Functional unit of the RNA polymerase II C-terminal domain lies within heptapeptide pairs

Unlike all other RNA polymerases, the largest subunit (RPB1) of eukaryotic DNA-dependent RNA polymerase II (RNAP II) has a C-terminal domain (CTD) comprising tandemly repeated heptapeptides with the consensus sequence Y-S-P-T-S-P-S. The tandem structure, heptad consensus, and most key functions of the CTD are conserved between yeast and mammals. In fact, all metazoans, fungi, and green plants examined to date, as well as the nearest protistan relatives of these multicellular groups, contain a tandemly repeated CTD. In contrast, the RNAP II largest subunits from many other eukaryotic organisms have a highly degenerate C terminus or show no semblance of the CTD whatsoever. The reasons for intense stabilizing selection on CTD structure in certain eukaryotes, and its apparent absence in others, are unknown. Here we demonstrate, through in vivo genetic complementation, that the essential functional unit of the yeast CTD is contained within pairs of heptapeptides. Insertion of a single alanine residue between diheptads has little phenotypic effect, while increasing the distance between diheptads produces a mostly quantitative effect on yeast cell growth. We further explore structural constraints on the CTD within an evolutionary context and propose selective mechanisms that could maintain a global tandem structure across hundreds of millions of years of eukaryotic evolution.     

A gene's ability to buffer variation is predicted by its fitness contribution and genetic interactions

Background

Many single-gene knockouts result in increased phenotypic (e.g., morphological) variability among the mutant''s offspring. This has been interpreted as an intrinsic ability of genes to buffer genetic and environmental variation. A phenotypic capacitor is a gene that appears to mask phenotypic variation: when knocked out, the offspring shows more variability than the wild type. Theory predicts that this phenotypic potential should be correlated with a gene''s knockout fitness and its number of negative genetic interactions. Based on experimentally measured phenotypic capacity, it was suggested that knockout fitness was unimportant, but that phenotypic capacitors tend to be hubs in genetic and physical interaction networks.

Methodology/Principal Findings

We re-analyse the available experimental data in a combined model, which includes knockout fitness and network parameters as well as expression level and protein length as predictors of phenotypic potential. Contrary to previous conclusions, we find that the strongest predictor is in fact haploid knockout fitness (responsible for 9% of the variation in phenotypic potential), with an additional contribution from the genetic interaction network (5%); once these two factors are taken into account, protein-protein interactions do not make any additional contribution to the variation in phenotypic potential.

Conclusions/Significance

We conclude that phenotypic potential is not a mysterious “emergent” property of cellular networks. Instead, it is very simply determined by the overall fitness reduction of the organism (which in its compromised state can no longer compensate for multiple factors that contribute to phenotypic variation), and by the number (and presumably nature) of genetic interactions of the knocked-out gene. In this light, Hsp90, the prototypical phenotypic capacitor, may not be representative: typical phenotypic capacitors are not direct “buffers” of variation, but are simply genes encoding central cellular functions.     

Suppression Analysis Reveals a Functional Difference between the Serines in Positions Two and Five in the Consensus Sequence of the C-Terminal Domain of Yeast RNA Polymerase II

The largest subunit of RNA polymerase II contains a repetitive C-terminal domain (CTD) consisting of tandem repeats of the consensus sequence Tyr(1)Ser(2)Pro(3)Thr(4) Ser(5)Pro(6) Ser(7). Substitution of nonphosphorylatable amino acids at positions two or five of the Saccharomyces cerevisiae CTD is lethal. We developed a selection ssytem for isolating suppressors of this lethal phenotype and cloned a gene, SCA1 (suppressor of CTD alanine), which complements recessive suppressors of lethal multiple-substitution mutations. A partial deletion of SCA1 (sca1Δ::hisG) suppresses alanine or glutamate substitutions at position two of the consensus CTD sequence, and a lethal CTD truncation mutation, but SCA1 deletion does not suppress alanine or glutamate substitutions at position five. SCA1 is identical to SRB9, a suppressor of a cold-sensitive CTD truncation mutation. Strains carrying dominant SRB mutations have the same suppression properties as a sca1Δ::hisG strain. These results reveal a functional difference between positions two and five of the consensus CTD heptapeptide repeat. The ability of SCA1 and SRB mutant alleles to suppress CTD truncation mutations suggest that substitutions at position two, but not at position five, cause a defect in RNA polymerase II function similar to that introduced by CTD truncation.     

