Effectors of mammalian telomere dysfunction: a comparative transcriptome analysis using mouse models
Sonia Franco,
Andrés Canela,
Peter Klatt and
María A. Blasco *
Telomeres and Telomerase Group, Molecular Oncology Program, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain
* To whom correspondence should be addressed. Tel: +34 917328031; Fax: +34 917328028; Email: mblasco{at}cnio.es
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Abstract
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Critical telomere shortening in the absence of telomerase in late generation Terc/ mice (G3 Terc/) or loss of telomere capping due to abrogation of the DNA repair/telomere binding protein Ku86 (Ku86/ mice) results in telomere dysfunction and organismal premature aging. Here, we report on genome-wide transcription in mouse G3 Terc/, Ku86/ and G3 Terc//Ku86/ germ cells using high-density oligonucleotide microarrays. Although a few transcripts are modulated specifically in Ku86- or Terc-deficient cells, the observed transcriptional response is mainly inductive and qualitatively similar for all three genotypes, with highest transcriptional induction observed in double mutant G3 Terc//Ku86/ cells compared with either single mutant. Analysis of 92 known genes induced in G3 Terc//Ku86/ germ cells compared with wild-type cells shows predominance of genes involved in cell adhesion, cell-to-cell and cell-to-matrix communication, as well as increased metabolic turnover and augmented antioxidant responses. In addition, the data presented in this study support the view that telomere dysfunction induces a robust compensatory response to rescue impaired germ cell function through the induction of survival signals related to the PI3-kinase pathway, as well as by the coordinated upregulation of transcripts that are essential for mammalian spermatogenesis.
Abbreviations: ATM, ataxia telangiectasia; Cdk5, cyclin-dependent protein kinase-5; CTGF, connective tissue growth factor; DM, double mutant; DSB, double strand break; ECM, extracellular matrix; qRT-PCR, quantitative real-time PCR; RT, reverse transcription; SM, single mutants; Terc, telomerase RNA component
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Introduction
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Aging, or the progressive deterioration of tissue and organismal function over time, is a non-adaptive process that results from the accumulation of damage (1). Genetic studies in yeast and worms have unraveled several molecular pathways that control organismal lifespan (see http://sageke.sciencemag.org/cgi/genesdb for a comprehensive list) and a number of mouse models have also been engineered to partially recapitulate human aging [segmental progeroid syndromes (reviewed in ref. 2)]. Genomic instability, which has been conclusively linked to aging in Saccharomyces cerevisiae (3), is also a feature of many of these mouse models (2), suggesting that accumulation of DNA damage leads to aging in mammalian cells as well.
Telomeres are highly specialized heterochromatic structures at the end of mammalian chromosomes that buffer DNA loss with cell division (4). In addition, a highly specialized structure at the most distal telomere known as the T-loop, which is formed by folding back of the 3' overhang into adjacent double-stranded telomeric DNA, is proposed to prevent the processing of chromosomes ends as broken DNA ends (5). In fact, loss of telomere integrity (telomere uncapping) evokes a cellular DNA damage-like response, which promotes p53-dependent growth arrest or apoptosis (6,7). Telomerase is a reverse transcriptase capable of telomere elongation (8). In humans, decreased levels of telomerase activity due to defective accumulation of the telomerase RNA component (Terc), leads to accelerated telomere shortening and a premature aging syndrome known as Dyskeratosis congenita (9,10). In mice, gene targeting of the telomerase RNA component, Terc, leads to progressive telomere shortening with mouse generation, until critically short telomeres and loss of organismal viability appear (1113). These late-generation mice (third generation G3 Terc/ mice when in a C57BL6 genetic background) display a segmental progeroid syndrome that includes decreased lifespan, defects in highly proliferative tissues, immunosenescence, as well as decreased fertility, among others (1113). Critical loss of telomere repeats and appearance of end-to-end chromosomal fusions in late generation Terc/ mice, correlates with apoptosis and/or cellular growth arrest (1113).
In addition, telomere dysfunction may result from the loss of proteins that bind to the double stranded TTAGGG track, such as TRF2, Ku86 and DNA-PKcs among others (1420). Ku86 and DNA-PKcs are also essential components of the main pathway for double strand break (DSB) repair in mammalian cells, the non-homologous end joining pathway (NHEJ). Similar to critically short telomeres, Ku86 deficiency leads to premature senescence of MEFs (21), as well as premature aging of mice (22). However, currently, it is not clear what is the relative contribution of telomere dysfunction to these phenotypes, since Ku86 plays additional roles in DSB repair and many other cellular processes (reviewed in ref. 23).
