Transcriptional profiling of aging in human muscle reveals a common aging signature

 

Figures

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Figure 1. Expression of 250 age-regulated genes in muscle. Rows correspond to individual genes, arranged in order from greatest increase in expression with age at top to greatest decrease in expression with age at bottom. Columns represent individual patients, from youngest at left to oldest at right. Ages of certain individuals are marked for reference. Scale represents log2 expression level (Exp). Genes discussed in the text are marked for reference. A navigable version of this figure showing identities of specific genes can be found at cmgm.stanford.edu/~kimlab/aging_muscle/explorer.html.

Figure 2. Three gene sets are regulated with age in muscle. Rows represent the symporter activity, sialyltransferase activity and chloride transport gene sets. Columns correspond to individual genes within a given gene set. Scale represents the slope of the change in log2 expression level with age (ß1j). A navigable version of this figure showing identities of specific genes can be found at cmgm.stanford.edu/~kimlab/aging_muscle/explorer.html.
Figure 3. Gene expression predicts physiology of aging. A: Cross section of histologically unremarkable deltoid muscle from a 48 year old woman demonstrating relatively equivalent sizes of Types I and II muscle fibers. Arrows denote fibers types as distinguished by enzyme histochemistry. (Cryosection, 200X, myosin ATPase at pH 9.4) B: Cross section of deltoid muscle from an 88 year old woman demonstrating selective atrophy of Type II muscle fibers that stain darkly by ATPase enzyme histochemistry.. (Cryosection, 200X, myosin ATPase at pH 9.4) C: Histograms showing a correlation between muscle physiology and gene expression for age-regulated genes. Top: For each of the 250 age-regulated genes, we calculated the partial correlation coefficients between the TypeII/TypeI muscle fiber diameter ratio and gene expression excluding age variation (x-axis). Bottom: As top, 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 TypeII/TypeI muscle fiber diameter ratios while excluding the effects of age. D: Histogram showing the likelihood of finding 92 genes with |r| > 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 |r| > 0.2 as in part A. The procedure was repeated 1000 times and the histogram shows the number of genes from each random selection that have |r| > 0.2. The arrow shows the number of genes exceeding this threshold (92) from the set of 250 age-regulated genes (p < 0.001).

Figure 4. A common signature for aging in muscle, kidney, and brain. 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 (ß1j). Grey indicates genes were not present in the data set. A navigable version showing identities of specific genes can be found at http://cmgm.stanford.edu/~kimlab/aging_muscle/.

Figure 5. The electron transport chain decreases expression with age in human and mouse. 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 (ß1j). Data from different species were normalized by dividing the slope of expression with age by the standard deviation of all similar slopes in the data set. Grey indicates genes were not present in that species. A navigable version showing identities of specific genes can be found at cmgm.stanford.edu/~kimlab/aging_muscle/explorer.html.
Figure S1. Age distribution of medical and pharmaceutical factors. Each row denotes a medical or pharmaceutical factor. Age of patients is shown on the x-axis. Sex, biopsy location and 12 medical factors are shown in the legend. Only hypothyroidism shows any overt bias with age.
Figure S2. Medical and pharmaceutical factors do not affect age-regulation. A: Coronary artery disease was included as an additional term in equation 1 and the model was recalculated for the 250 genes that significantly change expression with age. The slope of expression with age (age coefficient) from models with (y-axis) and without (x-axis) the coronary artery disease term was plotted. If coronary artery disease affected expression, we would expect a large deviation in age coefficient. No significant deviation was seen for any of the 250 age-regulated genes, indicating that coronary artery disease does not adversely affect our study of age-regulation. B-O: Similar to A for eleven other medical factors. B, coronary artery disease; C, colorectal cancer; D, end stage renal disease; E, hyperlipidemia; F, hypertension; G, hypothyroidism; H, pancreatic cancer; I, prostate cancer; J, radiotherapy; K, statins; L, villous adenoma.
Figure S3. Cluster analysis of medical and pharmaceutical factors. Columns are individual muscle samples. Samples are clustered on the basis of 250 age-regulated genes in muscle. Top corresponds to the expression of seven representative age-regulated genes labeled atop by sample age. Below shows medical and pharmaceutical factors for each sample. Each row corresponds to one medical or pharmaceutical factor.