The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences

Sun, Liou, Y.;Bartke,, Andrzej

doi: 10.1093/gerona/62.2.117pmid: 17339637

Abstract Ames dwarf mice live considerably longer than normal animals, exhibit apparently normal cognitive functions, and maintain them into advanced age. Neurogenesis occurs throughout adult life span in the dentate gyrus of mammalian hippocampus and has been suggested to play an important role in cognitive function. We now report that the total number of bromodeoxyuridine (BrdU)-labeled cells in this brain region in aged Ames dwarf mice was not different from that in aged normal mice, whereas the fraction of newly generated neurons was significantly increased by monitoring BrdU labeling and cell marker expression. Evidence of activation of antiapoptosis signal transduction cascade was also found in the hippocampus of aged dwarf mice. Together with previous findings, the results may suggest that an increase in hippocampal insulin-like growth factor-I protein expression and subsequent activation of antiapoptotic signaling might contribute to survival of newly born neurons and subsequently to the delay of cognitive loss during aging in these long-lived dwarf mice. ONE of the most interesting topics in neuroscience is the birth of new neurons that occurs in discrete regions of adult mammalian brain, namely, the subgranular zone (SGZ) of the hippocampal dentate gyrus and the subventricular zone (SVZ) (1–3). In these regions, regulation of adult neurogenesis is often demonstrated by systemic injection of an exogenous synthetic (S)-phase marker such as bromodeoxyuridine (BrdU), the thymidine analogue, which is taken up by dividing cells during the S phase (4). This technique has shown that neural progenitor cells arising from the SGZ migrate into the granule cell layer (GCL) where they differentiate into new neurons and integrate into the local network, receiving afferents and sending out functional efferents (5), whereas SVZ-born neurons are destined for the olfactory bulb (6). The process of neurogenesis in the adult dentate gyrus can be divided into different phases including proliferation, differentiation, and migration. These newly born neurons may have a physiological role, because blockade of hippocampal neurogenesis is reported to inhibit hippocampus-dependent learning (7), and because reducing the population of new interneurons in the olfactory bulb impairs odor discrimination (8). Although a growing number of pharmacological and environmental manipulations have been shown to influence adult neurogenesis, the functional implication of the newly born neurons remains poorly understood. Hippocampus is one of the brain regions most susceptible to aging (9). The aging hippocampal formation is affected by a number of structural and functional changes, including reduced expression of growth factor and steroid receptors (10) and impairment of hippocampal long-term potentiation (LTP) induction under certain conditions (11,12). Neurogenesis in the dentate gyrus occurs throughout the life span, but decreases with increasing age in rats, mice, monkeys, and humans (13–15). Decreased hippocampal neurogenesis may be involved in age-related cognitive deficits because of its proposed role in learning and memory function (7). In contrast, hippocampal neurogenesis is reported to be increased in aged mice living in an enriched environment (16). Enhanced neurogenesis is accompanied by improved learning, exploratory behavior, and locomotor activity (16). Therefore, it is suggested that restoring hippocampal neurogenesis may be a strategy for reversing age-related cerebral dysfunction. In addition, growth factors stimulate proliferation of neuronal precursors from adult brain in vitro and in vivo. For example, fibroblast growth factor (FGF)-2 is expressed in adult rodent SVZ and SGZ (17) and intracerebroventricular (ICV) infusions of FGF-2 upregulate dentate neurogenesis significantly in the aged brain and also increase neurogenesis in SVZ and neuronal migration to the olfactory bulb (18,19). Insulin-like growth factor (IGF)-I is another interesting factor, given its pattern of regulation across the life span and its ability to stimulate neurogenesis (20). In addition, although IGF-I levels decline in aged rats, their restoration increases neurogenesis in aged rat brain (21). The aim of the present study was to investigate the effects of aging and dwarfism on neurogenesis in the dentate gyrus of Ames dwarf mice. The Ames dwarf mouse was selected because of its special phenotypic characteristics of delayed aging. Ames dwarf mice are homozygous for a loss-of-function mutation at the prop-1 locus (Prop1df) (22). The mutation impairs the development of the anterior pituitary resulting in dramatic depletion or absence of growth hormone-, thyroid-stimulating hormone-, and prolactin (PRL)-producing cells in the adenohypophysis and primary deficiency of the corresponding hormones (23). Circulating IGF-I is undetectable in Ames dwarf mice because of the primary growth hormone deficiency (24,25). However, these mice have a significantly increased life span and delayed onset of age-related changes including brain aging, and maintain physiological function at more youthful levels (26). Recently, we found that old Ames dwarf mice had elevated levels of growth hormone and IGF-I in the hippocampus, and increased neurogenesis was found in the dentate gyrus of young adult dwarf mice (27,28). Materials and Methods Animals Young adult (3-month-old) and aged (20-month-old) male Ames dwarf mice and age-matched normal male mice (n = 8 from each group) were used in the study. Normal mice were the siblings of the dwarfs. Animal protocols were reviewed and approved by the Southern Illinois University Animal Care and Use Committee. All animals had access to water and food ad libitum and were housed under a 12-hour light/dark cycle. Antibodies Primary antibodies and their final dilutions were: Rat-BrdU, 1:500 (Accurate Chemical, Westbury, NY); polyclonal goat antigrowth hormone immunoglobulin G (IgG), 1:300 (Santa Cruz Biotechnology, Santa Cruz, CA); polyclonal rabbit anti-IGF-I IgG, 1:200 (Santa Cruz Biotechnology); polyclonal rabbit antibodies against phospho-Bad (Ser-136), against Bad, against Bcl-2, 1:250 (Santa Cruz Biotechnology); mouse monoclonal anti-β-actin IgG, 1:2000 (Sigma, St. Louis, MO); mouse anti-NeuN IgG, 1:100 (Chemicon, Temecula, CA); anti-DCX, 1:100 (Santa Cruz Biotechnology); mouse anti-β-III tubulin, 1:1000 (Promega, Madison, WI); rabbit antiglial fibrillary acid protein (GFAP) IgG, 1:300 (Dako, Carpinteria, CA); and rabbit anti-Akt, phospho-Akt, caspase-3, and caspase-9, 1:500 (Cell Signaling, Danvers, MA). The following secondary antibodies from Jackson ImmunoResearch (West Grove, PA) were used: FITC-conjugated donkey-mouse IgG, 1:500; FITC-conjugated donkey-rabbit IgG, 1:500; rhodamine Red-X-conjugated donkey-goat IgG, 1:500; rhodamine Red-X-conjugated donkey-mouse IgG, 1:500; rhodamine Red-X-conjugated donkey-rat IgG, 1:500; or biotin-conjugated donkey-rat IgG, 1:500. BrdU Injections Cell proliferation was measured by the incorporation of the thymidine analogue BrdU, which is incorporated into the DNA of dividing cells in immunohistochemically detectable quantities during the S phase of cell division (3). Each animal received intraperitoneal injections of BrdU (Sigma) (50 mg/kg at a concentration of 10 mg/mL in 0.9% NaCl) daily for 7 consecutive days, and all animals were then killed 24 hours after the last BrdU injection. The animals were anesthetized by intraperitoneal injection of ketamine (50 mg/kg) and xylazine (10 mg/kg) and perfused with 0.9% NaCl followed by 4% paraformaldehyde. Histology After perfusion, brains were removed and post-fixed overnight in 4% paraformaldehyde at 4°C. The brains were then equilibrated in 30% sucrose for an additional 24 hours. Sections 40 μm thick were then prepared in the coronal plane using a freezing microtome. Immunostaining Diaminobenzidine (DAB) histochemical staining for BrdU was used for stereological quantification of labeled cells. Free-floating sections were treated with 0.6% H2O2 in phosphate-buffered saline (PBS) for 30 minutes to block endogenous peroxidase. For DNA denaturation, sections were incubated for 2 hours in 50% formamide/2× standard saline citrate (SSC) (0.3 M NaCl and 0.03 M sodium citrate) at 65°C, rinsed for 5 minutes in 2× SSC, incubated for 30 minutes in 2N HCl at 37°C, and rinsed for 10 minutes in 0.1 M boric acid, pH 8.5. Several rinses in PBS were followed by incubation in PBS/0.1% Triton X-100/3% normal horse serum (PBS-Ths) for 30 minutes and incubation with mouse anti-BrdU antibody in PBS-Ths overnight at 4°C. After being rinsed in PBS, sections were incubated for 1 hour with biotinylated horse antimouse antibody. Tissues were washed in PBS and then incubated for 30 minutes in pre-assembled biotin–avidin–horseradish peroxidase complex according to the manufacturer's recommendations (ABC Elite; Vector Laboratories, Burlingame, CA). Sections were then washed and incubated in DAB solution for sufficient time to develop intense brown staining in BrdU-labeled nuclei. Rinsed sections were then mounted on uncoated Superfrost slides (Fisher Scientific, Santa Clara, CA), dried, dehydrated through a graded alcohol series into xylene, and cover-slipped with Permount mounting medium (Fisher Scientific). Double-Labeling Immunofluorescence Sections were treated for DNA denaturation as described above, followed by several rinses in PBS and incubation in PBS/0.1% Triton X-100/3% normal donkey serum (Jackson ImmunoResearch) for 30 minutes. For labeling of BrdU and cell specific markers, sections were incubated with monoclonal rat anti-BrdU and anti-NeuN or anti-GFAP at 4°C overnight followed by antirat rhodamine Red-X IgG and antimouse or antirabbit FITC IgG for 2 hours at room temperature and after thorough removal of primary antibodies. Sections were washed and wet mounted, then dried in the dark. Fluorescent mounting medium was applied prior to placing coverslips onto the slides. For visualization and photography, specimens were observed under a confocal microscope. Stereology and Quantification of BrdU-Labeled Cells Stereology was performed on tissues stained for BrdU using DAB histochemistry as described above. From all animals, every sixth section (240 μm apart) of the series was stained for BrdU using the peroxidase method. Positive cells were counted using a 40× objective (Leica, Exton, PA) throughout the rostrocaudal extent of the GCL. Stereological principles and analyses were conducted as described by Williams and Rakic (29), and the optical dissector method was modified in that cells appearing sharp in the uppermost focal plane were not counted. Resulting numbers were multiplied by 6 to obtain the estimated total number of BrdU-positive cells per GCL. BrdU-positive counts were limited to the hippocampal granular cell layer and adjacent hilar SGZ margin using the fractionator probe (Stereo Investigator software; MBF Bioscience, Williston, VT). Fluorescently labeled tissue sections were evaluated using a Zeiss 510 confocal laser-scanning device attached to a Zeiss-Axiovert microscope using LSM510 software. Appropriate gain and black-level settings were determined on control tissues stained with secondary antibodies alone. Upper and lower thresholds were set using the range indicator function to minimize saturation in positive cells. To assess the phenotype of BrdU-labeled cells, the numbers of BrdU- and/or NeuN-positive cells were scored within the dentate GCL including the adjacent hilar margin or SGZ in the sections taken from the rostral, mid, and caudal hippocampus. Western Blot Analysis Hippocampi were homogenized in 10 vol of solubilization buffer (1% Triton, 100 mM HEPES, 100 mM sodium pyrophosphate, 100 mM sodium fluoride, 10 mM EDTA, 10 mM sodium vanadate, 2 mM phenylmethylsulfonyl fluoride [PMSF], and 0.035 TIU/mL aprotinin [pH 7.4]) at 4°C. Protease and phosphatase inhibitors and 1% of Triton-X 100 (Sigma) were added. After mixing, homogenates were centrifuged at 13,000 g for 30 minutes, and the supernatant was removed. Total hippocampal proteins were determined by the colorimetric method using a bovine serum albumin (BSA) protein assay reagent (Pierce, Rockford, IL). Protein levels were quantified by Western blotting using antibodies specific for the respective proteins. In brief, homogenate proteins (40 μg/well) from mouse hippocampus were subjected to sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and were then transferred to nitrocellulose membrane by electroblotting at 80 V for 80 minutes. After the transfer, membranes were washed using Tris-buffered saline (TBS) and blocked with 3% BSA in TBS for 1 hour at room temperature. After blocking, the membrane was incubated in appropriately diluted primary antibody (see reagents for different dilution) overnight at 4°C. After incubation and three washes, the membrane was probed with the specified horseradish peroxidase-linked second antibody for 2 hours at 20°C. Blots were processed using enhanced chemiluminescence (ECL) (Amersham Pharmacia Biotech, Piscataway, NJ), and immunoreactive bands were viewed on an image analysis system consisting of a monochrome video camera (Dage MTI CCD-72; Dage-MTI, Michigan City, IN) connected to an Inter Focus Ltd. image analysis system (MCID Imaging Research Inc. [now part of GE Healthcare], St. Catharines, Canada). The signals of the proteins were normalized to β-actin signals. Quantification of immunoblot signals was performed using GeneTools ImageQuant software (Syngene Inc., Frederick, MD). Statistical Analyses Results are presented as mean ± standard error of the mean (SEM). The statistical evaluation was performed using two-factor analysis of variance (ANOVA; phenotype and age), followed by Fisher's protected least significant difference test as a post hoc test. Student's t test was used when two groups were analyzed. Values of p <.05 were considered significant. All statistical analyses were performed using StatView 5.0 software (Abacus Concepts, Berkeley, CA). Results Neurogenesis and Aging To study the impact of aging on neurogenesis in the dentate gyrus, young adult (3 months old) and aged (20 month old) dwarf and normal mice were given BrdU for 7 consecutive days and killed 24 hours later, and the number of BrdU-labeled cells in the dentate gyrus of the hippocampus between different groups was compared by immunohistochemistry and cell counting. BrdU-labeled cells were detected in dentate gyrus (Figure 1) in both young adult and aged dwarf and normal mice. Cell proliferation was predominantly confined to the subgranular layer (Figure 1). The BrdU-positive nuclei were often clustered in the subgranular layer and exhibited variable shapes (Figure 1E). However, the majority of BrdU-labeled cells were located inside the GCL and characterized by large round nuclei (Figure 1F). Qualitative assessment indicated a dramatic decrease in total number of BrdU-labeled cells in the aged mice when compared to young mice (in both dwarf and normal animals) (Figure 2). The results were consistent with previous evidence that there is an age-related decline in proliferation of newly born cells in the dentate gyrus of mouse hippocampus (30). Consistent with our previous report (28), there is a robust increase in numbers of newly generated cells (BrdU positive) in the dentate gyrus of young adult Ames dwarf mice compared with young normal mice. However, there was no significant difference of total number of BrdU-labeled cells in the dentate gyrus area between aged dwarf and aged normal mice (Figure 2). To determine whether the BrdU-labeled cells that we observed expressed phenotypic neuronal features, we double-labeled brain sections with antibodies against BrdU and against the cell-type marker proteins NeuN or GFAP, or other specific markers such as Doublecortin. A considerable decrease was observed in the fraction of BrdU/NeuN-colabeled cells between the young and old groups in both dwarf and normal animals. Compared with that of young normal mice, dentate gyrus of young Ames dwarf mice has more newly born neurons (BrdU/NeuN double-labeled). Interestingly, more BrdU/NeuN-positive cells were detected in the dentate gyrus area of aged Ames dwarf mice than in that of the aged normal animals (Figure 3). For both normal and dwarf mice, a very small number of BrdU-positive cells in the GCL colocalized with GFAP, whereas many BrdU-positive cells remaining in the hilus were GFAP-positive. In addition, BrdU-labeled cells were seen also in several other brain areas including hippocampal regions CA1 and CA3 and cerebral cortex, but these cells were not immunoreactive with the cell markers in current study. Survival or Death Consistent with a previous study (27), aged Ames dwarf mice had elevated levels of IGF-I and increased phosphorylation of Akt locally in the hippocampus (Figures 4 and 5) despite having virtually no IGF-I in peripheral circulation. One reported mechanism of action of IGF-I is to increase the phosphorylated state of Akt, a protein kinase activated by insulin and various growth factors that is involved in blocking proapoptotic pathways through receptor-mediated phosphatidylinositol 3-kinase signaling (31). Bcl-2 protein has been proposed to serve as a signaling receptor for intracellular organelles in which serine phosphorylation of both apoptotic and antiapoptotic family members are hypothesized to affect cell survival. Numerous studies have reported on the ability of enhanced Bcl-2 expression to promote the survival of various cell types, as well as the ability to block apoptotic cell death (32–34). Western blot analysis of immunoreactive Bcl-2 protein with a molecular mass of 26 kd from proteins extracted from aged dwarf and normal mice hippocampus indicated that that the Bcl-2 protein expression was significantly (p <.05) more abundant in dwarf mice relative to that in normal mice (Figure 6). Bad is a pro-apoptotic member of the Bcl-2 family of proteins. Phosphorylation of Bad is a key step in the signaling events that come into play in the rescue of cells from apoptosis. As shown in Figure 7, phosphorylation of Bad (at SEM136) was significantly increased in the hippocampus of old dwarf mice compared with old normal mice. The total Bad protein did not differ between the two groups. Both IGF-I and phosphorylated Akt have been shown to prevent cleavage of caspase-9, thereby inhibiting apoptosis. Here we found that there is a significantly (p <.05) reduced amount of caspase-9 cleavage and subsequently decreased cleaved 37 kd and 39 kd subunits in the dwarf hippocampus (Figure 8), indicating that caspase activation involved in the apoptotic pathway was also attenuated. Thus the results suggest that the presence of a greater number of newly born neurons in the dwarf mice compared with normal mice may be due to survival effects of IGF-I and subsequent activation of antiapoptotic pathway. Discussion In the present study, we have investigated the impact of aging and dwarfism on neurogenesis in the dentate gyrus of long-lived Ames dwarf and normal mice. It has been demonstrated that the incorporation of BrdU into hippocampal granule cells might reflect the number of proliferating progenitor cells. The thymidine analog BrdU is incorporated into DNA and can be detected immunohistochemically in cell progeny (35). Bioavailability of BrdU after injection has been estimated to last ∼2 hours, and BrdU labels DNA only during the S phase, which has been estimated to last ∼8 hours (36,37). Thus, the regimen used in this study is not likely to cause an overestimation of the number of proliferating cells, because one injection cannot label all dividing cells during a 24-hour period. Age-Related Changes in Neurogenesis Neurogenesis persists in the hippocampal formation of old mice. In all four groups, proliferating cells (Figure 1) were located in the SGZ, and differentiating cells migrated into the GCL. BrdU-labeled cells expressed the neuronal marker NeuN 1 week after injection of BrdU (Figure 3). Our stereological findings of a significant age-dependent decline in neurogenesis in the GCL parallel previous findings in mice (30) and rats (13,15). The reduction of neurogenesis with aging was possibly caused by an age-related decrease in the number of newly generated cells. Several mechanisms could be responsible for the reduction in hippocampal neurogenesis during aging. Neuronal precursor cells could either change their cell-specific fate, leading to their becoming glial cells, or alter their mitotic activity, leading to a reduction of the actual number of newly born cells. Also, the newly born cells could die before differentiating into granule neurons. The progressive decline of precursor cell proliferation during aging raises the question of whether the precursor cells become unresponsive to environmental cues or whether the environment does not provide the stimuli for further proliferation. The newly born cells either die or lose their appropriate signaling mechanism (such as growth factors) for the mitotic stimulus. Moreover, during aging the local environment for the precursor cell changes so that the mitotic stimulus is no longer provided. Several factors that regulate neuronal birth in the adult dentate gyrus were recently described (38–40). Mayo and colleagues (41) reported that infusions of the neurosteroid pregnenolone sulfate (PREG-S) will reverse memory impairments in aged rats. The possible role of oxidative stress in increasing neuronal vulnerability in aged hippocampal neurons has also been discussed (42). Interestingly, the effects of dietary restriction on neural progenitor/stem cells have been described (43). Dietary restriction induced an increase in newly generated neural cells in the adult brain, an increase in expression of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), and an increase in the resistance of neurons to dysfunction and apoptosis (43). Glucocorticoids have also been shown to inhibit neurogenesis in the dentate gyrus (38,44). Interestingly, corticosterone levels were highest in young normal female mice and lowest in young normal male mice, with the levels in Ames dwarf mice falling in the middle (45). However, plasma corticosterone levels in old Ames dwarf mice did not differ from the values measured in normal animals (45). Further studies will be needed to relate the levels of glucocorticoids to the degree of neurogenesis in Ames dwarf mice. Glutamatergic afferents to the dentate gyrus also limit adult neurogenesis, as does psychosocial stress (46). Peripheral administration of IGF-I increases cellular proliferation in the dentate subgranular proliferative zone (47). Interestingly, in current studies we found that newly born cell differentiation and survival were not changed in parallel with proliferation in aged Ames dwarf mice; the total number of BrdU-labeled cells was not different from that in the aged normal mice, whereas the fraction of newly generated neurons (BrdU and NeuN double-labeled) was significantly increased. This finding might indicate that the progression rate of neural stem cells and/or neuron precursors to mature neurons is significantly increased in Ames dwarf mice. However, the total number of newly born neuronal precursors is similar in both normal and dwarf mice. There