Mice deficient for both telomerase and Ku86 have been previously generated by us (Terc//Ku86/ mice) and their telomere length dynamics and response to critically short telomeres characterized in detail (24,25). In particular, we focused on male germ cell telomeres, which have a protein composition similar to that of somatic telomeres (26). Significantly, germ cell apoptosis observed in response to short telomeres in G3 Terc//Ku86+/+ germ cells (12,27) was rescued in G3 Terc//Ku86/ littermate germ cells despite a similar degree of telomere shortening (24), suggesting a role for Ku86 in signaling critically short telomeres. In contrast, proliferative arrest due to short telomeres (12,27) was aggravated in G3 Terc//Ku86/ germ cells compared with single G3 Terc/ cells (24), pointing to the cooperative effect of simultaneous Ku86 and telomerase deficiency on cell growth suppression. In this report, we use oligonucleotide arrays to identify transcriptionally regulated elements downstream of dysfunctional telomeres in G3 Terc/, Ku86/ and G3 Terc//Ku86/ cells.
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Materials and methods
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Mice and sample collection
Mice deficient in either the telomerase RNA component, Terc, and/or Ku86 were generated as previously described (24). The genetic background of wild-type mice, as well as of the Terc and Ku86 heterozygous mice used to generate the different mouse colonies, was in all cases a
95% C57BL6 background (13,24). Testes were excised from two 35-month-old mice of each genotype (Terc+/+/Ku86+/+; Terc+/+/Ku86/; G3 Terc//Ku86+/+ and G3 Terc//Ku86/) and germ cells were obtained following standard procedures. In brief, seminiferous tubules were separated from the fibrous capsule in a Petri dish and minced with razor blades to release germ cells. After a brief centrifugation to remove debris, cells were flash-frozen and stored at 80°C.
RNA extraction and oligonucleotide microarray analysis (Affymetrix)
Total RNA was extracted from frozen testes using RNAwiz (Ambion, Austin, TX) followed by the Qiagen Rneasy (Qiagen, Valencia, CA). Each RNA preparation was tested for degradation. cDNA was synthesized from 10 µg total RNA using an oligo-deoxythymidylic acid 24 primer with a T7 RNA polymerase promoter site added to the 3' end (Superscript II ReverseTranscriptase; Life Technologies, Rockville, MD). After second-strand synthesis, in vitro transcription was performed using BioArray High Yield RNA transcript labeling kit (Enzo, Farmingdale, NY) to produce biotin labeled cRNA. 20 µg of the cRNA product was fragmented at 94°C for 35 min into 35200 bases in length, and 5 µg of the cRNA was used for the TestChip (test3, Affymetrix, Santa Clara, CA) and 15 µg of the cRNA for the MG-U74Av2 (Affymetrix) hybridization. Before application to Testchips and MG-U74Av2 chips, RNA quality was tested using the Agilent 2100 Bioannalyzer (Agilent technologies, Palo Alto, CA) as a correct quality control report in the Testchips was a prerequisite to MG-U74Av2 chips hybridization. Each sample was added to a hybridization solution containing 100 mmol/l 4-morpholinepropanesulfonic acid, 1 mol/l Na+ and 20 mmol/l of EDTA in the presence of 0.01% Tween 20 to a final cRNA concentration of 0.05 µg/ml. Hybridization was performed for 16 h by incubating 300 µl of the sample to MG-U74Av2 chips at 45°C, and each microarray was stained with streptavidin-phycoerythrin in Fluidics station (Affymetrix) and scanned at 3 µm resolution by Agilent HP G2500A GeneArray scanner (Agilent Technologies) according to procedures developed by Affymetrix. Images and Absolute and Comparison Data were obtained using Affymetrix Microarray Suite 5.0 Software, and all chips were scaled at TGT: 200.
Microarray data analysis
Detailed protocols for data analysis of Affymetrix microarrays, as well as extensive documentation on the sensitivity and quantitative aspects of the method, have been described (28,29). Briefly, mismatch probes act as specificity controls that allow the direct subtraction of both background and cross-hybridization signals. To determine the quantitative RNA abundance, the average of the difference representing perfect matchmismatch for each gene-specific probe family is calculated. The global method of scaling/normalization was used, as recommended by Affymetrix for the analysis of experiments in which a small subset of transcripts is changing. For hierarchical clustering, we used the signal log ratio for each transcript of each individual wild-type comparison (4 comparisons/genotype), and the average signal log ratio for each transcript for the comparisons among genotypes, with a cut-off at 1.8-fold, according to the Wilcoxon's Signed Rank test (P-value for each individual comparison <0.02). Data were further filtered using Affymetrix categories absent or present to eliminate RNAs with low levels of expression, i.e. for transcripts that decreased in all comparisons; those classified as absent in the base were eliminated. Similarly, for transcripts that increased in all four comparisons, those being absent in the experiment were eliminated. Microarray raw data are publicly available at the Gene Expression Omnibus (GEO) web repository (http://www.ncbi.nlm.nih.gov/geo) under the GEO series entry number GSE2498.
