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Genomic robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of genomic robustness in budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high cooccurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggests a surprising degree of functional plasticity of macromolecular complexes, and the existence of numerous degenerate pathways for circumventing the effects of potentially lethal mutations. These results imply that organisms and cancer are likely able to exploit the genomic robustness properties, due the persistence of cryptic gene and pathway functions, to generate variation and adapt to selective pressures. © 2016 The Author(s).

Chromosome stability models are usually qualitative models derived from molecular-genetic mechanisms for DNA repair, DNA synthesis, and cell division. While qualitative models are informative, they are also challenging to reformulate as precise quantitative models. In this report we explore how (A) laboratory experiments, (B) quantitative simulation, and (C) seriation algorithms can inform models of chromosome stability. Laboratory experiments were used to identify 19 genes that when over-expressed cause chromosome instability in the yeast Saccharomyces cerevisiae To better understand the molecular mechanisms by which these genes act, we explored their genetic interactions with 18 deletion mutations known to cause chromosome instability. Quantitative simulations based on a mathematical model of the cell cycle were used to predict the consequences of several genetic interactions. These simulations lead us to suspect that the chromosome instability genes cause cell-cycle perturbations. Cell-cycle involvement was confirmed using a seriation algorithm, which was used to analyze the genetic interaction matrix to reveal an underlying cyclical pattern. The seriation algorithm searched over 10(14) possible arrangements of rows and columns to find one optimal arrangement, which correctly reflects events during cell cycle phases. To conclude, we illustrate how the molecular mechanisms behind these cell cycle events are consistent with established molecular interaction maps.

A healthy cell must maintain chromosome integrity during each phase of the cell cycle. Yet during the cell cycle, defects in DNA repair, DNA synthesis, and chromosome transmission can cause chromosome instability. Here, we build a cyclical model for chromosome maintenance by assaying the genetic interactions of pairs of genes that each normally functions to maintain chromosomes. First, we confirm that 19 genes affect chromosome maintenance by overexpressing the respective genes using an assay for chromosome instability. We then introduce plasmids containing these 19 genes into 18 deletion mutant strains each of which is known to cause chromosome maintenance defect. By conditionally overexpressing each of the 19 genes in each of the 18 deletion strains, we observe 34 cases of synthetic dosage lethality, 58 of synthetic dosage sickness, and 26 of suppression (out of 349 combinations tested). Ordinarily, given a large matrix of genetic interactions, it is computationally intractable (and NP hard) to compute all permutations of the arrangements of the rows and columns, which is necessary for finding an optimum arragement of entries for a given pattern of functional significance. Here, using our small yet dense matrix we rearrange the matrix into an optimal cycle. By optimally reordering the matrix representation of these genetic interactions using integer linear programming, we find that the optimal order of the functions of the genes approximates the temporal order of processes during the yeast cell cycle. To further validate this theoretical approach, we computed in silico genetic interactions derived from a predetermined cyclical model and confirmed that the predicted cycle was consistent. These results suggest that matrices from other genetic experiments could be computationally rearranged using this method to reveal an underlying cycle that is biological meaningful.

Mutational robustness is the extent to which an organism has evolved to withstand the effects of deleterious mutations. We explored the extent of mutational robustness in the budding yeast by genome wide dosage suppressor analysis of 53 conditional lethal mutations in cell division cycle and RNA synthesis related genes, revealing 660 suppressor interactions of which 642 are novel. This collection has several distinctive features, including high co-occurrence of mutant-suppressor pairs within protein modules, highly correlated functions between the pairs, and higher diversity of functions among the co-suppressors than previously observed. Dosage suppression of essential genes encoding RNA polymerase subunits and chromosome cohesion complex suggest a surprising degree of functional plasticity of macromolecular complexes and the existence od degenerate pathways for circumventing potentially lethal mutations. The utility of dosage-suppressor networks is illustrated by the discovery of a novel connection between chromosome cohesion-condensation pathways involving homologous recombination, and Huntington's disease., 42 pages, 2 tables, 6 Figures. Supplementary Tables S1-S12 and Supplementary Figures S1-S8 at http://dx.doi.org/10.6084/m9.figshare.844761