is also a possibility that an effect on cell survival/apoptosis could be responsible for the increase in newly born neurons observed in dentate gyrus of aged Ames dwarf mice. Little is known about the role of apoptosis in the regulation of adult neurogenesis during aging, although it is plausible that the decrease in BrdU-positive cells after injection is attributable to elimination by programmed cell death. Quantitatively, the loss of BrdU-positive cells might reflect an overestimation of apoptosis, because BrdU itself could damage some labeled cells. However, cumulative labeling with BrdU over a number of days might reduce the temporal resolution in terms of distinguishing between proliferation and survival effects. So with our current BrdU injection protocol, the number of newly generated cells or neurons represents a combination of the proliferation rate, the differentiation rate, and net cell survival/apoptosis. It will be important to distinguish proliferation from survival/differentiation effects by using different BrdU strategies (48,49). Future studies will focus on these issues. Activation of Antiapoptotic Pathway: Survival of the Newly-Generated Neurons Cell birth and cell death appear closely associated in the dentate gyrus as continuous cell turnover takes place. Consequently, the dentate gyrus consists of a diverse and heterogeneous group of mature and developing cells. Furthermore, this turnover is highly sensitive to various hormonal and environmental stimuli (50,51). For example, removal of steroid hormones by adrenalectomy induces apoptosis in the dentate gyrus, but at the same time increases the division of immature cells (52). In contrast, stress reduces new cell birth (46,53). Apoptosis is an important biological mechanism through which tissues shape normal developmental patterns and adapt to new environmental changes. The occurrence of apoptosis is regulated by serial alterations of intracellular molecules in response to extracellular signaling changes. A key event in early stages of apoptosis is mitochondrial permeability transition, which may release cytochrome c and, in turn, activate caspase 3 (54). Homodimers of Bcl-2 associate with mitochondrial membrane and stabilize membrane permeability (55). The protective effect of Bcl-2 on mitochondria permeability is lost when Bcl-2 homodimers are sequestered by the formation of Bcl-2/Bax heterodimer (55,56). Bad, which is regarded as a heterodimeric partner for Bcl-XL and Bcl-2, displaces Bax and promotes cell death (57). In the current study, we showed an increased level of Bcl-2 protein, increased phosphorylation of Bad, and inhibition of caspase-9 cleavage (a pattern of activation of antiapoptosis signal transduction) in the hippocampus of dwarf mice compared with normal mice. Recent evidence indicates that the Akt-phosphoinositide 3-kinase pathway plays an important role in IGF-I promotion of neuronal survival by increasing the expression of the Bcl-2 family proteins and decreasing caspase activity (58–60). Interestingly, recent reports indicate that IGF-I could attenuate apoptosis in hippocampal neurons after ischemia (61), and suggest that the observed increase in the fraction of newly born neurons after IGF-I treatment is in part due to an effect on cell survival. In summary, the above results may suggest that increase in hippocampal IGF-I protein expression and subsequent activation of Akt-phosphoinositide 3-kinase and antiapoptosis signal transduction cascade might contribute to a delay of the age-related cognitive loss in these long-lived mice compared with normal animals. Taken together, we speculate that aging would influence the balance between neurogenesis and apoptosis in the mouse hippocampus as well as the process of the regulation of the neuronal progenitors. Locally produced IGF-I might work primarily as a promoting factor to increase neurogenesis in the dentate gyrus area of the hippocampus in early adulthood; however, it might function mainly as a survival factor to inhibit neuronal death during aging in the central nervous system. Decision Editor: Huber R. Warner, PhD Figure 1. Open in new tabDownload slide Age-dependent proliferation and survival of bromodeoxyuridine (BrdU)-labeled cells in the dentate gyrus. Ames dwarf (A and C) and normal mice (B and D) received BrdU injections on 7 consecutive days and were analyzed 24 hours later. Although BrdU-positive cells exhibit a variety of shapes (E), most cells are predominantly aligned and clustered at the hilar-granule cell border revealing large round nuclei with a chromatin structure similar to mature granular cells (F). Note the dramatic decrease in BrdU-positive cells in 20-month-old mice (C and D) compared to 3-month-old mice (A and B). Scale bars are 60 μm (A–D) and 10 μm (E and F) Figure 1. Open in new tabDownload slide Age-dependent proliferation and survival of bromodeoxyuridine (BrdU)-labeled cells in the dentate gyrus. Ames dwarf (A and C) and normal mice (B and D) received BrdU injections on 7 consecutive days and were analyzed 24 hours later. Although BrdU-positive cells exhibit a variety of shapes (E), most cells are predominantly aligned and clustered at the hilar-granule cell border revealing large round nuclei with a chromatin structure similar to mature granular cells (F). Note the dramatic decrease in BrdU-positive cells in 20-month-old mice (C and D) compared to 3-month-old mice (A and B). Scale bars are 60 μm (A–D) and 10 μm (E and F) Figure 2. Open in new tabDownload slide Stereological estimation of the total number of bromodeoxyuridine (BrdU)-positive cells in the dentate gyrus during aging. Results revealed that there was an age-related decrease in the number of newly born cells in the granule cell layer (GCL) in both dwarf and normal mice. More BrdU-labeled cells were found in young Ames dwarf GCL than in normal mice. Interestingly, however, in the GCL region, no difference was seen between aged Ames dwarf and aged normal mice. Asterisks indicate significant differences from young normal mice (*p <.05; **p <.005) Figure 2. Open in new tabDownload slide Stereological estimation of the total number of bromodeoxyuridine (BrdU)-positive cells in the dentate gyrus during aging. Results revealed that there was an age-related decrease in the number of newly born cells in the granule cell layer (GCL) in both dwarf and normal mice. More BrdU-labeled cells were found in young Ames dwarf GCL than in normal mice. Interestingly, however, in the GCL region, no difference was seen between aged Ames dwarf and aged normal mice. Asterisks indicate significant differences from young normal mice (*p <.05; **p <.005) Figure 3. Open in new tabDownload slide Cellular phenotyping of bromodeoxyuridine (BrdU)-positive cells. Top: BrdU immunofluorescence (red) was combined with the neuronal marker NeuN (green), and colocalization was assessed with confocal scanning microscopy. In both aged normal (A) and aged dwarf mice (B), BrdU-labeled cells were positive for the neuronal marker NeuN (arrow). Note the variety of BrdU labeling, ranging from dense stained neurons to only partly labeled neurons, indicating a dilution effect of repeated stem cell divisions. Scale bar = 30 μm. Bottom: Results of quantification of BrdU/NeuN double-positive cells in the granular cell layer (GCL) of the dentate gyrus of aged normal mice and aged dwarf mice. Asterisks indicate significant differences (*p <.05) Figure 3. Open in new tabDownload slide Cellular phenotyping of bromodeoxyuridine (BrdU)-positive cells. Top: BrdU immunofluorescence (red) was combined with the neuronal marker NeuN (green), and colocalization was assessed with confocal scanning microscopy. In both aged normal (A) and aged dwarf mice (B), BrdU-labeled cells were positive for the neuronal marker NeuN (arrow). Note the variety of BrdU labeling, ranging from dense stained neurons to only partly labeled neurons, indicating a dilution effect of repeated stem cell divisions. Scale bar = 30 μm. Bottom: Results of quantification of BrdU/NeuN double-positive cells in the granular cell layer (GCL) of the dentate gyrus of aged normal mice and aged dwarf mice. Asterisks indicate significant differences (*p <.05) Figure 4. Open in new tabDownload slide Hippocampal insulin-like growth factor (IGF)-I protein expression of aged Ames dwarf and normal mice. Top: Autoradiography of Western blot. Bottom: Densitometric analysis of Western blots. β-Actin signal was used to normalize data. Each lane of the blots represents materials from a different normal or dwarf mouse. *p <.05. Representative blot is shown Figure 4. Open in new tabDownload slide Hippocampal insulin-like growth factor (IGF)-I protein expression of aged Ames dwarf and normal mice. Top: Autoradiography of Western blot. Bottom: Densitometric analysis of Western blots. β-Actin signal was used to normalize data. Each lane of the blots represents materials from a different normal or dwarf mouse. *p <.05. Representative blot is shown Figure 5. Open in new tabDownload slide Increased activation of phosphoinositide 3-kinase-Akt (P-AKT) pathway in the hippocampus of aged Ames dwarf and aged normal mice. Phosphorylation of Akt in the hippocampus of Ames dwarf and normal mice. Top: Autoradiography of Western blot of phosphorylated Akt and total Akt protein. Bottom: Densitometric analysis of Western blots. Values are expressed as means ± standard error of the mean. Asterisk indicates significant differences (*p <.05) Figure 5. Open in new tabDownload slide Increased activation of phosphoinositide 3-kinase-Akt (P-AKT) pathway in the hippocampus of aged Ames dwarf and aged normal mice. Phosphorylation of Akt in the hippocampus of Ames dwarf and normal mice. Top: Autoradiography of Western blot of phosphorylated Akt and total Akt protein. Bottom: Densitometric analysis of Western blots. Values are expressed as means ± standard error of the mean. Asterisk indicates significant differences (*p <.05) Figure 6. Open in new tabDownload slide Bcl-2 protein expression in the hippocampus of aged Ames dwarf and aged normal mice. Top: Autoradiography of Western blot of Bcl-2 protein. Bottom: Densitometric analysis of Western blots. Values are expressed as means ± standard error of the mean. Asterisk indicates significant differences (*p <.05). β-actin signal used to normalize the data Figure 6. Open in new tabDownload slide Bcl-2 protein expression in the hippocampus of aged Ames dwarf and aged normal mice. Top: Autoradiography of Western blot of Bcl-2 protein. Bottom: Densitometric analysis of Western blots. Values are expressed as means ± standard error of the mean. Asterisk indicates significant differences (*p <.05). β-actin signal used to normalize the data Figure 7. Open in new tabDownload slide Phosphorylation of Bad in the hippocampus of aged Ames dwarf and normal mice. Top: Autoradiography of Western blot of phosphorylated Bad and total Bad protein. Bottom: Densitometric analysis of Western blots. Values are expressed as means ± standard error of the mean. Asterisk indicates significant differences (*p <.05) Figure 7. Open in new tabDownload slide Phosphorylation of Bad in the hippocampus of aged Ames dwarf and normal mice. Top: Autoradiography of Western blot of phosphorylated Bad and total Bad protein. Bottom: Densitometric analysis of Western blots. Values are expressed as means ± standard error of the mean. Asterisk indicates significant differences (*p <.05) Figure 8. Open in new tabDownload slide Cleavage of caspase-9 in the hippocampus of aged Ames dwarf and normal mice. The amount of caspase-9 cleavage was significantly reduced in dwarf hippocampus. The cleaved 37 kd and 39 kd subunits were decreased in dwarf mice by more than 50% compared to those in the normal mice (densitometric analysis of Western blot) (*p <.05) Figure 8. Open in new tabDownload slide Cleavage of caspase-9 in the hippocampus of aged Ames dwarf and normal mice. The amount of caspase-9 cleavage was significantly reduced in dwarf hippocampus. The cleaved 37 kd and 39 kd subunits were decreased in dwarf mice by more than 50% compared to those in the normal mice (densitometric analysis of Western blot) (*p <.05) This work was supported by National Institutes of Health Grant AG019899 and the Ellison Medical Foundation Grant (to A.B.), by Glenn Foundation/AFAR Scholarships (to L.S.), and by the Southern Illinois University Geriatrics Medicine and Research Initiative. References 1 Altman J, Das GD. Autoradiographic and histological studies of postnatal neurogenesis. I. A longitudinal investigation of the kinetics, migration and transformation of cells incorporating tritiated thymidine in neonate rats, with special reference to postnatal neurogenesis in some brain regions. J Comp Neurol. 1966 ; 126 : 337 -389. 2 Gage FH. Mammalian neural stem cells. Science. 2000 ; 287 : 1433 -1438. 3 Altman J, Das GD. Autoradiographic and histological evidence of postnatal hippocampal neurogenesis in rats. J Comp Neurol. 1965 ; 124 : 319 -335. 4 Miller MW, Nowakowski RS. Use of bromodeoxyuridine-immunohistochemistry to examine the proliferation, migration and time of origin of cells in the central nervous system. Brain Res. 1988 ; 457 : 44 -52. 5 Cameron HA, Woolley CS, McEwen BS, Gould E. Differentiation of newly born neurons and glia in the dentate gyrus of the adult rat. Neuroscience. 1993 ; 56 : 337 -344. 6 Seaberg RM, van der KD. Stem and progenitor cells: the premature desertion of rigorous definitions. Trends Neurosci. 2003 ; 26 : 125 -131. 7 Shors TJ, Miesegaes G, Beylin A, Zhao M, Rydel T, Gould E. Neurogenesis in the adult is involved in the formation of trace memories. Nature. 2001 ; 410 : 372 -376. 8 Gheusi G, Cremer H, McLean H, Chazal G, Vincent JD, Lledo PM. Importance of newly generated neurons in the adult olfactory bulb for odor discrimination. Proc Natl Acad Sci U S A. 2000 ; 97 : 1823 -1828. 9 Geinisman Y, Detoledo-Morrell L, Morrell F, Heller RE. Hippocampal markers of age-related memory dysfunction: behavioral, electrophysiological and morphological perspectives. Prog Neurobiol. 1995 ; 45 : 223 -252. 10 Bhatnagar M, Cintra A, Chadi G, et al. Neurochemical changes in the hippocampus of the brown Norway rat during aging. Neurobiol Aging. 1997 ; 18 : 319 -327. 11 Murray CA, Lynch MA. Dietary supplementation with vitamin E reverses the age-related deficit in long term potentiation in dentate gyrus. J Biol Chem. 1998 ; 273 : 12161 -12168. 12 Rosenzweig ES, Rao G, McNaughton BL, Barnes CA. Role of temporal summation in age-related long-term potentiation-induction deficits. Hippocampus. 1997 ; 7 : 549 -558. 13 Cameron HA, McKay RD. Restoring production of hippocampal neurons in old age. Nat Neurosci. 1999 ; 2 : 894 -897. 14 Gould E, Reeves AJ, Fallah M, Tanapat P, Gross CG, Fuchs E. Hippocampal neurogenesis in adult Old World primates. Proc Natl Acad Sci U S A. 1999 ; 96 : 5263 -5267. 15 Kuhn HG, Dickinson-Anson H, Gage FH. Neurogenesis in the dentate gyrus of the adult rat: age-related decrease of neuronal progenitor proliferation. J Neurosci. 1996 ; 16 : 2027 -2033. 16 Kempermann G, Gast D, Gage FH. Neuroplasticity in old age: sustained fivefold induction of hippocampal neurogenesis by long-term environmental enrichment. Ann Neurol. 2002 ; 52 : 135 -143. 17 Goldman SA. Adult neurogenesis: from canaries to the clinic. J Neurobiol. 1998 ; 36 : 267 -286. 18 Kuhn HG, Winkler J, Kempermann G, Thal LJ, Gage FH. Epidermal growth factor and fibroblast growth factor-2 have different effects on neural progenitors in the adult rat brain. J Neurosci. 1997 ; 17 : 5820 -5829. 19 Jin K, Sun Y, Xie L, et al. Neurogenesis and aging: FGF-2 and HB-EGF restore neurogenesis in hippocampus and subventricular zone of aged mice. Aging Cell. 2003 ; 2 : 175 -183. 20 Anderson MF, Aberg MA, Nilsson M, Eriksson PS. Insulin-like growth factor-I and neurogenesis in the adult mammalian brain. Brain Res Dev Brain Res. 2002 ; 134 : 115 -122. 21 Lichtenwalner RJ, Forbes ME, Bennett SA, Lynch CD, Sonntag WE, Riddle DR. Intracerebroventricular infusion of insulin-like growth factor-I ameliorates the age-related decline in hippocampal neurogenesis. Neuroscience. 2001 ; 107 : 603 -613. 22 Sornson MW, Wu W, Dasen JS, et al. Pituitary lineage determination by the Prophet of Pit-1 homeodomain factor defective in Ames dwarfism. Nature. 1996 ; 384 : 327 -333. 23 Bartke A, Brown-Borg H, Mattison J, Kinney B, Hauck S, Wright C. Prolonged longevity of hypopituitary dwarf mice. Exp Gerontol. 2001 ; 36 : 21 -28. 24 Chandrashekar V, Bartke A. Induction of endogenous insulin-like growth factor-I secretion alters the hypothalamic-pituitary-testicular function in growth hormone-deficient adult dwarf mice. Biol Reprod. 1993 ; 48 : 544 -551. 25 Bartke A, Chandrashekar V, Dominici F, et al. Insulin-like growth factor 1 (IGF-1) and aging: controversies and new insights. Biogerontology. 2003 ; 4 : 1 -8. 26 Kinney BA, Meliska CJ, Steger RW, Bartke A. Evidence that Ames dwarf mice age differently from their normal siblings in behavioral and learning and memory parameters. Horm Behav. 2001 ; 39 : 277 -284. 27 Sun LY, Al Regaiey K, Masternak MM, Wang J, Bartke A. Local expression of GH and IGF-1 in the hippocampus of GH-deficient long-lived mice. Neurobiol Aging. 2005 ; 26 : 929 -937. 28 Sun LY, Evans MS, Hsieh J, Panici J, Bartke A. Increased neurogenesis in dentate gyrus of long-lived Ames dwarf mice. Endocrinology. 2005 ; 146 : 1138 -1144. 29 Williams RW, Rakic P. Three-dimensional counting: an accurate and direct method to estimate numbers of cells in sectioned material. J Comp Neurol. 1988 ; 278 : 344 -352. 30 Kempermann G, Kuhn HG, Gage FH. Experience-induced neurogenesis in the senescent dentate gyrus. J Neurosci. 1998 ; 18 : 3206 -3212. 31 Brunet A, Datta SR, Greenberg ME. Transcription-dependent and -independent control of neuronal survival by the PI3K-Akt signaling pathway. Curr Opin Neurobiol. 2001 ; 11 : 297 -305. 32 Sentman CL, Shutter JR, Hockenbery D, Kanagawa O, Korsmeyer SJ. bcl-2 inhibits multiple forms of apoptosis but not negative selection in thymocytes. Cell. 1991 ; 67 : 879 -888. 33 Bissonnette RP, Echeverri F, Mahboubi A, Green DR. Apoptotic cell death induced by c-myc is inhibited by bcl-2. Nature. 1992 ; 359 : 552 -554. 34 Hockenbery D, Nunez G, Milliman C, Schreiber RD, Korsmeyer SJ. Bcl-2 is an inner mitochondrial membrane protein that blocks programmed cell death. Nature. 1990 ; 348 : 334 -336. 35 del Rio JA, Soriano E. Immunocytochemical detection of 5′-bromodeoxyuridine incorporation in the central nervous system of the mouse. Brain Res Dev Brain Res. 1989 ; 49 : 311 -317. 36 Nowakowski RS, Lewin SB, Miller MW. Bromodeoxyuridine immunohistochemical determination of the lengths of the cell cycle and the DNA-synthetic phase for an anatomically defined population. J Neurocytol. 1989 ; 18 : 311 -318. 37 Takahashi T, Nowakowski RS, Caviness VS, Jr. BUdR as an S-phase marker for quantitative studies of cytokinetic behaviour in the murine cerebral ventricular zone. J Neurocytol. 1992 ; 21 : 185 -197. 38 Gould E, Woolley CS, McEwen BS. Adrenal steroids regulate postnatal development of the rat dentate gyrus: I. Effects of glucocorticoids on cell death. J Comp Neurol. 1991 ; 313 : 479 -485. 39 Benraiss A, Chmielnicki E, Lerner K, Roh D, Goldman SA. Adenoviral brain-derived neurotrophic factor induces both neostriatal and olfactory neuronal recruitment from endogenous progenitor cells in the adult forebrain. J Neurosci. 2001 ; 21 : 6718 -6731. 40 Jin K, Zhu Y, Sun Y, Mao XO, Xie L, Greenberg DA. Vascular endothelial growth factor (VEGF) stimulates neurogenesis in vitro and in vivo. Proc Natl Acad Sci U S A. 2002 ; 99 : 11946 -11950. 41 Mayo W, Le Moal M, Abrous DN. Pregnenolone sulfate and aging of cognitive functions: behavioral, neurochemical, and morphological investigations. Horm Behav. 2001 ; 40 : 215 -217. 42 Nicolle MM, Gonzalez J, Sugaya K, et al. Signatures of hippocampal oxidative stress in aged spatial learning-impaired rodents. Neuroscience. 2001 ; 107 : 415 -431. 43 Lee J, Seroogy KB, Mattson MP. Dietary restriction enhances neurotrophin expression and neurogenesis in the hippocampus of adult mice. J Neurochem. 2002 ; 80 : 539 -547. 44 Gould E, Woolley CS, Cameron HA, Daniels DC, McEwen BS. Adrenal steroids regulate postnatal development of the rat dentate gyrus: II. Effects of glucocorticoids and mineralocorticoids on cell birth. J Comp Neurol. 1991 ; 313 : 486 -493. 45 Borg KE, Brown-Borg HM, Bartke A. Assessment of the primary adrenal cortical and pancreatic hormone basal levels in relation to plasma glucose and age in the unstressed Ames dwarf mouse. Proc Soc Exp Biol Med. 1995 ; 210 : 126 -133. 46 Gould E. The effects of adrenal steroids and excitatory input on neuronal birth and survival. Ann N Y Acad Sci. 1994 ; 743 : 73 -92. 47 Aberg MA, Aberg ND, Hedbacker H, Oscarsson J, Eriksson PS. Peripheral infusion of IGF-I selectively induces neurogenesis in the adult rat hippocampus. J Neurosci. 2000 ; 20 : 2896 -2903. 48 Hayes NL, Nowakowski RS. Exploiting the dynamics of S-phase tracers in developing brain: interkinetic nuclear migration for cells entering versus leaving the S-phase. Dev Neurosci. 2000 ; 22 : 44 -55. 49 Hayes NL, Nowakowski RS. Dynamics of cell proliferation in the adult dentate gyrus of two inbred strains of mice. Brain Res Dev Brain Res. 2002 ; 134 : 77 -85. 50 Eisch AJ, Barrot M, Schad CA, Self DW, Nestler EJ. Opiates inhibit neurogenesis in the adult rat hippocampus. Proc Natl Acad Sci U S A. 2000 ; 97 : 7579 -7584. 51 van Praag H, Kempermann G, Gage FH. Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci. 1999 ; 2 : 266 -270. 52 Montaron MF, Petry KG, Rodriguez JJ, et al. Adrenalectomy increases neurogenesis but not PSA-NCAM expression in aged dentate gyrus. Eur J Neurosci. 1999 ; 11 : 1479 -1485. 53 Gould E, Tanapat P, McEwen BS, Flugge G, Fuchs E. Proliferation of granule cell precursors in the dentate gyrus of adult monkeys is diminished by stress. Proc Natl Acad Sci U S A. 1998 ; 95 : 3168 -3171. 54 Nicholson DW, Ali A, Thornberry NA, et al. Identification and inhibition of the ICE/CED-3 protease necessary for mammalian apoptosis. Nature. 1995 ; 376 : 37 -43. 55 Reed JC. Regulation of apoptosis by bcl-2 family proteins and its role in cancer and chemoresistance. Curr Opin Oncol. 1995 ; 7 : 541 -546. 56 Herrmann JL, Bruckheimer E, McDonnell TJ. Cell death signal transduction and Bcl-2 function. Biochem Soc Trans. 1996 ; 24 : 1059 -1065. 57 Yang E, Zha J, Jockel J, Boise LH, Thompson CB, Korsmeyer SJ. Bad, a heterodimeric partner for Bcl-XL and Bcl-2, displaces Bax and promotes cell death. Cell. 1995 ; 80 : 285 -291. 58 Dudek H, Datta SR, Franke TF, et al. Regulation of neuronal survival by the serine-threonine protein kinase Akt. Science. 1997 ; 275 : 661 -665. 59 Datta SR, Dudek H, Tao X, et al. Akt phosphorylation of BAD couples survival signals to the cell-intrinsic death machinery. Cell. 1997 ; 91 : 231 -241. 60 Minshall C, Arkins S, Straza J, et al. IL-4 and insulin-like growth factor-I inhibit the decline in Bcl-2 and promote the survival of IL-3-deprived myeloid progenitors. J Immunol. 1997 ; 159 : 1225 -1232. 61 Tagami M, Ikeda K, Nara Y, et al. Insulin-like growth factor-1 attenuates apoptosis in hippocampal neurons caused by cerebral ischemia and reperfusion in stroke-prone spontaneously hypertensive rats. Lab Invest. 1997 ; 76 : 613 -617. Copyright 2007 by The Gerontological Society of America

