Comparison of Individual Strain Analysis to Microarray Results

<div><p>(A) Representative growth curves of 16 individual strains grown in the presence of solvent alone (DMSO, red curve) and 62.5 μM mechlorethamine (black curve). Growth was monitored by measuring the optical density (OD₆₀₀) of cultures every 15 min for 30 h. The fitness of each strain was defined by the difference between the average doubling times in mechlorethamine and in DMSO (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0010024#s4" target="_blank">Materials and Methods</a>).</p><p>(B) Correlation between growth rates of individual strains and microarray-based fitness estimates. The ratio of growth rates of the 186 individual homozygous deletion strains (the top 233 ranked mechlorethamine-sensitive strains as determined by three replicate microarray experiments minus 47 strains which exhibited a slow-growth phenotype when individually cultured in the absence of mechlorethamine) over an average wild-type growth rate are plotted on the x-axis against the average fitness-defect scores from three pool experiments on the y-axis. The correlation (<i>R</i>² = 0.7462) is highly significant (<i>p</i> = 5.4e<i>−</i>57).</p></div

The Effect of Exposure Duration on the Genetic Requirements for Resistance to DNA-Damaging Agents

<div><p>(A) Pie charts showing relative percentage of sensitive genes categorized into either “DNA metabolism”, “unknown”, or “other”. All of the Gene Ontology [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0010024#pgen-0010024-b63" target="_blank">63</a>] slim annotations (<a href="ftp://ftp.yeastgenome.org/yeast/data_download/literature_curation/go_slim_mapping.tab." target="_blank">ftp://ftp.yeastgenome.org/yeast/data_download/literature_curation/go_slim_mapping.tab</a>. Accessed February 17, 2005) are combined into “other” except for those classified in the unknown or DNA-metabolism category. The relative distributions of the mechlorethamine experiments are shown as a function of both time and gene rank.</p><p>(B) BY4743 (wild-type) and the DNA-damage-checkpoint mutants, <i>rad9Δ</i> and <i>rad24Δ,</i> were grown in the presence of 500 μM cisplatin over the course of ten population doublings. Yeast cultures were maintained in an exponential phase of growth by robotic dilution of cultures after five doublings into fresh media containing cisplatin. The growth of each deletion strain (black curve) is compared to that of BY4743 (red curve) between 0–5 (left) and 6–10 (right) population doublings. The <i>rad9Δ</i> and <i>rad24Δ</i> deletion strains exhibit accelerated grow rates in the first five generations, but by ten generations begin to demonstrate slow growth as compared to wild-type.</p><p>(C) Viability assay of strains treated with cisplatin or DMSO for five generations. Indicated strains were removed from cultures after they reached an OD₆₀₀ of 2.0, and were diluted as shown. Strains were chosen based on the criteria that they did not show a growth defect at five generations but did show a growth defect at 20 generations. Dilutions were pinned onto YPD plates in a 5-fold concentration series. The wild-type parental diploid strain is compared to several diploid deletion strains that exhibited a decrease in viability: <i>ddc1Δ, rad24Δ, rad17Δ, mec3Δ,</i> and <i>rad9Δ.</i> In contrast, several of these strains showed little or no decrease in viability at five generations of growth: <i>swi4Δ, yme1Δ, rtf1Δ,</i> and <i>gcs1Δ</i>. This figure underscores the point that, despite the lack of an apparent growth defect in liquid culture, several deletion strains lose viability relatively rapidly when exposed to cisplatin.</p></div

The Relative Importance of Well-Characterized DDR Modules for Resistance to Different DNA-Damaging Agents

<p>Detailed examination of strains with mutations in DNA-damage-response genes. Each bar graph represents only strains that were found to be among the top 30 (or 250) most sensitive strains in that compound and are known to be members of a well-characterized DNA-damage-response pathway. The bars represent the median rank for genes in each of the gene groups listed in the visual key. The gene groups were defined in the following way: x-linking genes <i>(PSO2);</i> NER <i>(RAD2, RAD4, RAD10, RAD1,</i> and <i>RAD14);</i> PRR <i>(RAD6, RAD18,</i> and <i>RAD5);</i> error-prone TLS <i>(REV1, REV3,</i> and <i>REV7);</i> HRR <i>(RAD57, RAD55, RAD51, RAD52, RAD54,</i> and <i>RAD59);</i> stalled replication-fork repair <i>(MUS81</i> and <i>MMS4).</i> Those compounds that form ICLs are labeled with an asterisk.</p