Functional analysis of selected transcripts was done using NetAffyx (Affymetrix), Gene Ontology, publicly available software at Stanford University (SOURCE) and at the European Bioinformatics Institute (EPCLUST tool for cluster generation), as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID; http://apps1.niaid.nih.gov/david) (30) and the MILANO web server for literature-based annotation of microarray results (http://milano.md.huji.ac.il) (31).
Quantitative real-time RTPCR
For microarray validation by quantitative real-time PCR (qRT-PCR), total RNA was extracted from frozen testes using RNAwiz (Ambion). Reverse transcription (RT) was performed using 5 µg of total RNA previously treated with RQI RNAse-free DNAse (Promega, Madison, WI), random hexameres and Superscript II reverse transcriptase (Life Technologies) following the manufacturer's instructions. Real-time PCR was performed with an ABI PRISM 7700 instrument (Applied Biosystems, Foster City, CA) using SYBR Green PCR Core Reagents (Applied Biosystems). Final concentrations of MgCl2 and primers were 4.5 mM and 0.4 µM, respectively. Reactions were incubated for 10 min at 95°C followed by 40 PCR cycles (15 s at 95°C, 45 s at 60°C and 90 s at 68°C). The relative gene expression levels were determined upon normalization with ß-actin mRNA 
Ct values, which express the cycle threshold difference between each selected gene (Csrp1, Timp3 and Fyn) and ß-actin. Primer pairs for Csrp1 were: 5' TCT AGC CAC TCC TGA GGG TTC C 3' and 5' TGG GGT GGG CAA GGT AGT GAA G 3'; Timp3: 5' AGT GGT GGG AAA GAA GCT GGT G 3' and 5' GAC TTT CAG AGG CTT CCG TGT G 3'; Fyn: 5' GAA CTC CTC CTC TCA CAC TGG G 3' and 5' CCA GTT TCC CCG GTT GTC AAG G 3'; ß-Actin: 5' GGC ACC ACA CCT TCT ACA ATG 3' and 5' GTG GTG GTG AAG CTG TAG 3'. qRTPCR was performed with RNA preparations from two mice per genotype and each PCR was repeated at least twice.
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Results
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Trancriptional changes in germ cells with uncapped telomeres: comparison of single (G3 Terc/, Ku86/) and double (G3 Terc//Ku86/ ) mutants
Using Affymetrix algorithms, 12 490 probe set IDs corresponding to
6000 known transcripts and 6000 ESTs were classified as either present (P), marginally present (M) or absent (A). In wild-type testes, 4665 ± 132.2 transcripts were P/M, which corresponds to 37.3 ± 1.1% of the total transcripts represented in the array (data obtained as average and standard deviation of two mice). This was the same for G3 Terc/, Ku86/ and G3 Terc//Ku86/ RNA (35.4 ± 4.2%, 36.5 ± 0.4% and 40.2 ± 4.1%, respectively). Using a 1.8-fold change as a threshold, we compared the transcriptional profile of single (G3 Terc/ and Ku86/) or double mutant cells (G3 Terc//Ku86/) to wild-type cells (experimental design and phenotypic features of each genotype are summarized in Figure 1A). We found 18 transcripts (9 known genes and 9 unknown) that varied significantly in G3 Terc/ testes, 19 transcripts (16 known genes, 3 unknown) in Ku86-deficient testes, and 154 transcripts (92 known genes, 62 unknown) in G3 Terc//Ku86/ testes (Figure 1B, transcripts listed in Tables I
III). Thus, a significantly greater effect on gene transcription (
8-fold higher) was observed in G3 Terc//Ku86/ cells compared with the single mutant controls. In particular, only
0.2% of transcripts varied significantly in single mutant cells, while
1.3% varied in double mutant cells.

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Fig. 1. Experimental design and global result quantification. (A) Total RNA from wild-type, G3 Terc/, Ku86/ and G3 Terc//Ku86/ testes was hybridized to Affymetrix U74Av2 oligonucleotide arrays. Phenotypic features of each genotype are summarized and the relevant comparisons are indicated: Comparison (1) base array: wild-type RNA; experiment array: G3 Terc/ RNA: identifies transcriptional changes related to critical telomere shortening (Table II); Comparison (2) base array: wild-type RNA; experiment array: Ku86/ RNA; identifies transcriptional changes related to telomere uncapping and DNA damage (Table III); Comparison (3) base array: wild-type RNA; experiment array: G3 Terc//Ku86/ RNA; identifies transcriptional variation due to telomere dysfunction (loss of repeats and uncapping) and DNA damage (Table I). (B) Quantification of transcripts that vary over 1.8-fold in G3 Terc/, Ku86/ and G3 Terc//Ku86/ RNA (compared with wild-type RNA in all cases). Notice that transcriptional variation in double mutants is 8-fold higher than in either single mutant. (C) The transcriptional response to telomere dysfunction is predominantly inductive.