Liou Y. Sun, Andrzej Bartke

doi: biomedgerontology;62/2/117pmid: N/A

Ames dwarf mice live considerably longer than normal animals, exhibit apparently normal cognitive functions, and maintain them into advanced age. Neurogenesis occurs throughout adult life span in the dentate gyrus of mammalian hippocampus and has been suggested to play an important role in cognitive function. We now report that the total number of bromodeoxyuridine (BrdU)-labeled cells in this brain region in aged Ames dwarf mice was not different from that in aged normal mice, whereas the fraction of newly generated neurons was significantly increased by monitoring BrdU labeling and cell marker expression. Evidence of activation of antiapoptosis signal transduction cascade was also found in the hippocampus of aged dwarf mice. Together with previous findings, the results may suggest that an increase in hippocampal insulin-like growth factor-I protein expression and subsequent activation of antiapoptotic signaling might contribute to survival of newly born neurons and subsequently to the delay of cognitive loss during aging in these long-lived dwarf mice. Copyright 2007 by The Gerontological Society of America « Previous | Next Article » Table of Contents This Article J Gerontol A Biol Sci Med Sci (2007) 62 (2): 117-125. » Abstract Free Full Text (HTML) Free Full Text (PDF) Free Classifications Journal of Gerontology: Biological Sciences Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Similar articles in PubMed Add to my archive Download citation Request Permissions Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Sun, L. Y. Articles by Bartke, A. Search for related content PubMed PubMed citation Articles by Sun, L. Y. Articles by Bartke, A. Related Content Load related web page information Share Email this article CiteULike Delicious Facebook Google+ Mendeley Twitter What's this? Search this journal: Advanced » Current Issue November 2015 70 (11) Alert me to new issues The Journal About the journal Translational Articles Free Editors’ Choice Articles Impact Factor Articles The Journals of Gerontology, Series A Supplements Special Issues Rights & permissions We are mobile – find out more Journal Career Network Published on behalf of The Gerontological Society of America Impact Factor: 5.416 5-Yr impact factor: 5.406 Editorial Boards The Journals of Gerontology, Series A: Biological Sciences Rafael de Cabo, PhD, Editor View full editorial board The Journals of Gerontology, Series A: Medical Sciences Stephen B. Kritchevsky, PhD View full editorial board For the Media GSA Press Room For Authors Instructions to authors Services for authors Submit Now: Biological Sciences Submit Now: Medical Sciences Self-Archiving Policy Online Submission Open access options for authors - visit Oxford Open WhsSvhnOkaAwYG81FJCYgwG7z1LnIP2F true Looking for your next opportunity? Looking for jobs... jQuery_1_11 = jQuery.noConflict(true); Corporate Services What we offer Advertising sales Reprints Supplements Classified Advertising Sales Alerting Services Email table of contents CiteTrack XML RSS feed

Sun, Liou Y.; Bartke, Andrzej

doi: N/Apmid: N/A

Ames dwarf mice live considerably longer than normal animals, exhibit apparently normal cognitive functions, and maintain them into advanced age. Neurogenesis occurs throughout adult life span in the dentate gyrus of mammalian hippocampus and has been suggested to play an important role in cognitive function. We now report that the total number of bromodeoxyuridine (BrdU)-labeled cells in this brain region in aged Ames dwarf mice was not different from that in aged normal mice, whereas the fraction of newly generated neurons was significantly increased by monitoring BrdU labeling and cell marker expression. Evidence of activation of antiapoptosis signal transduction cascade was also found in the hippocampus of aged dwarf mice. Together with previous findings, the results may suggest that an increase in hippocampal insulin-like growth factor-I protein expression and subsequent activation of antiapoptotic signaling might contribute to survival of newly born neurons and subsequently to the delay of cognitive loss during aging in these long-lived dwarf mice.