Hierarchical Clustering of Genome-Wide Profiles Identifies Mechanistic Relationships Between Drugs and Functional Relationships Between Genes

<div><p>(A) Clustergram containing all strains significant in two or more array experiments. Raw fitness-defect values were hierarchically clustered using Spearman's rank correlation. Colored bars represent gene clusters of note, including NER (<i>RAD2, RAD4, RAD10, RAD14,</i> and <i>RAD1—</i>blue); error-prone TLS (<i>REV1</i> and <i>REV3—</i>red); PRR (<i>RAD6, RAD18,</i> and <i>RAD5—</i>yellow); homologous recombination (<i>RAD57, RAD51,</i> and <i>RAD54—</i>green); cell-cycle checkpoint control (<i>RAD9, RAD24, RAD17, DDC1,</i> and <i>MEC3—</i>orange); and a cluster shown in (B) (<i>SHU2, SHU1, CSM2, MPH1,</i> and <i>PSY3—</i>magenta).</p><p>(B) Zoom view showing one cluster containing the class I NER genes and a second cluster containing several uncharacterized DNA-repair genes. Four of these five genes <i>(SHU1, SHU2, CSM2,</i> and <i>PSY3)</i> are known to encode proteins that physically interact [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0010024#pgen-0010024-b65" target="_blank">65</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0010024#pgen-0010024-b77" target="_blank">77</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.0010024#pgen-0010024-b78" target="_blank">78</a>].</p><p>(C) Individual growth curves of single and double deletion strains with <i>MPH1</i> in 0.002% MMS. In all panels, the growth of wild-type (BY4741) is represented by the black curve and the growth of <i>mph1Δ</i> by the red curve. The growth of eight different deletion strains <i>(shu1Δ, shu2Δ, csm2Δ, psy3Δ, mag1Δ, mus81Δ, rad51Δ,</i> and <i>rad54Δ)</i> are shown in green, and double mutants, in which the <i>MPH1</i> deletion is added to each of the above, are shown in blue. Double mutants of <i>MPH1, MAG1,</i> and <i>MUS81</i> show additive or synergistic sensitivity to MMS, whereas double mutants of <i>MPH1,</i> with the four other genes in its cluster, show no additional sensitivity to MMS, suggesting that <i>MPH1</i> is epistatic with <i>SHU1, SHU2, CSM2,</i> and <i>PSY3</i>.</p></div

The Electron Transport Chain Decreases Expression with Age in Humans, Mice, and Flies

<p>Rows represent either human tissues or model organisms. Columns correspond to individual human genes and homologs to human genes defined by reciprocal best BLAST hits in other species. Scale represents the normalized slope of the change in log₂ expression level with age (<i>β_1j</i>). Data from different species were normalized by dividing the slope of expression with age by the standard deviation of all similar slopes in the dataset. Gray indicates genes were not present in that species. A navigable version of this figure showing identities of specific genes can be found at <a href="http://cmgm.stanford.edu/~kimlab/aging_muscle" target="_blank">http://cmgm.stanford.edu/~kimlab/aging_muscle</a>.</p

A Common Signature for Aging in Muscle, the Kidney, and the Brain

<p>Shown are expression data from sets of extracellular matrix genes, cell growth genes, complement activation genes, cytosolic ribosomal genes, chloride transport genes, and electron transport chain genes. Rows are human tissues (M, muscle; K, kidney; B, brain). Columns correspond to individual genes in each gene set. Scale represents the slope of the change in log₂ expression level with age <i>(β_1j).</i> Gray indicates genes were not present in the dataset. A navigable version showing identities of specific genes can be found at <a href="http://cmgm.stanford.edu/~kimlab/aging_muscle" target="_blank">http://cmgm.stanford.edu/~kimlab/aging_muscle</a>.</p