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Table IV. Transcriptional induction of endogenous retroviral components in G3 Terc/, Ku86/ and G3 Terc//Ku86/ germ cells
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A striking feature observed in all three genotypes, and most prominent in the double mutants, was the marked unidirectionality of the transcription changes. In particular, the number of transcripts that increased/decreased were 17/1, 13/6 and 152/1 for G3 Terc/, Ku86/ and Ku86//G3 Terc/ cells, respectively (Figure 1C). This finding is in line with data obtained from previous aging studies, where the stress response was also characterized mainly by gene induction (32). Moreover, the fold-change for each individual transcript was consistently higher in double mutant cells compared with either single-mutant (Tables I
III; Figures 2 and 3). To delineate this further, we clustered all transcripts that varied in any direction in all three genotypes using publicly available tools at the European Bioinformatics Institute (EMBL-EBI) (Figure 2A). Significantly, the overwhelming majority of genes that were upregulated in the Ku86//G3 Terc/ double mutants were also upregulated, albeit to a minor extent (often below our threshold level of 1.8-fold change) in the G3 Terc/ and Ku86/ single mutants (Figure 2A). These observations indicate a strong commonality in the transcriptional response to different mechanisms of DNA damageshort telomeres in the case of G3 Terc/ mice and combined defective DSB repair and dysfunctional telomeres in the case of Ku86-deficiency. We further calculated for each transcript the double mutant (DM) to single mutants (SM) index, IDM/SM = (fold change in G3 Terc//Ku86/ compared with wild-type)/(fold change in G3 Terc/ compared with wild-type + fold change in Ku86/ compared with wild-type). As shown in Figure 2B, with the exception of Tyro3 tyrosine kinase, IDM/SM values were close to 1 for nearly all transcripts with an average IDM/SM of 0.8 ± 0.3, indicating an additive rather than synergistic effect of both mutations on in vivo transcription.


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Fig. 3. Telomere dysfunction induces a global stress response: comparative fold-change of representative transcripts for single (G3 Terc/, Ku86/) and double mutants (G3 Terc//Ku86/). (A) Extracellular matrix modulators grouped according to their function (collagens, proteoglycans, proteases, protease inhibitors, adhesion, cell-to-cell communication, survival, transport). (B) Structural and regulatory cytoskeletal components. (C) Regulators of metabolism (protein metabolism, lipid metabolism, retinoic acid metabolism, redox metabolism, etc.). (D) Cell signaling. (E) Regulators of nucleic acid metabolism (transcription, splicing, chromatin remodeling, transposition, gametogenesis). Bars represent the average and standard deviation of four independent comparisons for each genotype. Numerical labels represent the average fold-increase for double mutant (G3 Terc//Ku86/) cells.
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Using the NetAffx Transcript Ontology Mining Tool and publicly available protein domain information (http://www.ebi.ac.uk/interpro), we classified transcripts according to their cellular location, biological function and molecular function in five main groups: (i) extracellular matrix (ECM, including collagens, proteoglycans, proteases, protease inhibitors, adhesion, cell-to-cell communication, survival, cation transport); (ii) cytoskeletal dynamics (both structural and regulatory subunits); (iii) metabolism (including protein synthesis, protein transport, protein folding, protein degradation, lipid metabolism, retinoic acid metabolism, carbohydrate metabolism, mitochrondria metabolism, iron metabolism and redox metabolism); (iv) cell signaling, and (v) nucleic acid metabolism (including transcription factors, DNA damage response, splicing, chromatin remodeling, transposons and genes involved in gametogenesis) (Tables I
III; Supplementary Figures 1 and 3). Transcripts coding for known proteins that varied over 1.8-fold in G3 Terc//Ku86/ cells compared with wild-type cells (n = 92) are listed in Table I grouped according to the five categories described above. Relevant references for the individual transcripts are available as Supplementary Bibliography (see Supplementary information). All transcripts that varied over 1.8-fold in single mutant G3 Terc/ cells and Ku86/ cells compared with wild-type cells are shown in Tables II and III, respectively.