Jianjun Chen, Damla Senturk, Jane-Ling Wang, Hans-Georg Müller, James R. Carey, Hal Caswell, Edward P. Caswell-Chen

doi: biomedgerontology;62/2/126pmid: N/A

We monitored survival and reproduction of 1000 individuals of Caenorhabditis elegans wild type (N2) and 800 individuals of clk-1 and daf-2 , and used biodemographic analysis to address fitness as the integrative consequence of the entire age-specific schedules of survival and reproduction. Relative to N2, the mutants clk-1 and daf-2 extended average life span by 27% and 111%, respectively, but reduced net reproductive rate by 44% and 18%. The net result of differences in survival and fertility was a significant differential in fitness, with both clk-1 (λ = 2.74) and daf-2 (λ = 3.78) at a disadvantage relative to N2 (λ = 3.85). Demographic life table response experiment (LTRE) analysis revealed that the fitness differentials were due to negative effects in mutants on reproduction in the first 6–7 days of life. Fitness costs in clk-1 and daf-2 of C. elegans are consistent with the theory of antagonistic pleiotropy for the evolution of senescence. Copyright 2007 by The Gerontological Society of America « Previous | Next Article » Table of Contents This Article J Gerontol A Biol Sci Med Sci (2007) 62 (2): 126-135. » Abstract Free Full Text (HTML) Free Full Text (PDF) Free Classifications Journal of Gerontology: Biological Sciences Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Similar articles in PubMed Add to my archive Download citation Request Permissions Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Chen, J. Articles by Caswell-Chen, E. P. Search for related content PubMed PubMed citation Articles by Chen, J. Articles by Senturk, D. Articles by Wang, J. L. Articles by Müller, H. G. Articles by Carey, J. R. Articles by Caswell, H. Articles by Caswell-Chen, E. P. Related Content Load related web page information Share Email this article CiteULike Delicious Facebook Google+ Mendeley Twitter What's this? Search this journal: Advanced » Current Issue November 2015 70 (11) Alert me to new issues The Journal About the journal Translational Articles Free Editors’ Choice Articles Impact Factor Articles The Journals of Gerontology, Series A Supplements Special Issues Rights & permissions We are mobile – find out more Journal Career Network Published on behalf of The Gerontological Society of America Impact Factor: 5.416 5-Yr impact factor: 5.406 Editorial Boards The Journals of Gerontology, Series A: Biological Sciences Rafael de Cabo, PhD, Editor View full editorial board The Journals of Gerontology, Series A: Medical Sciences Stephen B. Kritchevsky, PhD View full editorial board For the Media GSA Press Room For Authors Instructions to authors Services for authors Submit Now: Biological Sciences Submit Now: Medical Sciences Self-Archiving Policy Online Submission Open access options for authors - visit Oxford Open WhsSvhnOkaAwYG81FJCYgwG7z1LnIP2F true Looking for your next opportunity? Looking for jobs... jQuery_1_11 = jQuery.noConflict(true); Corporate Services What we offer Advertising sales Reprints Supplements Classified Advertising Sales Alerting Services Email table of contents CiteTrack XML RSS feed

Chen,, Jianjun;Senturk,, Damla;Wang,, Jane-Ling;Müller,, Hans-Georg;Carey, James, R.;Caswell,, Hal;Caswell-Chen, Edward, P.

