Crosses performed and rust accession used to test for resistance/susceptibility.

<p>(S) designates susceptible and (R) designates resistant.</p

Cross results: tests for deviation from single locus inheritance.

<p>No. R S₂: number of S₂ that were resistant. No. S S₂: number of S₂ that were susceptible. Ratio R/S: ratio of resistant to susceptible individuals. LRχ²: value of likelihood ratio chi-square statistic. None were significant. Critical value for 1 d.f. is 3.84 at P<0.05. Heterogeneity: test for heterogeneity among Parental Pairs for segregation ratios. Pooled: test for deviation from 3∶1 ratio after pooling S₂'s from all Parental Pairs.</p

locations of host populations and rust accessions used in crossing experiments.

<p>locations of host populations and rust accessions used in crossing experiments.</p

Interaction effect in fitted data, demonstrating that the best-fit model accounts for the interaction.

<p>Interaction effect in fitted data, demonstrating that the best-fit model accounts for the interaction.</p

Prediction plot for best-fit model, a regression of observed on fitted TSWV incidence, R² = 0.899.

<p>Prediction plot for best-fit model, a regression of observed on fitted TSWV incidence, R² = 0.899.</p

Transmission event timeline showing the occurrence of different host groups (summer and winter annual weeds, and crop tobacco) in time, and an interpretation of how and when relevant weather parameters influence the disease system.

<p>Note crops in TSWV susceptible crop hosts in the tobacco production regions are harvested and removed from the agroecosystem before winter annuals germinate.</p

Interaction effect in observed data, showing change in influence of prior-year thrips variable across range of March precipitation, and to lesser extent, <i>vice versa</i>.

<p>Interaction effect in observed data, showing change in influence of prior-year thrips variable across range of March precipitation, and to lesser extent, <i>vice versa</i>.</p

Panel of diagnostic plots for best-fit model, showing conditional Studentized residuals.

<p>Panel of diagnostic plots for best-fit model, showing conditional Studentized residuals.</p

Mutation rate of <i>pol2-4 msh6Δ</i> mother yeast cells at single cell resolution.

<p>(A) Polymerase errors (orange, green, and blue boxes) arising in maternal double-stranded DNA (dsDNA) as mismatches become mutations during S-phase DNA replication (see rectangle) and segregate to the mother (M) or daughter (D) cells. Subscript numbers following M or D indicate the division number that produced the cell (e.g. M₁ is the mother cell after one division). Red arrows indicate only one of several segregation scenarios. Single cell mutation rates (<i>M</i>_<i>1</i><i>μ</i>, <i>M</i>_<i>2</i><i>μ</i>, <i>M</i>_<i>3</i><i>μ</i>) are defined as the number of new mutations fixed in the maternal lineage at each cell division divided by the total number of nucleotides sequenced in all members of a lineage. (B) Genomic distribution of the 237 mutations observed in individual cell divisions (blue lines) among the 16 yeast chromosomes (gray lines). Red lines, centromeres. (C) Mutation spectra of <i>pol2-4 msh6Δ</i> cells from whole genome sequencing (blue) compared to published spectra (red).</p

Volatility of Mutator Phenotypes at Single Cell Resolution

<div><p>Mutator phenotypes accelerate the evolutionary process of neoplastic transformation. Historically, the measurement of mutation rates has relied on scoring the occurrence of rare mutations in target genes in large populations of cells. Averaging mutation rates over large cell populations assumes that new mutations arise at a constant rate during each cell division. If the mutation rate is not constant, an expanding mutator population may contain subclones with widely divergent rates of evolution. Here, we report mutation rate measurements of individual cell divisions of mutator yeast deficient in DNA polymerase ε proofreading and base-base mismatch repair. Our data are best fit by a model in which cells can assume one of two distinct mutator states, with mutation rates that differ by an order of magnitude. In error-prone cell divisions, mutations occurred on the same chromosome more frequently than expected by chance, often in DNA with similar predicted replication timing, consistent with a spatiotemporal dimension to the hypermutator state. Mapping of mutations onto predicted replicons revealed that mutations were enriched in the first half of the replicon as well as near termination zones. Taken together, our findings show that individual genome replication events exhibit an unexpected volatility that may deepen our understanding of the evolution of mutator-driven malignancies.</p></div