To validate the observed expression changes, three genes (Csrp, Fyn, Timp3) were randomly chosen from the set of genes detected as upregulated in G3 Terc//Ku86/ mice (see Table I) and their relative expression levels were validated by qRTPCR as described in Materials and methods. Expression of one of these genes (Fyn) was also validated in mice single mutant for either Terc or Ku86. We found that the expression changes determined by qRTPCR were in good agreement with the microarray data (for data please refer to Supplementary Table I).
Transcriptional profile of Ku86//G3 Terc/ cells
Significantly, 44% of transcripts induced in G3 Terc//Ku86/ cells code for extracellular proteins and 32% for integral membrane proteins (Gene Ontology). Using the DAVID software (http://apps1.niaid.nih.gov/david) (30) as a tool to analyze the statistical significance of this finding in the context of the U74Av2 array, we confirmed that extracellular matrix, basement membrane and cell adhesion were in fact the functional categories with highest statistical significance (see Supplementary Table I for complete analysis and P-values).
In addition, transcripts encoding for regulators of protein folding, chaperoning and degradation as well as transcripts encoding for nuclear factors, including several known transcription factors and regulators of alternative splicing, were found to be upregulated (Table I; Figure 3C). We failed to detect significant induction of genes with well-characterized roles in DNA damage responses, such as p53, ATM, 53BP1, BRCA1, BRCA2, RAD51 and the MRN complex components. For some cases (i.e. p53, ATM, RAD51 and BRCA1), their low levels of expression were below the sensitivity of oligonucleotide arrays precluding further analysis. Nonetheless, we observed that the hybridization signal intensity for ATM mRNA was consistently greater in arrays hybridized with RNA from the double mutant G3 Terc//Ku86/ cells as compared with the single mutants (data not shown), indicative of an upregulation of this pathway. Transcriptional changes indicative of a p53-mediated telomere-damage response included induction of Pa26, a known p53 target and DNA damage-inducible stress response gene, as well as induction of nucleolin, which has been reported to play a p53-dependent role in DNA repair and DNA damage-induced replication inhibition (see Figure 3E, Table I, and Supplementary Bibliography to Table I).
One particular feature of the male germ cell compartment is its sensitivity to subtle changes in the balance between death and growth signals at distinct developmental stages (33). Upregulated transcripts (see Table I) that might affect germ cell viability and survival in the testes of G3 Terc//Ku86/ mice include: tissue inhibitor of metalloproteinases-3 (Timp3), a putative mediator of apoptosis by stabilizing death receptors and activating caspase-8 and -9; progesterone receptor membrane component-1 (Pgrmc1), reported to sensitize human breast cancer cells to apoptosis upon exposure to oxidative stress; the family D melanoma antigen-1 (Maged1), a potent inducer of apoptosis via Jun kinase (JNK)-dependent caspase activation; the solute carrier family 25 member 4 (Slc25a4), recently reported to sensitize cells to apoptosis through mitochondrial recruitment of nuclear factor-
B; gap junction protein alpha-1 (Gja1), shown to mediate apoptosis through downregulation of bcl-2; cyclin-dependent kinase-5 (Cdk5), proposed to mediate cAMP- and toxin-induced apoptosis in extra-neuronal tissues; cellular retinol-binding protein-1 (Rbp1), an inhibitor of the PI3-kinase/Akt survival pathway; the cohesin subunit Rad21, which might amplify cell death signals through the accumulation of proteolytic cleavage products in the cytoplasm (see Figure 3, Table I, and Supplementary Bibliography to Table I). However, we observed the concomitant induction of transcripts that might contribute to a telomere damage-driven growth arrest: hypoxia-inducible factor-1
(Hif1a), a transcriptional regulator of cell cycle arrest during hypoxia and other types of cellular stress; Fyn, a tyrosine kinase related to growth suppression in neuronal cells and keratinocytes; IGF-1 binding protein-6 (Igfbp6), reported to impair growth of neuronal and epithelial cells; cyclin-dependent kinase-5 (Cdk5), known to modulate senescence-associated changes in cell shape and to be upregulated in the aging organism; a number of other senescence markers such as connective tissue growth factor (CTGF), clusterin (Clu), the heat shock protein Hspa8, and the growth arrest-specific gene products Gas5 and 6 (see Figure 3, Table I, and Supplementary Bibliography to Table I). The induction of these growth arrest-associated transcripts and senescence markers fits well with the severe proliferative defects observed in the testes of G3 Terc//Ku86/ mice.