doi: 10.1093/gerona/62.2.126pmid: 17339638

Abstract We monitored survival and reproduction of 1000 individuals of Caenorhabditis elegans wild type (N2) and 800 individuals of clk-1 and daf-2, and used biodemographic analysis to address fitness as the integrative consequence of the entire age-specific schedules of survival and reproduction. Relative to N2, the mutants clk-1 and daf-2 extended average life span by 27% and 111%, respectively, but reduced net reproductive rate by 44% and 18%. The net result of differences in survival and fertility was a significant differential in fitness, with both clk-1 (λ = 2.74) and daf-2 (λ = 3.78) at a disadvantage relative to N2 (λ = 3.85). Demographic life table response experiment (LTRE) analysis revealed that the fitness differentials were due to negative effects in mutants on reproduction in the first 6–7 days of life. Fitness costs in clk-1 and daf-2 of C. elegans are consistent with the theory of antagonistic pleiotropy for the evolution of senescence. THE nematode Caenorhabditis elegans has become a widely used model organism for studies of aging and biodemography (1–10). Its developmental biology and genetics are being intensively studied. There exist many genetically characterized longevity mutants of C. elegans, and their study is an important and growing part of gerontology (10–12). There has been a huge amount of work undertaken on longevity genes relative to longevity extension and other traits including aspects of reproduction, competitive ability, and survival relative to environment and stresses (3,4,9,10,13–18). Here we focus on two of these longevity mutants that are particularly well characterized and understood: clk-1 and daf-2. The clk-1 gene codes an enzyme required for coenzyme Q synthesis, and mutations in clk-1 influence metabolic activity and lead to reduced respiration, slowed developmental and physiological processes, and extended longevity that may be due in part to reduced production of reactive oxygen species (5,7,9,12,19–21). The gene daf-2 codes for an insulin/insulin-like growth factor type I (IGF-I) receptor involved in an insulin-like signaling cascade, and mutants are temperature-sensitive dauer-constitutive with extended longevity (1,3,7,10). Insulin/IGF-I signaling is part of a signaling cascade that influences life span; this signaling pathway has been reviewed by Kenyon (10). In this article, we extend the analysis of clk-1 and daf-2; our focus is on the demographic differences among genotypes and their fitness consequences (22–26). Studies of the evolution, as opposed to the mechanisms, of aging require estimates of the fitness consequences, but they have seldom been estimated for C. elegans. This is an important and potentially confusing point; fitness is an integrative consequence of the entire age-specific (or, more generally, stage-specific) schedules of survival and reproduction. Comparisons of survival alone, or of fertility alone, do not reveal fitness differences. Nor do comparisons of summary indices of survival and fertility (e.g., median longevity, total brood size, average reproductive output, generation time). It might appear that the fitness effects of longevity mutants have been documented, but much of the research has addressed effects of mutations on fitness components, not on fitness itself. For example, measurements of realized population growth [e.g, (14,16)] allow the population itself to integrate survival and fertility, and thus do provide an index of fitness. They have the drawback, however, of providing no information on the causation of the putative fitness differences revealed (i.e., are differences due to differences in survival, or fertility, in what proportions, at what ages?). Biodemographic studies of aging must address the evolution of life span, which requires estimates of fitness. Senescence (the increase of mortality rate with age) has long been a particularly difficult evolutionary problem (22,27,28). One explanation views the evolution of senescence as resulting from an indirect effect of selection for genes with favorable effects on fitness at early ages but negative effects at later ages—an explanation termed “antagonistic pleiotropy” (22,29). Studies of mortality in general, and senescence in particular, must include complete measures of fitness, including survival, fertility, and the timing of events in the life cycle, as only then will the pleiotropic effects on fitness of longevity mutants be revealed. Especially when dealing with longevity as a trait, analysis of fitness is rendered more powerful by the use of large cohorts, because such cohorts provide sufficient numbers for the actuarial properties of the cohort to be measured, including those of the oldest individuals (30). Such large-cohort studies exist for the Mediterranean fruit fly Ceratitis capitata (26), Drosophila melanogaster (31), and C. elegans (9). However, most studies of the life span of C. elegans have used relatively small cohorts (32,33). Here we subject a large cohort data set to demographic analysis, and report on deleterious fitness consequences of extended life span in C. elegans longevity mutants clk-1 and daf-2. Our goals are to: 1) analyze the relationship of reproduction and longevity, 2) quantify the fitness of each strain, 3) document the demographic bases of fitness differences in terms of tradeoffs between survival and reproduction, and 4) explore the relationships between life span and age-specific fertility at the individual level. We do this using a combination of survival analyses, event history diagrams, matrix population models, and life table response experiment (LTRE) analyses. Our results provide, for the first time, a quantitative analysis of the fitness tradeoffs associated with longevity mutations in C. elegans. Methods Genotypes Strains used in the study were: 1) N2, wild type, 2) MQ 130, clk-1(qm30) III, and 3) DR1572, daf-2(e1368) III (a class 1 allele of daf-2). The wild-type (N2 var Bristol; DR subclone of CB original, Tc1 pattern I), clk-1, and daf-2 worms were obtained from the Caenorhabditis Genetic Center at the University of Minnesota, St. Paul in October 2000. All experimental cohorts were two generations removed from a frozen culture maintained at −80°C (34). Experiments Experiments were based on cohorts followed until the death of the last individual. To initiate cohorts, frozen stock was placed on nematode growth medium (NGM) seeded with Escherichia coli strain OP-50 (35) at 20°C. Four days later, the eggs laid on the plate were transferred onto new NGM with OP-50. In 3 days, these eggs developed into mature hermaphrodites laying eggs. First-stage juveniles, newly hatched from the eggs, were used to initiate cohorts. Cohorts were followed 200 worms at a time, and all experiments were conducted in the same laboratory using the same equipment under the same conditions, with the same personnel, to provide consistency. Worms were transferred individually onto 60 mm × 15 mm NGM plates seeded with 1-day-old OP-50 and then maintained in the dark at 20°C in a constant temperature incubator. Worm survival was monitored daily. Survival was determined by observing worms for movement. If no movement was observed for 5–10 seconds, the plate was gently tapped to elicit movement; absent motion, the worm was gently touched near the head with a small piece of agar and then a nematode pick (8). Worms that failed to move were considered dead. During the time that a worm was laying eggs it was transferred each day to new NGM. To avoid mechanical damage, a small block of agar was cut from beneath the worm and transferred, with the worm, to new medium. After the worm had crawled off of the agar block, the block was removed from the plate. Each day, individual worm survival was assessed, and progeny were counted as juveniles emerging from eggs (1 day after eggs were laid) (8). Because facultative vivipary is a life-history trait in C. elegans (36,37), the few adults that died because of the internal hatch of eggs were included in this study. Experiments were initiated with 200 individual worms, with new experiments started at 2-week intervals to yield a total of 2600 individual worms. The experimental cohorts included wild-type (1000 individual worms total) and two longevity mutant strains (800 individual worms each). Demographic Analysis Standard life table parameters were calculated as described by Carey (24,26). Age-specific survivorship lx was calculated as the proportion of individuals surviving to age x. The expectation of life (e0; the average days remaining to an individual at birth) is defined as: In practice, we calculated it from the fundamental matrix [(38), eq. 3.5]. The force of mortality at age x was calculated as: The maternity function mx was measured as the mean number of juvenile progeny produced per worm per day at age x. The survival and reproduction history of each individual was depicted using a color-coded event history chart (39). The cohort generation time is the mean age of the parents of the offspring produced by a cohort over its lifetime. It is defined as: in practice, we computed it from the fundamental matrix (38). For analysis of population growth and fitness, the survival and maternity data were combined to construct an age-classified matrix population model [birth-flow, projection interval of 1 day; see (25)] where n is a vector giving the abundance of the age classes, and A is a population projection matrix which contains age-specific survival probabilities Pi on the subdiagonal and age-specific fertilities Fi in the first row. Such a population will eventually grow exponentially at a rate λ given by the dominant eigenvalue of A. This rate is a measure of fitness that integrates survival, reproduction, and the effects of the timing of reproduction; it can be interpreted as either a measure of mean fitness (23) or as the invasion exponent (40). We also calculated the net reproductive rate R0 (the average number of offspring produced by an individual over its lifetime) and the sensitivity of population growth rate to changes in age-specific survival and fertility. To determine the sources of the differences in fitness among genotypes, we performed an LTRE analysis [(25) Section 10.1 (41)]. Let AN2, Aclk-1, and Adaf-2 be the projection matrices for the three genotypes. Using AN2 as a reference, the fitness difference between wild type (N2) and the strain of interest (here clk-1) can be written as follows: where the superscripts denote genotypes. The terms in the first summation are the contributions to the fitness effect of differences in age-specific survival. The terms in the second summation are the contributions of differences in age-specific fertility. The partial derivatives are the sensitivities of λ to age-specific survival and fertility, and are calculated from A following Caswell [(25), Section 9.1]. Statistical Analysis Confidence intervals were computed on all estimated quantities using bootstrap resampling methods (42), following [(25), Section 12.1]. Each individual, with its age at death and its history of reproduction, was treated as a unit. Bootstrap data sets were created by randomly sampling 1000 individuals (for N2) or 800 individuals (for clk-1 and daf-2), with replacement, from the real data sets. Bootstrap estimates of all demographic parameters were created by applying to the bootstrap data set the same algorithm used for the real data. The 95% confidence intervals were computed using the percentile method [because all quantities were nearly median-unbiased, no bias correction was applied; cf. (42)]. Significance tests were carried out using nonparametric randomization tests (25,43). Comparisons of N2 with clk-1 and daf-2 were conducted for survival (lx), reproduction (mx), age at death (dx), mortality (μx), lambda (λ), life expectancy (ex), and generation time. Test statistics, measuring the differences between strains, were defined for each estimated quantity, as follows. For all scalar measures (life expectancy, λ, generation time), the absolute value of the difference between strains. Survivorship. Let l be the vector of age-specific survivorship. The test statistic was the ∞-norm of the difference between the two functions, This is equivalent to the test statistic used for the 2-sample Kolmogorov–Smirnov test of the difference between two cumulative probability distributions. Age at death. Let d be the vector giving the probability of death at each age. The test statistic was the 1-norm of the difference between the two distributions, this is a standard measure of the difference between two probability distributions. Fertility and the force of mortality. Let m be the vector giving age-specific fertility. The test statistic was the 2-norm of the difference between the two vectors, which is appropriate as these are simply non-negative vectors. This test statistic was also used for mortality (μx). To obtain the distribution of the test statistics under the null hypothesis, individuals (with their complete record of reproduction and age at death) were randomly permuted between treatments, maintaining sample sizes. The permuted data were then subjected to the same analyses as the original data, and the relevant test statistic calculated for each of 2000 permuted data sets. The statistical significance of the observed test statistic is the proportion of the permutation statistics greater than or equal to the observed value. Results Survival (lx), reproduction (mx), age at death (dx), mortality (μx), lambda (λ), life expectancy (ex), and generation time were significantly different (p ≤.0005) between N2 and clk-1 and between N2 and daf-2 (p =.002 for the comparison of μx between N2 and clk-1). Survival The clk-1 and daf-2 mutants both increased survival relative to N2 (Figure 1). Life expectancies at birth (e0) for N2, clk-1, and daf-2 were 14.3, 18.3, and 30.3 days, respectively (Table 1). The distribution of age at death (dx) (Figure 2) is concentrated between 5 and 20 days for N2, between 5 and 15 days with a long tail extending to 50 days in clk-1, and nearly uniformly distributed between 5 and 60 days, with fluctuation, in daf-2. The age patterns of the mortality (ln μxFigure 3) were different among the strains. N2 exhibited a generally increasing mortality, with the slope changing at about Day 8, with a decreasing slope until approximately Day 23, whereas clk-1 and daf-2 showed an increase until Day 6 to Day 8, followed by a decline and a period of essentially no age-related increase in mortality until approximately Day 30 followed by increasing, fluctuating mortality (Figure 3). The period of most intense reproduction was between Days 3 and 6 in N2 and daf-2 and Days 4 and 6 for clk-1, with the reproductive period essentially ended by about Day 9 for all three strains (Figures 4 and 5, and Table 1). About 10% of daf-2 worms had died by this time, compared to about 16% for the N2 and clk-1 (Table 2). Mortality was relatively low during the prereproductive period, with only 4.0%, 3.8%, and 6.4% of N2, clk-1, and daf-2 individuals, respectively, dying prior to reproduction. Differences in survival after most reproduction was completed (i.e., after age 9) accounted for the differences in total life span among strains (Table 1). The expectation of life at Day 9 for N2, clk-1, and daf-2 worms was 6.7, 11.3, and 23.8 days, respectively. Between Days 10 and 18, both longevity mutants exhibited reduced average daily mortality (0.08 for clk-1 and 0.02 for daf-2; 0.15 for N2). By Day 18, when the last daf-2 worm had finished reproduction, about 76% of N2 worms had died, compared to 61% for clk-1, and about 27% for daf-2 (Table 2). Life expectancy on Day 18 was 3.3, 11.2, and 19.6 days for N2, clk-1, and daf-2, respectively. The remaining life expectancy on Day 33, when the last N2 worm died, was 5.6 days for clk-1 and 10.4 days for daf-2. Reproduction Individual life span and lifetime reproduction were not correlated (Table 3 and Figure 6), and lifetime egg production (mean ± standard error) was 293 ± 1.6 for N2, 168 ± 1.3 for clk-1, and 239 ± 1.7 for daf-2. In all the strains, fertility (mx) was concentrated in a limited reproductive window between Days 3 and 7 (Table 1, Figures 4 and 5). Reproduction was initiated by Day 3 in N2 and daf-2, but was delayed until Day 4 in most clk-1 individuals (Table 1, Figures 4 and 5). The pattern of reproduction among individuals is shown in the event history graph (Figure 5). The peak of daily egg production occurred at Day 