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Supplementary discussion
Human aging is associated with a functional decline in both replicating and nonreplicating tissues. The transgenerational functional decline in replicative capacity of
DNA repair mutants that we report here is reminiscent of that observed in germ cells of
telomere replication defective C. elegans mutants(1). One such mutant, trt-1, which has
lost functional telomerase reverse transcriptase, shows a decline in transgenerational
replicative capacity but not in post-mitotic lifespan(2). Although this appears similar to
DNA repair mutants, the phenotypes associated with shortened replicative lifespan due
to DNA repair deficiency are different. Decline of DNA repair mutants is not only caused
by sterility, as in telomerase mutants, but also by embryonic and larval arrest.
Furthermore, DNA repair-deficiency does not necessarily lead to a progressive drop in
brood size as observed when telomeres shorten. This makes it unlikely that telomere
shortening is the main cause of the functional decline in DNA repair mutants.
There is the possibility that changes in gene expression in the ercc-1 mutant, including
regulation of growth and development, are influenced by its developmental and growth
defects. To avoid such bias, transcriptomic analysis was performed on ‘mixed-stage’
populations of four biological replicates grown on multiple plates. Importantly,
correlation analyses between our dataset and that published by Hill et al for each
developmental stage(3) indicated that the wild type and ercc-1 mixed populations that
we analyzed had similar distributions of developmental stages (data not shown). In
addition, the visible developmental and growth defects in the ercc-1 mutant were very
diverse, affecting individual animals in different ways. Thus it seems unlikely that there
was a specific enrichment of early developmental stages or enrichment of a specific
defective developmental process in the ercc-1 mixed stage populations, which had a
great influence on the transcriptomic data. Rather, in UV irradiated cultured mammalian
cells similar changes in gene expression occur as in naturally aged tissues, which is
reverted when UV-photolesions are removed(4). This suggests that accumulating DNA
damage can directly influence transcriptional regulation.
Supplementary Materials and Methods
Extended microarray data analysis
After pre-processing and normalization, “Two Group Comparison” t-tests were executed
on all four replicates per sample group (ercc-1 and N2, respectively) in Qlucore
(http://qlucore.se/) in order to identify genes differentially regulated at the 95%
confidence level (p<0.05) in ercc-1 relative to the wild type, N2. Principal Component
Analysis (PCA) was performed on the differentially expressed (p<0.05) genes after
normalization to mean 0 and variance 1 and correction for a False Discovery Rate
(FDR), q<0.07(5). Gene Set Enrichment Analysis (GSEA) to reveal over- or underrepresented Gene Ontology Biological Processes (BP) was performed using Cytoscape
(http://www.cytoscape.org/) and the plugin BiNGO(6). The analysis was executed using
the Hyper-geometric Test with Benjamini-Hochberg False Discovery Rate (FDR)
correction. Comparisons with other high throughput studies(3, 7-9) were performed; For
the studies where a fold-change (linear signal) or a Difference of Means (log2 signal)
was presented, the Pearson correlation was calculated using the function PEARSON in
Excel 2010. Where no fold-change (Difference of Means) was reported for specific lists
we added up the number of genes/transcripts overlapping between the respective
studies and the transcripts found in the ercc-1 regulated gene list (p<0.05). The counted
numbers of genes overlapping two studies were then subjected to a 2x2 contingency
table for the Fisher Exact test or Chi-square with Yates correction test in order to
calculate for the statistical significance regarding the association between the two
different
studies.
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References
1.
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8.
9.
Zucchero T, Ahmed S. Genetics of proliferative aging. Exp Gerontol. 2006
Oct;41(10):992-1000.
Meier B, Clejan I, Liu Y, Lowden M, Gartner A, Hodgkin J, Ahmed S. trt-1 is the
Caenorhabditis elegans catalytic subunit of telomerase. PLoS Genet. 2006
Feb;2(2):e18.
Hill AA, Hunter CP, Tsung BT, Tucker-Kellogg G, Brown EL. Genomic analysis of
gene expression in C. elegans. Science (New York, NY. 2000 Oct
27;290(5492):809-812.
Garinis GA, Uittenboogaard LM, Stachelscheid H, Fousteri M, van Ijcken W, Breit
TM, van Steeg H, Mullenders LH, van der Horst GT, Bruning JC, Niessen CM,
Hoeijmakers JH, Schumacher B. Persistent transcription-blocking DNA lesions
trigger somatic growth attenuation associated with longevity. Nat Cell Biol. 2009
May;11(5):604-615.
Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Powerful and
Practical Approach to Multiple Testing. J Roy Stat Soc B. 1995;57:289-300.
Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess
overrepresentation of gene ontology categories in biological networks.
Bioinformatics. 2005 Aug 15;21(16):3448-3449.
Budovskaya YV, Wu K, Southworth LK, Jiang M, Tedesco P, Johnson TE, Kim
SK. An elt-3/elt-5/elt-6 GATA transcription circuit guides aging in C. elegans.
Cell. 2008 Jul 25;134(2):291-303.
Lee SS, Kennedy S, Tolonen AC, Ruvkun G. DAF-16 target genes that control C.
elegans life-span and metabolism. Science (New York, NY. 2003 Apr
25;300(5619):644-647.
Roh JY, Sim SJ, Yi J, Park K, Chung KH, Ryu DY, Choi J. Ecotoxicity of silver
nanoparticles on the soil nematode Caenorhabditis elegans using functional
ecotoxicogenomics. Environ Sci Technol. 2009 May 15;43(10):3933-3940.