It is tempting to speculate that prolonged telomere dysfunction might trigger an adaptive response to compensate for the loss of the proliferative potential in the affected organ. Activation of the integrin/PI3-kinase signaling pathway is essential for male germ cell survival, growth and differentiation (33,34). Indeed, G3 Terc//Ku86/ germ cells expressed elevated levels of transcripts related to integrin/PI3-kinase signaling such as ß1-integrin (Itgb), the receptor protein tyrosine kinases Fyn/Tyro3 and their ligands Protein S and Gas6 as well as of a transcript encoding a Fyn-interacting protein, the Src activating and signaling molecule (Srcasm), which functions as an adaptor molecule between Fyn and the PI3-kinase subunit p85 (see Figure 3, Table I, and Supplementary Bibliography to Table I). Additional upregulated genes for which there is evidence that they may be implicated in survival and growth signaling through activation of PI3-kinase include the amyloid precursor protein (APP), the cyclin-dependent protein kinase-5 (Cdk5), the ectonucleotide pyrophosphatase/phosphodiesterase-2 (Enpp2) and the kit ligand (Kitl). Other upregulated transcripts reported to play a role in survival signaling, also through PI3-kinase-independent pathways, include Clusterin (Clu), connexin-43 (Gja1), nucleolin (Ncl), secreted acidic cysteine rich glycoprotein (Sparc), spindling (Spin 2), prothymosin-
(Ptma), thioredoxin (Txn1 and cathepsin L (Ctsl) (see Figure 3, Table I, and Supplementary Bibliography to Table I). Finally, it should be noted that three of the upregulated genes in G3 Terc//Ku86/ germ cells code for known activators of telomerase, namely the transcription factors Hif-1
and Max, as well as Kit ligand (Kitl), which has been shown to contribute to the self-renewing potential of male germ cells through the induction of telomerase activity in mitotic spermatogonia (see Figure 3, Table I, and Supplementary Bibliography to Table I). This compensatory response to telomere damage further involves functionally related components of cell junctions with essential roles in spermatogenesis (34,35) and occurs at the level of gap junctions (connexin-43), adherens junctions (beta-integrin) and tight junctions (claudin-11), as well as at the level of adaptor proteins (
-actinin), the underlying cytoskeletal network (
2-actin, myosin heavy chain IX, microtubule-actin crosslinking factor-1), membrane receptors (Fyn and Tyro3) and their ligands (Protein S and Gas6) (see Figure 3, Table I, and Supplementary Bibliography to Table I). Together, these results show that telomere dysfunction in the testes results in the coordinated upregulation of functionally linked genes that are essential for mammalian spermatogenesis.
In yeast, the upregulation of genes in response to telomere damage was proposed to involve a coordinated transcriptional stress response triggered by the STRE (stress responsive element) promoter element. To address whether this was also the case in our mouse model, we analyzed the promoters of the set of co-expressed genes in Table I that define the telomere damage signature in G3 Terc//Ku86/ germ cells (see Supplementary Table II for further details). We found that about two thirds of the analyzed promoters contained consensus binding sites for a limited number of transcription factors that can be grouped into two functional classes: (i) the Sry/Sox family of transcription factors (Sry, Sox5, Sox9 and SOX17), which are master regulators of male-specific gene expression in the testes (36), and (ii) transcriptional regulators related to stress and survival pathways including the nuclear factor-
B subunits p50, p65 and c-Rel, c-Fos, the aryl hydrocarbon receptor/aryl hydrocarbon receptor nuclear translocator complex (AHR/ARNT), upstream stimulating factor (USF), as well as n-Myc and Max (see Supplementary Table II). The target promoter with the highest probability to bind these testes-specific stress and survival factors, was the connective tissue growth factor (CTGF) promoter (predicted binding sites for 11 out of 12 transcriptional factors in this group). Of note, CTGF was one of the few genes consistently upregulated above the threshold of 1.8-fold in the three genotypes analyzed in this study (G3-Terc/: 1.9-fold; Ku86/: 2.0-fold; G3-Terc//Ku86/: 3.1-fold; see also Figure 3A and Tables I
III). CTGF, a putative marker of replicative and stress-induced senescence, functions as a potent stress-responsive inducer of ECM protein production and as a versatile regulator of cytoskeletal dynamics through beta-integrin/PI3-kinase signaling (refer to Supplementary Bibliography as indicated in Table I). These characteristics fit well with the observation that the telomere damage response that we observe in our system involves the upregulation of numerous transcripts related to ECM and cytoskeletal dynamics (see Table I and Figure 3A and B), as well as the induction of transcripts related to integrin and PI3-kinase signaling pathways (see above). More strikingly, CTGF transgenic mice exhibit some of the phenotypic hallmarks of telomere damage such as growth defects, decreased bone density and testes atrophy (37), suggesting that CTGF might play an, as yet, unexplored role in the organismal response to telomere damage.