4 in both N2 and daf-2, but was delayed to Day 5 in clk-1 (Figures 4 and 5). Egg-laying continued at a greatly reduced rate after Day 7, and had ceased completely by Day 14 in N2, Day 16 in clk-1, and Day 18 in daf-2. After egg-laying started, it usually continued without stopping, but interruptions in reproduction were observed in 14%, 26%, and 40% of N2, clk-1, and daf-2 worms, respectively (Table 1 and Figure 5). No individual worms were observed to produce progeny at times later than the average total life span of their strain. In N2 and clk-1 worms that lived longer than 18 days, there was no relationship between remaining lifetime and total reproduction. However, in daf-2 nematodes that lived longer than 18 days, there was a negative relationship between remaining lifetime and egg production during Days 10–18, although only about 0.5% of the reproduction occurred during this time interval. Fitness Fitness was highest for N2 (λ = 3.85), and lower for clk-1 (λ = 2.74) and daf-2 (λ = 3.78). Net reproductive rates (R0) were 286, 161, and 233 for N2, clk-1, and daf-2, respectively. LTRE analysis decomposed the fitness differential between the mutant strains and N2 into contributions from differences in age-specific survival probability and fertility (Figures 7 and 8). There were large differences in survival after age 5 (and especially after age 30) between clk-1, daf-2, and N2, but these survival differences contributed nothing to the fitness differential (Figure 7). There are small positive contributions from very small survival differences in the first 8 days of life, but the confidence intervals on these contributions overlap zero, so the contribution of survival differences to the fitness differential is essentially zero. Fertility differences between the mutants and N2, which were limited to the first 10 days of life, made large contributions to the fitness differentials between the strains (Figure 8). Summing the contributions over age gives the overall contributions of survival and fertility to the fitness differential. Comparing clk-1 and N2, the contributions are 1.37 × 10−3 for survival and −1.07 for fertility. Comparing daf-2 and N2, the contributions are 2.34 × 10−3 for survival and −7.53 × 10−2 for fertility. Thus the contributions of fertility differences to fitness costs were between one and 2 orders of magnitude larger than were the contributions of survival differences. Discussion The longevity mutants clk-1 and daf-2 reduce age-specific mortality and increase postreproductive survival, relative to wild type (N2) C. elegans. These mutations, although exerting a positive effect in later life, carry costs due to effects on other demographic parameters, and hence reduce fitness. These costs may be considered to act as tradeoffs influencing the evolution of life histories; they put the antagonism into the antagonistic pleiotropy theory of senescence. The small increases in early reproduction in the wild type more than make up for its reduced late survival relative to these two mutants. Our analysis of fitness using an assessment of λ is new. The only reported estimates of population growth rate for C. elegans that we are aware of were obtained by measurement of food consumption (13) or from the slope of a linear regression of the log of population size versus time (44), not by demographic calculation. An estimate obtained through regression does not provide insight on how fitness is related to specific differences in survival and fertility. The same general limitation applies to the estimates of relative (not absolute) fitness of C. elegans strains reported by Walker and colleagues (16) and Jenkins and colleagues (14) that were obtained by following changes in the relative abundance of populations over time. In addition, those estimates appear to have been obtained in a serial transfer environment that could be expected to fundamentally alter the selection regime on longevity mutants. Although such studies provide insights, they are not a substitute for demographic analysis as a method for understanding the survival and fertility components of fitness. The antagonistic pleiotropy theory of aging suggests that senescence results from genes with positive effects on fitness early in life but negative effects later in life (22,29). Relatively few genes have been demonstrated to have beneficial effects early in life and detrimental effects later in life (45), but the nematode life-span extension mutants age-1 and daf-2 have influences on life span and estimated fitness consistent with antagonistic pleiotropy (14,16). These longevity mutants change the slope of postreproductive age-specific mortality rates. The leveling of mortality after reproduction that was observed in clk-1 and daf-2 did not occur as clearly in N2. All three strains exhibited mortality trajectories that differed slightly from the two-stage Gompertz patterns reported by Johnson and colleagues (9), but generally agree with those patterns in having an initial exponential mortality increase followed by a lower rate of increase. It is intriguing to consider C. elegans behaviors governed through group interactions (e.g., pheromone influence on dauer formation) relative to the role of postreproductive survival in contributing to the evolution of senescence, given that in social species intergenerational transfers may shape senescence (46). In our experiments, the clk-1 and daf-2 mutants extend average life span relative to the wild type by 27% and 111%, respectively. However, they reduced reproduction in early life, leading to significant fitness costs. The magnitude of these costs can be appreciated by noting that the fitness differentials are sufficient to produce a decline in the frequency, relative to the wild type, of clk-1 of 29% per day and of daf-2 of 1.8% per day. The fitness costs are due to negative effects of the mutations on reproduction in the first 6–7 days of life, as shown by the LTRE analysis. The dramatic improvements in late survival make no contribution to fitness. The positive contributions of increases in early survival are 2 orders of magnitude smaller than the negative contributions of fertility differences during this same period. This is a clear quantitative documentation of the age-specific demographic basis of antagonistic pleiotropic effects on survival and reproduction. Our results are consistent with the quite different study of Hodgkin and Barnes (13), who compared food consumption rates of populations of several strains differing in sperm production, and thus in reproductive rate. They emphasized the importance of changes in the age at first reproduction; our LTRE analysis quantifies this effect, especially for clk-1 (see Figure 8). Our results are also consistent with the determination that longevity genes influence relative fitness and survival under stressful environmental conditions or under competition with wild-type worms (14,16). A key aspect of these effects we report is that, whereas life span is extended by clk-1 and daf-2, the duration of the reproductive window is not. The event history diagram (Figure 6) shows that the beginning and the end of this window are both tightly controlled in N2. In clk-1 the beginning is delayed by 1 day, but the end is even more tightly controlled. In daf-2, both the beginning and end of the reproductive window are very similar to N2, and a linear relationship between life span and postreproductive life span arises from the relatively fixed reproductive schedule. The developing reproductive system influences life span, and laser ablation of germ line precursor cells, eliminating reproduction, may extend the life span of C. elegans (6). Interestingly, both the clk-1 and daf-2 mutations dramatically increase the frequency of breaks in individual reproduction. This phenomenon suggests an effect, unknown at this point, on the genetic regulation of reproduction. It is interesting that two different mutations both show this disruption. Because the mutants increased longevity by extending postreproductive survival, there was no direct relationship between lifetime reproductive output and life span. In general, the results appear to represent tradeoffs relative to extended life span—with reduction in total fertility in longevity mutants. Any estimate of fitness is conditional on the environment in which it is carried out. Our measurements were carried out in controlled laboratory conditions with surplus food. Even under these unstressed conditions, the fitness costs of the longevity mutants were apparent. Stress, for example due to periodic starvation, can exacerbate these effects (14,16); large-cohort demographic data collected under such conditions would permit a detailed analysis of these effects. The ecology of C. elegans is poorly known (47,48). Studies under conditions more ecologically realistic than standard laboratory conditions could provide insights into the selection pressures on life history traits in C. elegans. Van Voorhies and colleagues (18), for example, compared survivorship in soil and sand with that on agar for a wild-type strain (fer-1 wv01) and a daf-2 mutant, although reproduction and fitness were not assessed. Survivorship was drastically reduced in soil, more so for the daf-2 than for the wild type (18). This line of research merits elaboration through experiments that would include monitoring of the introduced bacterial food, given that food concentration can alter life span (49). We anticipate that our N2 1000-worm cohort data will serve as a reference data set for further exploration of C. elegans aging in the wild (50). Our cohorts exhibited considerable interindividual variation in life span. Given the genetic homogeneity of the cohorts and the controlled culture environment, such variation may reflect the epigenetic stochastic elements described by Finch, Kirkwood, and colleagues (51,52), perhaps including senescent decline at the ultrastructural level and decreased gene regulation in the postreproductive period of life (53). Although discussions of longevity mutants often emphasize the unusually long-lived individuals, not all individuals experience long life. This variation in life span has ramifications relative to possible genetic therapies oriented toward life-span extension, that although life-span extension may be achieved through a given genetic pathway, the maximum possible increases in life span are only realized by a few individuals. Decision Editor: Huber R. Warner, PhD Figure 1. Open in new tabDownload slide Cohort survival (lx) of Caenorhabditis elegans (strains N2, clk-1, and daf-2) maintained as individuals on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily (bootstrap 95% confidence intervals shown) Figure 1. Open in new tabDownload slide Cohort survival (lx) of Caenorhabditis elegans (strains N2, clk-1, and daf-2) maintained as individuals on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily (bootstrap 95% confidence intervals shown) Figure 2. Open in new tabDownload slide Frequency of death (dx) for cohorts of Caenorhabditis elegans (strains N2, clk-1, and daf-2) individuals maintained on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily (bootstrap 95% confidence intervals shown) Figure 2. Open in new tabDownload slide Frequency of death (dx) for cohorts of Caenorhabditis elegans (strains N2, clk-1, and daf-2) individuals maintained on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily (bootstrap 95% confidence intervals shown) Figure 3. Open in new tabDownload slide Force of mortality (μx) for cohorts of Caenorhabditis elegans (strains N2, clk-1, and daf-2) individuals maintained on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily. The smoothed mortality rate curves were obtained using a locally weighted regression (LOESS) procedure Figure 3. Open in new tabDownload slide Force of mortality (μx) for cohorts of Caenorhabditis elegans (strains N2, clk-1, and daf-2) individuals maintained on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily. The smoothed mortality rate curves were obtained using a locally weighted regression (LOESS) procedure Figure 4. Open in new tabDownload slide Age-specific reproduction (mx) from cohorts of Caenorhabditis elegans (strains N2, clk-1, and daf-2) maintained as individuals on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily (bootstrap 95% confidence intervals shown) Figure 4. Open in new tabDownload slide Age-specific reproduction (mx) from cohorts of Caenorhabditis elegans (strains N2, clk-1, and daf-2) maintained as individuals on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily (bootstrap 95% confidence intervals shown) Figure 5. Open in new tabDownload slide Event history diagrams showing daily survival versus age for Caenorhabditis elegans cohorts, with individual reproduction at each age indicated by color. Individuals of C. elegans (strains N2, clk-1, and daf-2) were maintained separately on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily Figure 5. Open in new tabDownload slide Event history diagrams showing daily survival versus age for Caenorhabditis elegans cohorts, with individual reproduction at each age indicated by color. Individuals of C. elegans (strains N2, clk-1, and daf-2) were maintained separately on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) with survival and reproduction monitored daily Figure 6. Open in new tabDownload slide Individual life span (in days) versus lifetime total reproduction for Caenorhabditis elegans (strains N2, clk-1, and daf-2) individuals maintained separately on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) and survival and reproduction monitored daily. Curves depicted include fit, 95% confidence intervals, and 95% prediction intervals (N2: y = 312 – 3201/x2, Fit Standard Error = 45.4; clk-1: y = 164 – 13045e−x, Fit Standard Error = 37.1; daf-2: y = 245 – 2346/x2, Fit Standard Error = 40.4) Figure 6. Open in new tabDownload slide Individual life span (in days) versus lifetime total reproduction for Caenorhabditis elegans (strains N2, clk-1, and daf-2) individuals maintained separately on nematode growth medium and OP-50 at 20°C (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively) and survival and reproduction monitored daily. Curves depicted include fit, 95% confidence intervals, and 95% prediction intervals (N2: y = 312 – 3201/x2, Fit Standard Error = 45.4; clk-1: y = 164 – 13045e−x, Fit Standard Error = 37.1; daf-2: y = 245 – 2346/x2, Fit Standard Error = 40.4) Figure 7. Open in new tabDownload slide Life table response experiment analysis of the survival differences and survival contributions to fitness in clk-1 and daf-2 relative to N2 (bootstrap 95% confidence intervals shown) Figure 7. Open in new tabDownload slide Life table response experiment analysis of the survival differences and survival contributions to fitness in clk-1 and daf-2 relative to N2 (bootstrap 95% confidence intervals shown) Figure 8. Open in new tabDownload slide Life table response experiment analysis of the fertility differences and fertility contributions to fitness in clk-1 and daf-2 relative to N2 (bootstrap 95% confidence intervals shown) Figure 8. Open in new tabDownload slide Life table response experiment analysis of the fertility differences and fertility contributions to fitness in clk-1 and daf-2 relative to N2 (bootstrap 95% confidence intervals shown) Table 1. Demographic Parameter in a Large Cohort of Individually Maintained Caenorhabditis elegans (N2, clk-1, and daf-2). Parameter . N2 . clk-1 . daf-2 . Fitness* (95% CI)† 3.85 (3.83, 3.87) 2.74 (2.73, 2.76) 3.78 (3.76, 3.80) Net reproductive rate (R0) (95% CI)† 285.6 (281.8, 289.5) 160.8 (158.0, 163.6) 233.5 (230.3, 237.0) Life expectancy (e0) (95% CI)† 14.33 (14.02, 14.62) 18.25 (17.57, 18.93) 30.26 (29.22, 31.33) Generation time (d) 3.85 (3.84, 3.87) 4.42 (4.40, 4.44) 3.73 (3.71, 3.75) Prereproductive life span (d) 3.01 ± 0.01‡ 3.76 ± 0.02 3.01 ± 0.01     Change relative to N2 +24.9% +0.0% Reproductive life span (d) 6.04 ± 0.05 5.15 ± 0.07 6.43 ± 0.09     Change relative to N2 −14.7% +6.5% Worms with interrupted reproductive period§ 14% 26% 40% Prereproductive and reproductive life span (d) 9.05 ± 0.05 8.91 ± 0.07 9.44 ± 0.09     Change relative to N2 −1.5% +4.3% Postreproductive life span (d) 5.77 ± 0.16 9.83 ± 0.37 21.3 ± 0.53     Change relative to N2 +70.4% +269% Parameter . N2 . clk-1 . daf-2 . Fitness* (95% CI)† 3.85 (3.83, 3.87) 2.74 (2.73, 2.76) 3.78 (3.76, 3.80) Net reproductive rate (R0) (95% CI)† 285.6 (281.8, 289.5) 