Gene Expression Predicts Physiology of Aging

<div><p>(A) Cross-section of histologically unremarkable deltoid muscle from a 48-y-old woman demonstrating relatively equivalent sizes of types I and II muscle fibers. Arrows denote fibers types as distinguished by enzyme histochemistry (cryosection, 200×, myosin ATPase at pH 9.4).</p><p>(B) Cross-section of deltoid muscle from an 88-y-old woman demonstrating selective atrophy of type II muscle fibers that stain darkly by ATPase enzyme histochemistry (cryosection, 200×, myosin ATPase at pH 9.4).</p><p>(C) Histograms showing a correlation between muscle physiology and gene expression for age-regulated genes. Top panel: for each of the 250 age-regulated genes, we calculated the partial correlation coefficients between the type II/type I muscle fiber diameter ratio and gene expression excluding age variation (<i>x</i>-axis). Bottom panel: same as top panel, except that correlation coefficients were calculated for all 31,948 genes. The squared partial correlation coefficient denotes the amount that changes in gene expression account for variance in type II/type I muscle fiber diameter ratios while excluding the effects of age.</p><p>(D) Histogram showing the likelihood of finding 92 genes with |<i>r</i>| > 0.2 from a set of random genes. We performed a Monte Carlo experiment by randomly selecting sets of 250 genes from the genome, and calculating how many genes in the set had |<i>r</i>| > 0.2 as in (C). The procedure was repeated 1,000 times and the histogram shows the number of genes from each random selection that have |<i>r</i>| > 0.2. The arrow shows the number of genes exceeding this threshold (92) from the set of 250 age-regulated genes (<i>p</i> < 0.001). We also determined the total number of genes in the genome with |<i>r</i>| > 0.2, and then showed that 92 genes from a set of 250 is significant (hypergeometric distribution; <i>p</i> < 1 × 10⁻⁴).</p></div

Age-Regulated Genes

<div><p>(A) Shown are expression levels for gene <i>CDO1</i>. White and black circles represent expression from cortex and medulla, respectively. The y-axis indicates log₂ (expression level), and the x-axis indicates age of patient (years). Dotted and solid lines indicate best fit slopes for the cortex and medulla values, respectively.</p> <p>(B) For every gene, we calculated a one-sided p̃ -value that its expression changes with age. Shown is a histogram representing all of the genes represented by the Affymetrix DNA chip. Genes that decrease with age have p̃ -values near zero, and genes that increase with age have p̃ -values near one. If there were no age-regulated genes (i.e., the true <i>β_kj</i> = 0 for every gene <i>j</i>), then the histogram of p̃ -values would be flat (i.e., have a uniform distribution on the interval from zero to one). The x-axis shows the p̃ -value, and the y-axis shows the number of genes with that p̃ -value. There are 985 genes with a <i>p-</i>value less than 0.001. </p></div

Differential Expression in the Cortex and the Medulla

<p>For each gene, we calculated a p̃ -value for expression differences in the cortex versus the medulla. Shown is a histogram of these p̃ -values. Genes enriched in the cortex are in a peak on the left, and genes enriched in the medulla are in a peak on the right. The x-axis indicates p̃ -value, and the y-axis indicates number of genes. </p

Similar Age-Regulation in Cortex and Medulla

<div><p>(A) For every gene, we calculated a p̃ -value that there is a Tissue<i>_i</i> ×Age<i>_i</i> effect, and plotted the results in a histogram. Genes that show different age regulation in the cortex or the medulla would be contained in peaks on the left and right parts of the histogram. The figure shows that the number of genes that have different expression levels in the cortex and medulla is about the same as or less than would be expected by chance. The x-axis shows one-sided p̃ -values for Tissue<i>_i</i> ×Age<i>_i,</i> and the y-axis shows the number of genes with that p̃ -value. There is a systematic under-representation of the edge regions compared to a random sample of uniform random variables because of correlations among the 44,928 p̃ -values computed from 133 samples. </p> <p>(B) To show whether aging in the cortex and the medulla is similar, we selected age-regulated genes in the cortex and calculated the one-tailed p̃ -value for age effects in the medulla. The histogram shows these selected p̃ -values. The spike at the right shows genes that increase with age in the medulla. Those genes also increased with age in the cortex. </p> <p>(C) Shown is a scatterplot of all 684 genes that are age-regulated in either the medulla or the cortex (<i>p <</i> 0.001). The y-axis is the slope for the medulla of the expression change with respect to age, and the x-axis is the slope for the cortex. The solid line is the least squares line, with a slope of 0.58. The dotted line has a slope of one and passes through the origin.</p> <p>(D) Same as (C) but for 22 genes that are age-regulated in both the cortex and the medulla (<i>p <</i> 0.001).</p></div