Genotype-specific responses: G3 Terc/ versus Ku86/ cells
Overall, profiling of either G3 Terc/ or Ku86/ testes compared with wild-type testes revealed transcription patterns qualitatively similar to each other (Figure 2A; Figure 3 for comparative fold-increase of representative transcripts across genotypes; Tables I and III for lists of all transcripts that varied
1.8-fold in G3 Terc/ and Ku86/ cells, respectively). Interestingly, some genotype-specific transcriptional changes were also observed, which may reveal Terc-specific or Ku86-specific responses. In particular, a glutathione-based antioxidant response appeared to be activated only in G3 Terc/, but not in Ku86/ cells (Figure 3C). Transcripts encoding for the cytosolic glutathione S transferases Gsmt1, Gsmt2 and Gmst3, as well as for the microsomal glutathione S transferase 1 (Mgst1), were modestly but consistently induced in G3 Terc/ and G3 Terc//Ku86/ cells, while Ku86-deficient cells showed no detectable change in transcriptional regulation of these genes (Figure 3C). In contrast, induction of three transcripts coding for intracisternal A particles (IAPs), which are LTR-containing defective endogenous retroviruses (38), was observed specifically in Ku86/ and G3 Terc//Ku86/ cells, but not in G3 Terc/ cells (Tables I and IV for details; Figure 3E). Interestingly, there is evidence linking retrotransposition to telomere maintenance (reviewed in ref. 39). Alternatively, Ku86 may be a repressor of this pathway in wild-type cells. In this regard, activation of the LTR-containing retrotransposon Ty1 has been recently shown in response to telomere erosion in S.cerevisiae (40) as general response to DNA damage in the absence of an effective NHEJ pathway (41).
Transcriptional changes affecting chromatin remodeling activities and subtelomeric genes in G3 Terc//Ku86/ cells
Several transcripts encoding for proteins involved in chromatin remodeling were found induced in G3 Terc//Ku86/ cells: retinoblastoma binding protein 7 (Rbbp7/mRbAp46), an essential component of the core histone deacetylase complex; the high mobility group box 3 (Hmgb3/HMG2A), an architectural transcription factor essential for spermatogenesis; the linker Histone 1 H2bc (Hist1h2bc); and the histone deacetylase 6 (Hdac6), also involved in cytoskeletal dynamics (Table I, Figure 3B and E). These transcriptional changes in chromatin remodeling genes suggest epigenetic alterations associated with short/dysfunctional telomeres in G3 Terc//Ku86/ cells. In addition, we have performed a gross topological assessment to investigate a possible epigenetic regulation of transcriptional patterns with telomere dysfunction, which in turn may be relevant for cellular senescence. Telomeric heterochromatin, which is maintained by specific histone methylating activities (42), has been proposed to regulate transcription in subtelomeric regions through the so-called telomere position effect (TPE) (43). In contrast, other studies have proposed that the cellular response to senescence results from the coordinated activation of cell type-specific gene clusters (44). To investigate whether either of these patterns was operative in mouse germ cells with telomere dysfunction, we mapped 90 transcripts induced in G3 Terc//Ku86/ cells to specific chromosomal regions using publicly available bioinformatic tools (http://source.stanford.edu). As expected, the number of transcripts correlated grossly with the size of the chromosome, with the exception of the X chromosome, which was overrepresented (15.7% of the transcripts were X-linked; Figure 4A). This relative increase in X-linked transcripts, best observed when plotting the number of transcripts induced versus the estimated total number of transcripts in the same chromosome (http://www.ensembl.org/Mus_musculus) (Figure 4B), could reflect either on the previously described preferential expression of X-linked transcripts in male spermatogenenesis (45) or on a specific telomeric effect in the X chromosome (46). However, X chromosome overrepresentation was not observed in a prior study of gene expression with replicative senescence (44) and gene distribution over the length of this chromosome revealed no preferential distribution in subtelomeric areas (Figure 4C). This finding was extended to the autosomes and neither telomere position effect nor gene clustering were observed (Figure 4D), despite the fact that a significant proportion of chromosome ends have been shown to have undetectable or very short telomeres in these cells (24).

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Fig. 4. Epigenetic regulation of transcriptional patterns in G3 Terc//Ku86/ cells. (A) Chromosome distribution of transcripts that were induced in double mutants. Of notice, 15.7% located to the X chromosome. (B) Plotting of the number of transcripts induced in each chromosome versus the total number of genes in the same chromosome (ENSEMBL). Relative enrichment for X-transcribed genes is noticed. (C) X chromosome band distribution of genes preferentially induced in G3 Terc//Ku86/ germ cells (ENSEMBL). (D) Geographical transcription pattern for autosomal transcripts induced in G3 Terc//Ku86/ cells compared with wild-type cells (ENSEMBL). No significant clustering at subtelomeric regions or other chromosomal sites was observed.