160.8 (158.0, 163.6) 233.5 (230.3, 237.0) Life expectancy (e0) (95% CI)† 14.33 (14.02, 14.62) 18.25 (17.57, 18.93) 30.26 (29.22, 31.33) Generation time (d) 3.85 (3.84, 3.87) 4.42 (4.40, 4.44) 3.73 (3.71, 3.75) Prereproductive life span (d) 3.01 ± 0.01‡ 3.76 ± 0.02 3.01 ± 0.01     Change relative to N2 +24.9% +0.0% Reproductive life span (d) 6.04 ± 0.05 5.15 ± 0.07 6.43 ± 0.09     Change relative to N2 −14.7% +6.5% Worms with interrupted reproductive period§ 14% 26% 40% Prereproductive and reproductive life span (d) 9.05 ± 0.05 8.91 ± 0.07 9.44 ± 0.09     Change relative to N2 −1.5% +4.3% Postreproductive life span (d) 5.77 ± 0.16 9.83 ± 0.37 21.3 ± 0.53     Change relative to N2 +70.4% +269% Notes: *Fitness calculated as λ, the dominant eigenvalue of the population projection matrix A, determined from cohorts of 1000, 800, and 800 individual worms (N2, clk-1, and daf-2, respectively). †95% confidence intervals (CI) were calculated from 2000 bootstrap samples. ‡Days ± standard error. §Worms in which egg-laying was not continuous after initiation. Open in new tab Table 1. Demographic Parameter in a Large Cohort of Individually Maintained Caenorhabditis elegans (N2, clk-1, and daf-2). Parameter . N2 . clk-1 . daf-2 . Fitness* (95% CI)† 3.85 (3.83, 3.87) 2.74 (2.73, 2.76) 3.78 (3.76, 3.80) Net reproductive rate (R0) (95% CI)† 285.6 (281.8, 289.5) 160.8 (158.0, 163.6) 233.5 (230.3, 237.0) Life expectancy (e0) (95% CI)† 14.33 (14.02, 14.62) 18.25 (17.57, 18.93) 30.26 (29.22, 31.33) Generation time (d) 3.85 (3.84, 3.87) 4.42 (4.40, 4.44) 3.73 (3.71, 3.75) Prereproductive life span (d) 3.01 ± 0.01‡ 3.76 ± 0.02 3.01 ± 0.01     Change relative to N2 +24.9% +0.0% Reproductive life span (d) 6.04 ± 0.05 5.15 ± 0.07 6.43 ± 0.09     Change relative to N2 −14.7% +6.5% Worms with interrupted reproductive period§ 14% 26% 40% Prereproductive and reproductive life span (d) 9.05 ± 0.05 8.91 ± 0.07 9.44 ± 0.09     Change relative to N2 −1.5% +4.3% Postreproductive life span (d) 5.77 ± 0.16 9.83 ± 0.37 21.3 ± 0.53     Change relative to N2 +70.4% +269% Parameter . N2 . clk-1 . daf-2 . Fitness* (95% CI)† 3.85 (3.83, 3.87) 2.74 (2.73, 2.76) 3.78 (3.76, 3.80) Net reproductive rate (R0) (95% CI)† 285.6 (281.8, 289.5) 160.8 (158.0, 163.6) 233.5 (230.3, 237.0) Life expectancy (e0) (95% CI)† 14.33 (14.02, 14.62) 18.25 (17.57, 18.93) 30.26 (29.22, 31.33) Generation time (d) 3.85 (3.84, 3.87) 4.42 (4.40, 4.44) 3.73 (3.71, 3.75) Prereproductive life span (d) 3.01 ± 0.01‡ 3.76 ± 0.02 3.01 ± 0.01     Change relative to N2 +24.9% +0.0% Reproductive life span (d) 6.04 ± 0.05 5.15 ± 0.07 6.43 ± 0.09     Change relative to N2 −14.7% +6.5% Worms with interrupted reproductive period§ 14% 26% 40% Prereproductive and reproductive life span (d) 9.05 ± 0.05 8.91 ± 0.07 9.44 ± 0.09     Change relative to N2 −1.5% +4.3% Postreproductive life span (d) 5.77 ± 0.16 9.83 ± 0.37 21.3 ± 0.53     Change relative to N2 +70.4% +269% Notes: *Fitness calculated as λ, the dominant eigenvalue of the population projection matrix A, determined from cohorts of 1000, 800, and 800 individual worms (N2, clk-1, and daf-2, respectively). †95% confidence intervals (CI) were calculated from 2000 bootstrap samples. ‡Days ± standard error. §Worms in which egg-laying was not continuous after initiation. Open in new tab Table 2. Demographic Parameters for Cohorts of Individual Caenorhabditis elegans (N2, clk-1, and daf-2) Over Three Successive Age Intervals. . \(Age\ Interval\ (d)\) . . . . . . . . . . 0–9 . . . 10–18 . . . 18–ω* . . . Demographic Trait . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . % Worms dying in interval 15.8 16.0 9.75 60.6 45.0 17.5 23.6 39.0 72.8 Average daily mortality 0.026 0.025 0.014 0.148 0.083 0.023 0.365 0.159 0.143 Life expectancy at end of interval† 6.68 11.3 23.8 3.32 11.2 19.6 2.25 8.04 13.9 No. of eggs per hermaphrodite 292.6 167.4 237.3 0.59 0.54 1.37 0 0 0 . \(Age\ Interval\ (d)\) . . . . . . . . . . 0–9 . . . 10–18 . . . 18–ω* . . . Demographic Trait . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . % Worms dying in interval 15.8 16.0 9.75 60.6 45.0 17.5 23.6 39.0 72.8 Average daily mortality 0.026 0.025 0.014 0.148 0.083 0.023 0.365 0.159 0.143 Life expectancy at end of interval† 6.68 11.3 23.8 3.32 11.2 19.6 2.25 8.04 13.9 No. of eggs per hermaphrodite 292.6 167.4 237.3 0.59 0.54 1.37 0 0 0 Notes: The mean prereproductive and reproductive life span is approximately 9 days for all three genotypes (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively). Day 18 is the last day for egg production among 2600 worms in three strains. *Day ω is the day the last surviving worm died within each strain. †Life expectancy e9, e18, e27. Open in new tab Table 2. Demographic Parameters for Cohorts of Individual Caenorhabditis elegans (N2, clk-1, and daf-2) Over Three Successive Age Intervals. . \(Age\ Interval\ (d)\) . . . . . . . . . . 0–9 . . . 10–18 . . . 18–ω* . . . Demographic Trait . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . % Worms dying in interval 15.8 16.0 9.75 60.6 45.0 17.5 23.6 39.0 72.8 Average daily mortality 0.026 0.025 0.014 0.148 0.083 0.023 0.365 0.159 0.143 Life expectancy at end of interval† 6.68 11.3 23.8 3.32 11.2 19.6 2.25 8.04 13.9 No. of eggs per hermaphrodite 292.6 167.4 237.3 0.59 0.54 1.37 0 0 0 . \(Age\ Interval\ (d)\) . . . . . . . . . . 0–9 . . . 10–18 . . . 18–ω* . . . Demographic Trait . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . N2 . clk-1 . daf-2 . % Worms dying in interval 15.8 16.0 9.75 60.6 45.0 17.5 23.6 39.0 72.8 Average daily mortality 0.026 0.025 0.014 0.148 0.083 0.023 0.365 0.159 0.143 Life expectancy at end of interval† 6.68 11.3 23.8 3.32 11.2 19.6 2.25 8.04 13.9 No. of eggs per hermaphrodite 292.6 167.4 237.3 0.59 0.54 1.37 0 0 0 Notes: The mean prereproductive and reproductive life span is approximately 9 days for all three genotypes (n = 1000, 800, and 800 for N2, clk-1, and daf-2, respectively). Day 18 is the last day for egg production among 2600 worms in three strains. *Day ω is the day the last surviving worm died within each strain. †Life expectancy e9, e18, e27. Open in new tab Table 3. Mean and Coefficient of Variation (CV) for Life History Components of Long-Lived and Wild-Type Caenorhabditis elegans. . \(Strains\) . . . . . . . \(Wild\ Type\) . . \(\mathit{clk-1}\) . . \(\mathit{daf-2}\) . . Life History Components . Mean . CV . Mean . CV . Mean . CV . Life span (d) 14.8 34.5 18.7 55.1 30.7 48.9 First day of reproduction 3.01 5.55 3.76 13.4 3.01 6.53 Last day of reproduction 9.05 17.7 8.91 21.2 9.44 25.6 No. of days in reproductive period 6.04 26.7 5.15 36.4 6.43 37.6 Total egg production 293 17.6 168 22.8 239 18.3 . \(Strains\) . . . . . . . \(Wild\ Type\) . . \(\mathit{clk-1}\) . . \(\mathit{daf-2}\) . . Life History Components . Mean . CV . Mean . CV . Mean . CV . Life span (d) 14.8 34.5 18.7 55.1 30.7 48.9 First day of reproduction 3.01 5.55 3.76 13.4 3.01 6.53 Last day of reproduction 9.05 17.7 8.91 21.2 9.44 25.6 No. of days in reproductive period 6.04 26.7 5.15 36.4 6.43 37.6 Total egg production 293 17.6 168 22.8 239 18.3 Open in new tab Table 3. Mean and Coefficient of Variation (CV) for Life History Components of Long-Lived and Wild-Type Caenorhabditis elegans. . \(Strains\) . . . . . . . \(Wild\ Type\) . . \(\mathit{clk-1}\) . . \(\mathit{daf-2}\) . . Life History Components . Mean . CV . Mean . CV . Mean . CV . Life span (d) 14.8 34.5 18.7 55.1 30.7 48.9 First day of reproduction 3.01 5.55 3.76 13.4 3.01 6.53 Last day of reproduction 9.05 17.7 8.91 21.2 9.44 25.6 No. of days in reproductive period 6.04 26.7 5.15 36.4 6.43 37.6 Total egg production 293 17.6 168 22.8 239 18.3 . \(Strains\) . . . . . . . \(Wild\ Type\) . . \(\mathit{clk-1}\) . . \(\mathit{daf-2}\) . . Life History Components . Mean . CV . Mean . CV . Mean . CV . Life span (d) 14.8 34.5 18.7 55.1 30.7 48.9 First day of reproduction 3.01 5.55 3.76 13.4 3.01 6.53 Last day of reproduction 9.05 17.7 8.91 21.2 9.44 25.6 No. of days in reproductive period 6.04 26.7 5.15 36.4 6.43 37.6 Total egg production 293 17.6 168 22.8 239 18.3 Open in new tab This work was supported in part by grants from the Center for the Demography and Economics of Aging at the University of California, Berkeley, National Institutes of Health (NIH) Grant P01-AG022500-01, and National Science Foundation grants DEB-0235692 and DEB-0343820. Strains used in this work were provided by the Caenorhabditis Genetics Center, which is funded by the NIH National Center for Research Resources. We thank Ed Lewis and two anonymous reviewers for helpful comments on the manuscript, William Moore for discussion and assistance in the laboratory, and K. Kaplan, A. Foster, M. Olsen, D. Raju, R. Ramirez, J. Shinen, T. Wasilchen, and K. Sanchez for laboratory assistance. References 1 Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R. A C. elegans mutant that lives twice as long as wild type. Nature. 1993 ; 366 : 461 -464. 2 Wang JL, Muller HG, Capra WB, Carey JR. Rates of mortality in populations of Caenorhabditis elegans. Science. 1994 ; 266 : 827 -828. 3 Gems D, Sutton AJ, Sundermeyer ML, et al. Two pleiotropic classes of daf-2 mutation affect larval arrest, adult behavior, reproduction and longevity in Caenorhabditis elegans. Genetics. 1998 ; 150 : 129 -155. 4 Apfeld J, Kenyon C. Regulation of lifespan by sensory perception in Caenorhabditis elegans. Nature. 1999 ; 402 : 804 -809. 5 Felkai S, Ewbank JJ, Lemieux J, Labbe JC, Brown GG, Hekimi S. CLK-1 controls respiration, behavior and aging in the nematode Caenorhabditis elegans. EMBO J. 1999 ; 18 : 1783 -1792. 6 Hsin H, Kenyon C. Signals from the reproductive system regulate the lifespan of C. elegans. Nature. 1999 ; 399 : 362 -366. 7 Vanfleteren JR, Braeckman BP. Mechanisms of life span determination in Caenorhabditis elegans. Neurobiol Aging. 1999 ; 20 : 487 -501. 8 Chen J, Carey JR, Ferris H. Comparative demography of isogenic populations of Caenorhabditis elegans. Exp Gerontol. 2001 ; 36 : 431 -440. 9 Johnson TE, Wu D, Tedesco P, Dames S, Vaupel JW. Age-specific demographic profiles of longevity mutants in Caenorhabditis elegans show segmental effects. J Gerontol Biol Sci. 2001 ; 56A : B331 -B339. 10 Kenyon C. The plasticity of aging: insights from long-lived mutants. Cell. 2005 ; 120 : 449 -460. 11 Boehm M, Slack F. A developmental timing microRNA and its target regulate life span in C. elegans. Science. 2005 ; 310 : 1954 -1957. 12 Hamilton B, Dong Y, Shindo M, Liu W, Odell I, Ruvkun G. A systematic RNAi screen for longevity genes in C. elegans. Genes Dev. 2005 ; 19 : 1544 -1555. 13 Hodgkin J, Barnes TM. More is not better: brood size and population growth in a self-fertilizing nematode. Proc R Soc Lond Ser B. 1991 ; 246 : 19 -24. 14 Jenkins NL, McColl G, Lithgow GJ. Fitness cost of extended lifespan in Caenorhabditis elegans. Proc R Soc Lond Ser B. 2004 ; 271 : 2523 -2526. 15 Johnson TE, Hutchinson EW. Absence of strong heterosis for life span and other life history traits in Caenorhabditis elegans. Genetics. 1993 ; 134 : 465 -474. 16 Walker DW, McColl G, Jenkins NL, Harris J, Lithgow GJ. Evolution of lifespan in C. elegans. Nature. 2000 ; 405 : 296 -297. 17 Van Voorhies WA, Ward S. Genetic and environmental conditions that increase longevity in Caenorhabditis elegans decrease metabolic rate. Proc Natl Acad Sci U S A. 1999 ; 96 : 11399 -11403. 18 Van Voorhies WA, Fuchs J, Thomas S. The longevity of Caenorhabditis elegans in soil. Biol Lett. 2005 ; 1 : 247 -249. 19 Lakowski B, Hekimi S. Determination of life-span in Caenorhabditis elegans by four clock genes. Science. 1996 ; 272 : 1010 -1013. 20 Hekimi S, Burgess J, Bussière F, Meng Y, Bénard C. Genetics of lifespan in C. elegans: molecular diversity, physiological complexity, mechanistic simplicity. Trends Gen. 2001 ; 17 : 712 -718. 21 Kurz CL, Tan MW. Regulation of aging and innate immunity in C. elegans. Aging Cell. 2004 ; 3 : 185 -193. 22 Rose M. Evolutionary Biology of Aging. New York: Oxford University Press; 1991. 23 Charlesworth B. Evolution in Age-Structured Populations. Cambridge, U.K.: Cambridge University Press; 1994. 24 Carey JR. Insect biodemography. Ann Rev Entomol. 2001 ; 46 : 79 -110. 25 Caswell H. Matrix Population Models: Construction, Analysis, and Interpretation. 2nd ed. Sunderland, MA: Sinauer Associates, Inc.; 2001. 26 Carey JR. Longevity: The biology and demography of life span. Princeton, NJ: Princeton University Press; 2003. 27 Medawar PB. The Uniqueness of the Individual. London: Methuen; 1957. 28 Hamilton WD. The moulding of senescence by natural selection. J Theor Biol. 1966 ; 12 : 12 -45. 29 Williams GC. Pleiotropy, natural selection, and the evolution of senescence. Evolution. 1957 ; 11 : 398 -411. 30 Carey JR, Liedo P, Muller HG, Wang JL, Chiou JM. Relationship of age patterns of fecundity to mortality, longevity, and lifetime reproduction in a large cohort of Mediterranean fruit fly females. J Gerontol Biol Sci. 1998 ; 53 : B245 -B251. 31 Curtsinger JW, Fukui HH, Townsend DR, Vaupel JW. Demography of genotypes: failure of the limited life-span paradigm in Drosophila melanogaster. Science. 1992 ; 258 : 461 -463. 32 Vaupel JW, Carey JR, Christensen K, et al. Biodemographic trajectories of longevity. Science. 1998 ; 280 : 855 -860. 33 Brooks A, Lithgow GJ, Johnson TE. Mortality rates in a genetically heterogeneous population of Caenorhabditis elegans. Science. 1994 ; 263 : 668 -671. 34 Stiernagle T. Maintenance of C. elegans. In: Hope IA, ed. C. elegans: A Practical Approach. Oxford: Oxford University Press; 1999:51–67. 35 Brenner S. The genetics of Caenorhabditis elegans. Genetics. 1974 ; 77 :(1): 71 -94. 36 Chen J, Caswell-Chen EP. Facultative vivipary is a life-history trait in C. elegans. J Nematol. 2004 ; 36 : 107 -113. 37 Chen J, Caswell-Chen EP. Why Caenorhabditis elegans adults sacrifice their bodies to progeny. Nematology. 2003 ; 5 : 641 -645. 38 Caswell H. Applications of Markov chains in demography. In: Langville AN, Stewart WJ, eds. MAM2006: Markov Anniversary Meeting. Raleigh, North Carolina: Boson Books; 2006:319–334. 39 Carey JR, Liedo P, Muller HG, Wang JL, Vaupel JW. A simple graphical technique for displaying individual fertility data and cohort survival: case study of 1000 Mediterranean Fruit Fly females. Func Ecol. 1998 ; 12 : 259 -363. 40 Metz JAJ, Nisbet RM, Geritz SAH. How should we define ‘fitness’ for general ecological scenarios? Trends Ecol Evol. 1992 ; 7 : 198 -202. 41 Caswell H. The analysis of life table response experiments. I. Decomposition of effects on population growth rate. Ecol Model. 1989 ; 46 : 221 -237. 42 Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York: Chapman & Hall; 1993. 43 Manly BFJ. Randomization and Monte Carlo Methods in Biology. New York: Chapman & Hall; 1997. 44 Venette RC, Ferris H. Influence of bacterial type and density on population growth of bacterial-feeding nematodes. Soil Biol Biochem. 1998 ; 30 : 949 -960. 45 Ricklefs RE, Finch CE. Aging: A Natural History. New York: Scientific American Library; 1995. 46 Lee RD. Rethinking the evolutionary theory of aging: transfers, not births, shape senescence in social species. Proc Natl Acad Sci U S A. 2003 ; 100 : 9637 -9642. 47 Caswell-Chen EP, Chen J, Lewis EE, Douhan GW, Nadler SA, Carey JR. Revising the standard wisdom of C. elegans natural history: ecology of longevity. Sci Aging Knowl Environ. 2005 ; 40 : pe30 DOI: 10.1126 sageke.200540.pe30]. 48 Chen J, Lewis EE, Carey JR, Caswell H, Caswell-Chen EP. The ecology and biodemography of C. elegans. Exp Gerontol. 2006; 41:1059–1065. 49 Klass MR. Aging in the nematode Caenorhabditis elegans: major biological and environmental factors influencing life span. Mech Aging Dev. 1977 ; 6 : 413 -429. 50 Müller HG, Wang JL, Carey JR, et al. Demographic window to aging in the wild: constructing life tables and estimating survival functions from marked individuals of unknown age. Aging Cell. 2004 ; 3 : 125 -131. 51 Finch CE, Kirkwood TBL. Chance, Development, and Aging. New York: Oxford University Press; 2000. 52 Kirkwood TBL, Feder M, Finch CE, et al. What accounts for the wide variation in life span of genetically identical organisms reared in a constant environment? Mech Ageing Dev. 2005 ; 126 : 439 -443. 53 Herndon LA, Schmeissner PJ, Dudaronek JM, et al. Stochastic and genetic factors influence tissue-specific decline in ageing C. elegans. Nature. 2002 ; 419 : 808 -814. Author notes 1Department of Nematology, 3Division of Statistics, and 4Department of Entomology, University of California, Davis. Copyright 2007 by The Gerontological Society of America

