to Saccharomyces and Schizosaccharomyces

Transcriptional noise adjusted for expression levels reveals genes with high transcriptional noise that are highly expressed, functionally related, and co-regulated in yeast

Understanding the relationship between variability in single-cell and non-single-cell gene expression studies will aid in understanding the role of and mechanisms that lead to variability in biological systems. Studies on the variation of gene expression levels in yeast normally focus on single cells and use the coefficient of variance (CV) as a measure of noise. The CV is typically negatively correlated with gene expression levels, so most of the studies using yeast find that genes with high transcriptional noise are lowly expressed. We find adjusting noise for expression levels using linear/natural log polynomial, and local fits and analyzing many non-single-cell RNA-seq sets identifies genes with high median transcriptional noise that are different than those that have high median CVs. Interestingly, these genes are heavily regulated by transcription factors that are related to variability and stochastic processes based on single-cell studies, including Msn2p, Msn4p, Hsf1p, and Crz1p but are not associated with genes with high median CVs based on non-single-cell gene expression data. In addition, adjusting noise for expression levels in a single-cell RNA-seq data set adds value by finding genes that have noisy gene expression levels and their associated transcription factors that are not found to be associated with genes with high CVs in the single-cell expression data or a comparable non-single-cell gene expression data. Lastly, S. cerevisiae genes with noisy expression tend to have orthologs with noisy gene expression in C. albicans, indicating transcriptional noise is evolutionarily conserved.

The Influence of Codon Usage, Protein Abundance, and Protein Stability on Protein Evolution Vary by Evolutionary Distance and the Type of Protein

In general, the evolutionary rate of proteins is not primarily related to protein and amino acid functions, and factors such as protein abundance, codon usage, and the protein's T_M are more important. To better understand the factors that affect protein evolution, E. coli MG1655 orthologs were compared to those in closely related bacteria and to more distantly related prokaryotes, eukaryotes, and archaea. Also, the evolution of different types of proteins was studied. The analyses indicate that the amino acid conservation of enzymes that do not use macromolecules (e.g. DNA, RNA, and proteins) as substrates and that carry out metabolic processes involving small molecules (i.e. small molecule enzymes) is different than other enzymes. For example, the small molecule enzymes have a lower percent identity than other enzymes when sequences from closely related bacteria are compared. Analyses indicate the lower percent identity is not a result of the amino acid or codon usage of the small molecule enzymes. The small molecule enzymes also don't have a significantly lower protein abundance indicating that is also not likely an important factor driving differences in amino acid conservation. Analyses indicate different methods to measure the T_M of proteins have different relationships between amino acid conservation over different evolutionary distances. In totality, the results demonstrate that the relationship between the factors thought to affect protein evolution (protein abundance, codon usage, and proteins T_Ms) and protein evolution are complex and depend on the factor, the organisms, and the type of proteins being analyzed.