Histone Deacetylases and Phosphorylated Polymerase II C-Terminal Domain Recruit Spt6 for Cotranscriptional Histone Reassembly

Spt6 is a multifunctional histone chaperone involved in the maintenance of chromatin structure during elongation by RNA polymerase II (Pol II). Spt6 has a tandem SH2 (tSH2) domain within its C terminus that recognizes Pol II C-terminal domain (CTD) peptides phosphorylated on Ser2, Ser5, or Try1 in vitro. Deleting the tSH2 domain, however, only has a partial effect on Spt6 occupancy in vivo, suggesting that more complex mechanisms are involved in the Spt6 recruitment. Our results show that the Ser2 kinases Bur1 and Ctk1, but not the Ser5 kinase Kin28, cooperate in recruiting Spt6, genome-wide. Interestingly, the Ser2 kinases promote the association of Spt6 in early transcribed regions and not toward the 3′ ends of genes, where phosphorylated Ser2 reaches its maximum level. In addition, our results uncover an unexpected role for histone deacetylases (Rpd3 and Hos2) in promoting Spt6 interaction with elongating Pol II. Finally, our data suggest that phosphorylation of the Pol II CTD on Tyr1 promotes the association of Spt6 with the 3′ ends of transcribed genes, independently of Ser2 phosphorylation. Collectively, our results show that a complex network of interactions, involving the Spt6 tSH2 domain, CTD phosphorylation, and histone deacetylases, coordinate the recruitment of Spt6 to transcribed genes in vivo.     

Genetic modules and networks for behavior: lessons from Drosophila

Behaviors are quantitative traits determined through actions of multiple genes and subject to genome-environment interactions. Early studies concentrated on analyzing the effects of single genes on behaviors, often generating views of simplified linear genetic pathways. The genome era has generated a profound paradigm shift enabling us to identify all the genes that contribute to expression of a behavioral phenotype, to investigate how they are organized as functional ensembles and to begin to identify polymorphisms that contribute to phenotypic variation and are targets for natural selection. Recent studies show that the genetic architecture of behavior is determined by dynamic and plastic modular networks of pleiotropic genes and that the behavioral phenotype manifests itself as an emergent property of such networks. Such networks are exquisitely sensitive to genetic background and sex effects. This review describes how Drosophila can serve as a model for uncovering fundamental principles of the genetic architecture of behavior.     

The balance of weak and strong interactions in genetic networks

Genetic interactions are being quantitatively characterized in a comprehensive way in several model organisms. These data are then globally represented in terms of genetic networks. How are interaction strengths distributed in these networks? And what type of functional organization of the underlying genomic systems is revealed by such distribution patterns? Here, I found that weak interactions are important for the structure of genetic buffering between signaling pathways in Caenorhabditis elegans, and that the strength of the association between two genes correlates with the number of common interactors they exhibit. I also determined that this network includes genetic cascades balancing weak and strong links, and that its hubs act as particularly strong genetic modifiers; both patterns also identified in Saccharomyces cerevisae networks. In yeast, I further showed a relation, although weak, between interaction strengths and some phenotypic/evolutionary features of the corresponding target genes. Overall, this work demonstrates a non-random organization of interaction strengths in genetic networks, a feature common to other complex networks, and that could reflect in this context how genetic variation is eventually influencing the phenotype.     

Genome-wide prediction of functional gene-gene interactions inferred from patterns of genetic differentiation in mice and men

The human genome encodes a limited number of genes yet contributes to individual differences in a vast array of heritable traits. A possible explanation for the capacity our genome to generate this virtually unlimited range of phenotypic variation in complex traits is to assume functional interactions between genes. Therefore we searched two mammalian genomes to identify potential epistatic interactions by looking for co-adapted genes marked by excess two-locus genetic differentiation between populations/lineages using publicly available SNP genotype data. The practical motivation for this effort is to reduce the number of pair-wise tests that need to be performed in genome-wide association studies aimed at detecting GxG interactions, by focusing on pairs predicted to be more likely to jointly affect variation in complex traits. Hence, this approach generates a list of candidate interactions that can be empirically tested. In both the mouse and human data we observed two-locus genetic differentiation in excess of what can be expected from chance alone based on simulations. In an attempt to validate our hypothesis that pairs of genes showing excess genetic divergence represent potential functional interactions, we selected a small set of gene combinations postulated to be interacting based on our analyses and looked for a combined effect of the selected genes on variation in complex traits in both mice and man. In both cases the individual effect of the genes were not significant, instead we observed marginally significant interaction effects. These results show that genome wide searches for gene-gene interactions based on population genetic data are feasible and can generate interesting candidate gene pairs to be further tested for their contribution to phenotypic variation in complex traits.