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Discussion
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We, along with others, have previously shown that telomere dysfunction leads to loss of cell viability and premature deterioration of the organism, as exemplified by both telomerase-deficient Terc/ mice with critically short telomeres and by Ku86/ mice, which have a defective DNA repair as well as impaired telomere protection (13,22). Here, we have used genomic tools to directly compare unmodified mouse tissues from mice with different types of telomere dysfunction: (i) critically short telomeres in G3 Terc/; (ii) defective DNA repair and dysfunctional telomeres in Ku86/ mice; and (iii) a combination of these defects in double mutant G3 Terc//Ku86/ mice.
The acute response to telomere dysfunction involves the activation of DNA repair and recombination pathways as well as p53-dependent activation of apoptosis and growth arrest (24,4750). In yeast, microarray analysis of the telomere damage response picked up only few genes with known roles in telomere function or DNA recombination and repair pathways (51). Similarly, in mouse germ cells only few transcriptional changes could be related to p53 signaling and DNA repair. This finding might be explained by the fact that microarray analysis fails to identify post-transcriptional modifications, such as phosphorylation of proteins that signal telomere dysfunction (7). Nonetheless, many of the gene expression changes observed here, such as the concomitant induction of transcripts with roles in apoptosis, cell proliferation and differentiation, are indicative of the profound impact of telomere damage on male germ cell homeostasis and fit with the phenotype of severe loss of testes function in these mouse models.
As mentioned before, the three mouse models analyzed here show a common phenotype, namely testicular atrophy and impaired fertility (13,22,25). A recent study describes gene expression patterns associated with infertility in humans and in three different infertile mouse models, namely atrichosis mutation, ataxia telangiectasia (ATM) knock-out, and cyclic AMP response modulator (Crem) tau knock-out mice (52). It is noteworthy that the infertility signature observed in this study partially overlaps the gene expression changes found in our mouse models of telomere damage. Gene expression changes common to both studies include induction of transcripts with roles in cell adhesion (Bgn, Tyro3, Vcam1), cell growth (Igfbp6) and redox homeostasis (Mgst1, Txn1), as well as upregulation of a senescence marker (Clu).
Microarray analysis of telomerase-deficient yeast suggests that telomere-driven changes in gene expression represent downstream effectors of telomere damage shared by different forms of cellular stress to maintain a compensatory and adaptive response over time (51). In yeast, such an adaptive response to telomere function involves a marked upregulation of energy production genes accompanied by mitochondrial proliferation (51). In our study, the gene expression changes point to a testes-specific compensatory response adapting germ cells to survival in a situation of telomere dysfunction (Figure 5). Such an adaptive response includes the upregulation of transcripts involved in integrin/Fyn/Tyro3 protein kinase signaling and in the downstream activation of the PI3-kinase/Akt survival pathway as well as the induction of numerous transcripts essential for mammalian spermatogenesis. This adaptation to loss of telomere function also includes upregulation of a set of transcription factors that, in telomerase proficient cells, would trigger activation of telomerase, such as Hif-1
, Max, and Kitl. All together, these results suggest that, in response to severe telomere function, cells upregulate genes aimed at rescuing impaired testes function. Activation of PI3-kinase signaling appears to play a central role in such adaptive response. In this context, it is noteworthy that PI3 kinase signaling has been related to organismal aging (53) pointing to a link between the transcriptional changes observed in our mouse models and telomere damage-driven aging.

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Fig. 5. Schematic representation of a global stress response in mouse germ cells in response to telomere dysfunction/DNA damage. Classification of transcripts according to their cellular location and biological functions suggests a network of interconnected cellular stress responses.
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Supplementary material
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Supplementary material can be found at http://carcin.oxfordjournals.org/
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Acknowledgments
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We thank A.Benguría and staff at the CNB Affymetrix Core Facility for technical assistance, R.Serrano for mouse care, E.Santos and J.Freire for genotyping, and M.Serrano for critical comments on the manuscript. M.A.B.'s laboratory is funded by the MCyT (SAF2001-1869, GEN2001-4856-C13-08), CAM (08.1/0054/01), European Union (TELOSENS FIGH-CT-2002-00217, INTACT LSHC-CT-2003-506803, ZINCAGE FOOD-CT-2003-506850, RISC-RAD FI6R-CT-2003-508842) and the Josef Steiner Award 2003. S.F. is a Predoctoral Fellow of the Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III.
Conflict of Interest Statement: None declared.
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Received January 24, 2005;
revised April 15, 2005;
accepted April 19, 2005.