Chen, Jianjun; Senturk, Damla; Wang, Jane-Ling; Müller, Hans-Georg; Carey, James R.; Caswell, Hal; Caswell-Chen, Edward P.

doi: N/Apmid: N/A

We monitored survival and reproduction of 1000 individuals of Caenorhabditis elegans wild type (N2) and 800 individuals of clk-1 and daf-2, and used biodemographic analysis to address fitness as the integrative consequence of the entire age-specific schedules of survival and reproduction. Relative to N2, the mutants clk-1 and daf-2 extended average life span by 27% and 111%, respectively, but reduced net reproductive rate by 44% and 18%. The net result of differences in survival and fertility was a significant differential in fitness, with both clk-1 (λ = 2.74) and daf-2 (λ = 3.78) at a disadvantage relative to N2 (λ = 3.85). Demographic life table response experiment (LTRE) analysis revealed that the fitness differentials were due to negative effects in mutants on reproduction in the first 6–7 days of life. Fitness costs in clk-1 and daf-2 of C. elegans are consistent with the theory of antagonistic pleiotropy for the evolution of senescence.

Zheng,, Hongjun;Martin, James, A.;Duwayri,, Yazan;Falcon,, Gilbert;Buckwalter, Joseph, A.

doi: 10.1093/gerona/62.2.136pmid: 17339639

Abstract Damaged articular cartilage rarely heals or regenerates in middle-aged and elderly adults, suggesting that the chondrogenic potential of mesenchymal stem cells declines with age. To test this hypothesis, we measured the responses of rat bone marrow-derived mesenchymal stem cells (BMSCs) to chondrogenic induction in vitro. BMSCs from immature rats (1 week old), young adult rats (12 weeks old), and old adult rats (1 year old) were analyzed for cartilage extracellular matrix (ECM) production. Histologic analysis showed strong cartilage ECM formation by BMSCs from 1-week-old rats, but not by BMSCs from 12-week-old or 1-year-old rats. Real-time polymerase chain reaction revealed age-related declines in messenger RNA encoding type II collagen, aggrecan, and link protein, three major cartilage ECM components. Microarray analysis indicated significant age-related differences in the expression of genes that influence cartilage ECM formation. These findings support the hypothesis that the chondrogenic potential of mesenchymal stem cells declines with age. POSTTRAUMATIC osteoarthritis (PTOA) commonly develops rapidly after fractures that disrupt the articular surface (1–3). Despite aggressive surgical intervention, more than 25% of patients with fractures of the hip and knee joints develop PTOA, and the rate increases to 50% or more of patients with ankle fractures (4–7). Osteoarthritis with extensive cartilage degeneration and joint space narrowing is usually evident on radiologic examination within 2–5 years after fracture (6,7). Clinical experience and epidemiologic studies indicate that the incidence of PTOA after joint fracture is strongly dependent on patient age (8–10). Adults in their 20s and 30s are significantly less likely to develop PTOA in injured joints than are adults older than 50 years (8). The obvious clinical goal of improving recovery rates in older patients awaits a better understanding of the biology underlying this strong aging effect. Recovery from serious articular injuries involves cartilage regeneration. Although cartilage itself shows a very limited capacity for self-repair, pluripotent bone marrow-derived mesenchymal stem cells (BMSCs) are capable of complete cartilage regeneration (11–14). A role for BMSCs in this process is supported by evidence for spontaneous cartilage and bone regeneration following microfracture, a surgical treatment designed to increase the access of MSCs to cartilage defects (15). Results from a number of animal models indicate that transplanted BMSCs improve the repair of damaged cartilage, bone, tendon, and ligament in vivo (11,16–22). Thus, given appropriate environmental conditions, BMSCs can differentiate into chondrocytes, osteoblasts, and tendon or ligament fibroblasts (23–25). In vitro studies show that inducers of chondrocyte differentiation include the bone morphogenic proteins-2 and -6 (BMP-2, BMP-6) (26–28), the transforming growth factors beta-1 and beta-3 (TGF-β1, TGF-β3) (23), and fibroblast growth factor-2 (FGF-2) (29). One or more of these factors may play a role in stimulating cartilage regeneration in vivo. Pluripotent BMSCs can be isolated from other cell types present in bone marrow aspirates on the basis of their adhesion to plastic culture dishes (23). However, clonal analysis has demonstrated that “plastic-selected” BMSCs are a far from homogenous population. BMSCs vary with respect to morphology (30,31), growth characteristics (32), ability to differentiate (33,34), and expression of cell surface antigens (35). BMSC isolates contain large, slowly replicating cells and small, rapidly growing cells, which retain multipotential differentiation status longer than do larger cells (36). There is evidence to suggest that some BMSCs express telomerase and that telomerase expression enhances osteogenic and chondrogenic differentiation (37,38). However, the fraction of telomerase-expressing cells appears to be species-specific, and telomerase activity may be very rare in human BMSCs compared to mouse BMSCs (39). Cells with different growth and differentiation characteristics have been separated from bone marrow isolates on the basis of immunoreactivity for antigens such as Stro-1 and CD105 (35,40,41). A number of studies suggest that aging impairs various aspects of mesenchymal stem cell function, but the impact of these changes on osteochondral repair remains unclear (42–45). Interestingly, the ratio of small to large cells in BMSC populations declines as the cells approach replicative senescence in culture (36). However, whether senescent stem cells accumulate with age in vivo is uncertain. Stenderup and colleagues (32) found that proliferation rate and population doubling limits of human BMSCs were significantly higher in young individuals (18–29 years) than in old individuals (aged 68–81 years), but there were no age-related differences in adipogenic or osteogenic potential (32). Additional studies show an age-related decline in the proportion of BMSCs capable of in vitro osteogenesis (33,43,44). D'Ippolito and colleagues (44) reported that alkaline phosphatase-positive colonies declined from 66 per million BMSCs in young patients (13–36 years old) to 15 per million cells in older patients (41–70 years old). In contrast, a recent in vivo study demonstrated that autologous bone marrow-derived stem cells harvested when rabbits were 1 year old were no better at repairing a patellar tendon injury than were cells isolated when the rabbits were 3 years older (45). Thus, although many in vitro studies support the hypothesis that aging effects on stem cells impair their wound healing function, the evidence for this impairment is still largely circumstantial. Little information is available concerning the molecular and cellular changes that occur in stem cell populations during aging. Furthermore, most aging studies have focused on osteogenesis or adipogenesis as an outcome measure, thus the specific effects of age on chondrogenic differentiation have not been elucidated. To determine if aging affects stem cell chondrogenesis, we investigated the effects of age on the in vitro chondrogenic differentiation of rat BMSCs. BMSCs harvested from skeletally immature animals (1 week old), young mature animals (12 weeks old), and old mature animals (1 year old) were compared to study the effects of maturation and adult aging on the expression of cartilage-specific genes and on hyaline cartilage matrix formation. Materials and Methods Isolation and Culture of BMSCs BMSCs were isolated from 1-week-old, 12-week-old, and 1-year-old male Sprague–Dawley rats. Femurs and tibias were removed, and soft tissues were detached aseptically. Metaphyses were resected from both ends, and the diaphyses were flushed with Hank's Balanced Salt Solution. A suspension of bone marrow cells was obtained by repeated aspiration of the cell preparation through a 20-gauge needle. The cell suspension was centrifuged at 1000 g for 5 minutes, resuspended in BMSC growth medium (Dulbecco's modified Eagle medium supplemented with 10% fetal bovine serum and 2% penicillin and streptomycin), aliquoted into T-225 tissue culture flasks, and cultured at 37°C in a 5% CO2 atmosphere. Three days later, nonadherent cells were washed out by replacing the medium. The remaining adherent BMSCs were fed every 2–3 days until they reached confluence (usually 7–10 days). BMSCs were passaged with 0.025% trypsin-EDTA for 5 minutes at 37°C and replated in 100-mm culture dishes at a density of 5 × 105 cells per dish (for RNA extraction) or 2.5 × 106 cells per dish (for in vitro cartilage formation). Each experiment was performed with BMSCs pooled from 3–5 rats for each age group. Chondrogenesis experiments were repeated 3 times with different batches of BMSCs isolated at different times. Chondrogenic Induction Twenty-four hours after the first passage, the BMSC growth medium was replaced by chondrogenic medium (Dulbecco's modified Eagle medium, pyruvate [1 mM], ITS-Premix [transferrin at 6.25 μg/mL, selenous acid, linoleic acid at 5.35 μg/mL, insulin, bovine serum albumin at 1.25 μg/mL; Invitrogen, Carlsbad, CA], 100 nM dexamethasone, ascorbate 2-phosphate at 37.5 μg/mL, TGF-β1 at 10 ng/mL, insulin like growth factor [IGF]-I at 100 ng/mL). Thereafter, the chondrogenic medium was changed every other day. RNA was isolated after 7 days. For in vitro cartilage formation experiments, the cells were cultured for 3 weeks. The cells aggregated spontaneously into blocks 3 or 4 days after the first feeding of chondrogenic medium. Total RNA Extraction Total RNA was isolated by extraction of cultures with Trizol Reagent (Invitrogen, Carlsbad, CA) followed by purification with an RNeasy kit (Qiagen, Chatsworth, CA). The concentration of RNA was measured with an ultraviolet spectrometer. Reverse Transcription–Polymerase Chain Reaction Reverse transcription–polymerase chain reaction (RT–PCR) was used to assess expression of mesenchymal marker genes in BMSCs. Complementary DNA (cDNA) was synthesized and amplified using a commercial kit according to the manufacturer's recommendations (Invitrogen). Reverse transcription was first performed during a 30-minute incubation at 55°C, followed by a 2-minute incubation at 94°C to inactivate the reverse transcriptase. PCR amplification was performed in a 50 μL volume with 35 cycles of 94°C for 15 seconds, 58°C for 30 seconds (60°C for GADPH), and 70°C for 30 seconds (extended to 5 minutes in the last cycle). RT–PCR products were subjected to electrophoresis on 2% agarose gels and visualized with ethidium bromide. Gene names, accession numbers, associated primer sequences, and expected PCR product lengths are given in Table 1. Real-Time PCR Analysis Three total RNA samples for each age group were analyzed to measure aging effects on chondrocyte marker gene expression. cDNA was synthesized as described above for RT–PCR. Oligonucleotide primers were designed for amplification of messenger RNA (mRNA) encoding GAPDH, cartilage-specific matrix proteins (collagen type II, aggrecan, link protein), and the chondrogenesis-related transcription factor, Sox9 (Table 2). Real-time PCR amplification was performed on an Applied Biosystems International (ABI, Foster City, CA) PRISM 7700 Sequence Detection System. Expression levels were determined using the relative threshold cycle (CT) method as described by the manufacturer of the detection system. Expression levels are stated in terms of fold increase or decrease relative to BMSCs from 1-year-old rats. This was calculated for each gene by evaluating the expression 2−ΔΔCT, where ΔΔCT is the result of subtracting [CTgene – CTGAPDH](1-year-old) from [CTgene – CTGAPDH](1-week-old) or [CTgene – CTGAPDH](12-week-old). The error bars given represent the standard deviation of the ΔΔCT values. One-way analysis of variance (ANOVA) and the Tukey test were used to determine the statistical significance of age-related differences in the expression of each gene. Microarrays Total RNA from three different rat BMSC isolates was pooled for each age group and submitted to the DNA Core Facility at the University of Iowa for microarray analysis. Affymetrix Rat Expression Set 230 GeneChip (Santa Clara, CA) microarrays containing more than 28,000 rat genes were used for analysis of global gene expression. Microarray assays were performed using procedures adapted from the Affymetrix GeneChip Expression Analysis Technical Manual. GeneChip one-cycle target labeling and control reagents kit (Affymetrix), which contained all the required reagents, was used. Briefly, 5 μg of total RNA was used to synthesize double-stranded cDNA using the One-Cycle cDNA Synthesis Kit. The DNA was purified by the Sample Cleanup Module (Affymetrix, Santa Clara, CA). In vitro transcription was performed to produce biotin-labeled copy RNA (cRNA) by using the IVT Labeling Kit (Affymetrix). Biotinylated cRNA was purified with the Sample Cleanup Module, fragmented to 50–200 nucleotides at 95°C in the presence of high magnesium concentration, and hybridized to Affymetrix Rat Expression Set 230 GeneChip at 45°C overnight (Hybridization Control Kit). After being washed, the array was stained with streptavidin–phycoerythrin. The staining signal was amplified by biotinylated antistreptavidin antibody, and final staining with streptavidin–phycoerythrin was performed on GeneChip fluidics station 450. Scans were performed with an Affymetrix GeneChip scanner 3000, and the expression value for each gene was calculated using Affymetrix Microarray Analysis Suite software. The expression data were analyzed by using GeneSpring software (Silicon Genetics, Santa Clara, CA). Safranin-O Histology and Immunofluorescence Aggregates were cryoembedded, sectioned, and fixed in 2% paraformaldehyde for histochemical and immunofluorescence staining. Some sections were stained with safranin-O to detect sulfated glycosaminoglycans in cell aggregates. The percentage of the cells that were stained by safranin-O was determined by manually counting cells in microscope fields representing.024 mm2 areas (0.6 mm w × 0.4 mm h) of aggregate cross-sections (2–3 mm diameter). Cells were counted as positive if the hematoxylin-stained nucleus was within a cell diameter of safranin-O-stained matrix. No attempt was made to distinguish between different stain intensities. Each section was sampled by taking images at six locations beginning at the outer edge of a section and passing through its middle. The means and standard deviations for each of the age groups were calculated based on a total of three sections (18 microscope fields) from three different aggregates. The results were analyzed by one-way ANOVA on ranks and Dunn's test to determine the significance of differences among the age groups (p <.05). The expression of the cartilage matrix proteins was detected using monoclonal antibodies to type II collagen (II-II6B3), aggrecan (12/21/1-C-6), and link protein (9/30/8-A-4) (Developmental Studies Hybridoma Bank, Iowa City, IA). Reduction and alkylation were performed for antiaggrecan and antilink protein staining. Briefly, sections were reduced in dithiothreitol at 10 mg/mL in 0.1 M Tris ph 8.0 for 2 hours at room temperature, then washed twice with 0.1 M Tris. Sections were then incubated for 20 minutes in iodoacetic acid at 60 mg/mL (in 0.1 M Tris ph 8.0), followed by two washes with phosphate-buffered saline [PBS]). Sections were blocked for 1 hour with PBS containing 0.1% Tween-20 and 1% bovine serum albumin, then incubated for 2 hours in undiluted hybridoma supernatants. Negative controls were incubated in PBS block (no primary antibody). Sections were washed in PBS containing 0.1% Tween-20 (PBST) and blocked for 30 minutes in PBST containing 10% goat serum. A Cy-3–conjugated goat anti-mouse secondary antibody (Jackson ImmunoResearch, West Grove, PA) was diluted 1:250 in PBST + 10% goat serum and applied to the sections. After a 1-hour incubation, the sections were washed in PBST and mounted in Vectashield (Vector Laboratories, Burlingame, CA). Stained sections were imaged on an Olympus BX60 epifluorescence microscope. Results Mesenchymal Cell Marker Expression in Rat Bone Marrow Cells Prior to chondrogenic induction, BMSCs from one isolate were subjected to RT–PCR analysis for genes associated with mesenchymal cells (c-Kit, CD105, BMPR1a, and BMPr1b) (Figure 1). There were no obvious age-related differences in expression levels for CD105, BMPR1a, or BMPR1b. However, the level of c-Kit, a protein tyrosine kinase receptor for stem cell factor (46) appeared to decrease with increasing age. The hematopoietic stem cell marker Sca-1 (47) was undetectable in all age groups. These data indicated that the bone marrow cells from all age groups shared a common mesenchymal origin. Cell Morphology and Aggregate Formation In serum-containing medium, BMSCs in first passaged monolayer culture displayed a fibroblast-like morphology, and there was no obvious age-related variation in cell shape or size (Figure 2A). When plated at low density in chondrogenic medium, BMSCs became more polygonal in shape after 2–3 days. Some cells formed cobblestone-like nodes reminiscent of those formed by primary chondrocytes (Figure 2B). When plated at high density in chondrogenic medium, BMSCs spontaneously formed large aggregates after 48–72 hours. Single aggregates were formed from entire monolayers, which rolled up from the edge of the culture dish and contracted to form irregularly shaped masses. After 10 days in chondrogenic medium, these initially loosely structured aggregates became firmer in texture and more spherical in shape (Figure 2C). This process occurred in high-density cultures of BMSCs regardless of the age of the rats from which they were isolated. Cartilage Matrix Formation In Vitro BMSC aggregates were cultured in chondrogenic medium for 2–3 weeks for histologic and biochemical evaluation of cartilage ECM formation (Figure 3). Safranin-O staining revealed extensive proteoglycan accumulation in aggregates formed by BMSCs from 1-week-old rats (Figure 3A). This accumulation was particularly evident at the center of the aggregates where the spaces between cells were heavily stained (Figure 3D). In contrast, sections from aggregates composed of BMSCs from 12-week-old or 1-year-old rats were largely safranin-O negative (Figure 3B, E, C, and F). The numbers of BMSCs that stained with safranin-O were counted in sections of aggregates (Figure 3G). These data indicated a significantly higher percentage of positive cells in aggregates from 1-week-old rat BMSCs than in aggregates from adult rat BMSCs (>12-week-old, >1-year-old rats; p >.05). The slight difference between BMSCs from 12-week-old and 1-year-old adult rats was not statistically significant. Replicate frozen sections from aggregate cultures were stained for collagen type II, aggrecan, and cartilage link protein using immunofluorescence (Figure 4). All three of these proteins were easily detected by this method in aggregates formed by BMSCs from 1-week-old rats (Figure 4A, D, and G) but not in aggregates formed by BMSCs from 12-week-old rats (Figure 4B, E, and H) or by BMSCs from 1-year-old rats (Figure 4C, F, and I). These findings were consistent with the safranin-O stains, which showed that BMSCs from only 1-week-old rats were capable of building a cartilage-like ECM under these conditions. Chondrocyte Marker Gene Expression Real-time PCR revealed significant age-related differences in the expression of genes encoding the cartilage matrix protein type II collagen, aggrecan, and link protein, and the chondrogenic transcription factor, Sox9 (Figure 5). Expression levels for all three matrix proteins were lowest in BMSCs from 1-year-old rats. Type II collagen gene expression was similar in BMSCs from 1-week-old and 12-week-old rats, but mean expression levels in both these groups were more than twice that measured in BMSCs from 1-year-old rats. Aggrecan expression peaked in BMSCs from 1-week-old rats and declined significantly thereafter: Expression in BMSCs from 1-week-old animals was nearly 4-fold greater than in BMSCs from 12-week-old animals and 9-fold greater than in BMSCs from 1-year-old animals. In contrast, link protein expression peaked in the 12-week-old age group: Expression in these BMSCs was more than 7-fold greater than in BMSCs from 1-week-old rats, and more than 14-fold greater than in BMSCs from 1-year-old rats. Sox9 expression also varied in an age-related manner, but levels were lowest in BMSCs from 1-week-old rats. Microarray Analysis Total RNA from BMSCs cultured for 1 week in chondrogenic medium was used for microarray hybridization analysis. The results showed differences in the expression of a variety of genes that may have affected in vitro cartilage ECM formation (Table 3). Age-related variations in the pattern of cartilage ECM gene expression were apparent (Table 3). The highest hybridization signal intensities for aggrecan and type II collagen were associated with BMSCs from 1-week-old rats, and the highest signal intensities for link protein were in BMSCs from 12-week-old rats. These results indicated a rank ordering of age groups similar to that indicated by PCR analysis. However, microarray and PCR results differed in terms of the apparent magnitudes of age-related changes in type II collagen and aggrecan gene expression. Signal intensities for type II collagen were approximately 2-fold greater in BMSCs from 1-week-old rats than in either adult group of BMSCs, whereas the difference between 1 week and 12 weeks measured by PCR was minimal. The signal intensity for aggrecan in BMSCS from 1-week-old rats was 2.9-fold higher than in BMSCs from 12-weeks-old rats, and 7.8-fold greater than in 1-year-old rats (as opposed to ∼ 4-fold and > 9-fold, respectively, as measured by PCR). A number of other cartilage-related genes were included in the analysis. Expression of cartilage oligomeric matrix protein (Comp) was 1.5-fold higher in BMSCs from young, 1-week-old rats compared to 12-week-old rats and 2.8-fold higher than in BMSCs from 1-year-old rats. Collagen type IX peptide expression (α-1 and α-3) declined by 2-6-fold in BMSCs from 12-week-old and 1-year-old rats compared with BMSCs from 1-week-old rats. No aging effects were observed for type XI collagen expression, which was expressed at the same high level in all three age groups. Type X collagen expression was slightly higher (1.5-fold) in BMSCs from 1-week-old rats than in BMSCs from adult animals. There was a striking age-related rise in the expression of decorin, which was 10-fold lower in BMSCs from 1-week-old rats than in BMSCs from 12-week-old rats or 1-year-old rats. Biglycan mRNA levels, in contrast, decreased slightly with maturation. BMSCs from all age groups expressed noncartilage-specific ECM proteins (collagen types I, III, V, VII, and XII and fibronectin) at high levels, indicating incomplete differentiation to the chondrocyte phenotype even in cultures of BMSCs from 1-week-old rats. Analysis of growth factor genes showed several significant age-related differences in expression (Table 3B). Of the bone morphogenic proteins, BMP-2 and BMP-6 were the most highly expressed in BMSCs from all age groups. Whereas BMP-2 expression was similar among the different ages, there was a slight (< 2-fold) increase in signal intensity for BMP-6 in BMSCs from 1-week-old compared to BMSCs from 1-year-old rats or 12-week-old rats. Growth and differentiation factors 1 and 15 were expressed at higher levels in BMSCs from 1-week-old rats than in BMSCs from either adult age group, whereas stem cell growth factor was expressed at higher levels in BMSCs from 12-week-old and 1-year-old rats than in BMSCs from 1-week-old rats. mRNA encoding IGF-I and its receptor were slightly more abundant in BMSCs from 1-week-old rats than in BMSCs from adult rats. The IGF binding protein IGFBP-5 was markedly more abundant in BMSCs from 12-week-old and 1-year-old rats than in BMSCs from 1-week-old rats. No consistent age-related patterns of expression were observed for connective tissue growth-related peptide (CTGRP); epidermal growth factor (EGF); FGF-1 and -2; TGF-β-1, -2, and -3; platelet-derived growth factor (PDGF-α); or vascular endothelial factor (VEGF). Receptors for most of these factors were also expressed at similar levels in all age groups with the exception of the FGF-2 receptor, which was expressed at a 5-fold greater level in BMSCs from 12-week-old rats than in BMSCs from 1-week-old rats. This FGF receptor was also expressed at relatively high levels in BMSCs from 1-year-old rats (3-fold > 1-week-old). There were no striking age-related differences in signal intensity for the major catabolic cytokines interleukin-1β and tumor necrosis factor-α, which were expressed at low levels by all BMSCs. However, expression of the inducible nitric oxide synthase gene, a mediator of interleukin-1β effects, was markedly higher in BMSCs from 12-week-old and 1-year-old rats than in BMSCs from 1-week-old rats. Some age-related differences were observed in the expression of genes encoding ECM proteases that are known to affect proteoglycan and collagen integrity and stability (Table 3). mRNA levels for several secreted matrix metalloproteinases (MMP-2, MMP-3, MMP-13), and the tissue metalloproteinase, disintegrin, and cysteine-rich protease-I (tMDCI) were slightly higher in BMSCs from mature rats than in BMSCs from immature rats. Expression of the plasminogen activator and urokinase receptor Plaur, which controls extracellular serine protease activity, was slightly greater in BMSCs from 12-week-old rats than in BMSCs from 1-week-old rats, but was 4-fold greater in BMSCs from 1-year-old rats than in BMSCs from 1-week-old rats. These findings suggest that ECM accumulation in cultures of adult cells might be inhibited by the degradation of secreted proteins caused by excess collagenase and aggrecanase activities. Discussion In the present study we found strong evidence for aging effects on the in vitro chondrogenic potential of rat BMSCs. The ability of BMSCs to produce a hyaline cartilage-like ECM in high-density aggregate cultures was used as the primary measure of chondrogenic potential. ECM accumulation over 2 weeks in culture was assessed by histologic analysis with safranin-O, a stain for cartilage proteoglycans. Positive safranin-O staining signifies a complex and ordered process of ECM development because proteoglycan accumulation under these conditions depends on coordinate expression of link protein, collagens, hyaluronan, and a number of accessory proteins and processing enzymes. Examination of safranin-O-stained sections from aggregate cultures revealed that chondrogenic culture conditions induced BMSCs from 1-week-old rats to produce a hyaline cartilage-like ECM, whereas the same conditions failed to induce ECM development by BMSCs from 12-week-old or 1-year-old rats. Image analysis to determine the percentage of safranin-O–stained cells showed that nearly 85% of BMSCs from 1-week-old rats were positive whereas less than 3% of BMSCs from 12-week-old or 1-year-old rats were positive. Although there were significantly higher numbers of positive cells in cultures from 1-week-old rats, there was a still a substantial proportion of unstained cells (∼15%) on the outer edges of the aggregates. It has been suggested that this lack of staining is due to differences in oxygen tension or cell density at the periphery versus the interior of three-dimensional BMSC cultures (23,24). Immunofluorescence staining confirmed abundant proteoglycan (aggrecan), link protein, and type II collagen in 1-week-old cultures, but these cartilage markers were barely detectable in 12-week-old and 1-year-old cultures. Similar results were obtained when the incubation in chondrogenic medium was extended from 2 weeks to 6 weeks, indicating that the failure of adult cells to establish a matrix was not attributable simply to a lower rate of matrix synthesis (data not shown). Real-time PCR analysis showed that aggrecan and type II collagen were expressed at the highest levels in BMSCs from 1-week-old rats and declined significantly with maturation and aging. However, link protein and Sox9 were expressed at higher levels in BMSCs from 12-week-old rats than in BMSCs from either 1-week-old or 1-year-old rats. Taken together, these findings demonstrate that (at the RNA level) cartilage ECM protein expression changes with maturation and aging but is never entirely lost. This finding would seem to be at odds with the apparent absence of cartilage ECM proteins in aggregate cultures of BMSCs from 12-week-old and 1-year-old rats as determined by histologic analysis. This disparity might be explained by the fact that RNA was extracted from BMSCs in monolayer culture, whereas protein analysis was performed on aggregates. Gene expression in BMSCs is likely to be affected by changes in cell shape and other differences inherent in the two culture conditions. The results might also be explained by failures in any of the many posttranscriptional and posttranslational processing steps required for proper ECM assembly. Thus, despite evidence of appreciable aggrecan, link protein, and type II collagen expression at the RNA level, the assembly of these molecules into a stable ECM cannot be taken for granted. The failure to accumulate proteoglycans in culture could also be related to excessive extracellular protease activities. Such enzymes are secreted by chondrocytes and have been shown to play a role in normal cartilage ECM turnover. However, excessive extracellular proteinase activity, such as occurs in degenerative joint disease, causes rapid ECM degradation and depletion even with high rates of synthesis and secretion. This suggests excessive ECM proteolysis could also play a role in the failure of BMSCs from adult rats to form cartilage ECM. RT–PCR was used to determine if the BMSC populations were similar with respect to expression of stem cell markers. These analyses showed minimal age-related differences in the expression of BMP receptors 1a and 1b, or CD105, genes associated with osteo- and chondro- progenitors in BMSC populations (40,41). However, levels of c-Kit, a protein tyrosine kinase receptor for stem cell factor (46), appeared to decrease with increasing age, suggesting that this pathway might be less active in cells from older animals. As expected, we failed in multiple attempts to detect expression of the hematopoietic stem cell marker Sca-1 (47). These data confirmed that the cell populations used in these experiments shared a common mesenchymal origin regardless of the age of the donor animal. Microarray analysis of chondrocyte-specific gene expression generally confirmed real-time PCR results and identified a number of other age-related differences in the expression of cartilage-specific genes. Among the many intriguing findings were the age-related declines in expression of the alpha 1 and 3 chains of collagen type IX, a minor collagen that plays an important role in the assembly of type II collagen fibrils (48). This decline, together with low type II collagen synthesis, might have led to deficient collagen fiber formation, a mechanism that could explain how, despite appreciable expression of aggrecan and link protein mRNAs by adult BMSCs, the proteins failed to accumulate around chondrocytes in aggregate cultures. Another relevant finding from microarray analyses involves the TGF-β-inducible early response genes (TIEG), a subfamily of Sp1-like transcription factors that regulate the expression of tissue-specific genes (49). Our analysis showed that TIEG expression in rat BMSCs decreased with maturation from 1 week to 12 weeks by more than 50% but did not decline further with aging to 1 year. This result suggested that TIEG-dependent TGF-β responses limited chondrogenesis in adult cells, a deficiency that might be overcome with higher TGF doses. However, we found no increased chondrogenic activity with TGF-β1 up to 20 ng/mL, indicating that the amount of growth factor in the medium was not limiting (data not shown). Another chondrogenic TGF superfamily member, BMP-6 (27,28), was expressed at higher levels in young rat BMSCs than in mature rat BMSCs, suggesting that age-related differences in the expression of factors secreted by BMSCs themselves might account for some of the loss of chondrogenic activity. The recent finding that FGF-2 increased the in vitro chondrogenic potential of adult stem cells suggests the growth factor milieu needed for efficient chondrogenesis may be more complex than previously thought (29). The microarray findings also showed that type I and type III collagen were expressed at high levels by BMSCs from rats of all ages. The expression of these noncartilage-specific collagens, together with high levels of fibronectin expression, indicates that rat BMSCs had not undergone full conversion to a chondrocyte phenotype after 1 week under chondrogenic conditions. In fact, the mixed expression of these noncartilage-specific and cartilage-specific matrix proteins suggests an intermediate phenotype typical of that found in fibrocartilaginous tissues. It is unknown at this point whether noncartilage-specific gene expression declines with longer times in aggregate culture. We acknowledge that our aggregate culture system differs in some details from many previously published micromass or pellet culture systems for chondrogenic induction. However, despite different starting conditions, the aggregate and conventional micromass or pellet approaches generate high-density, three-dimensional cultures that maintain cells in a rounded shape. Moreover, the spontaneous aggregate system has some advantage over micromass or pellet cultures, which are typically established using cells collected by trypsinization immediately prior to plating. This stressful and potentially damaging step is avoided in the aggregate system, in which a three-dimensional culture is formed by the cells with no manipulation other than feeding in serum-free chondrogenic medium. Finally, we observe that monolayer-cultured BMSCs from each age group readily and consistently formed aggregates, and that the aggregates they formed had similar cell densities. We believe that this behavior indicated that there was no age-related deficit in the ability to form aggregates that could explain the deficits in subsequent matrix production. Our in vitro findings demonstrate an age-related loss of chondrogenic potency of BMSCs. However, the impact of these aging effects on the repair of osteochondral injuries in vivo remains to be proven. The age-related decline in osteochondral repair could be explained by age-related changes in the microenvironment encountered by stem cells at wound sites. For example, Kume and colleagues (50) found that glycation end products, which accumulate with aging in cartilage and other tissues, induce stem cell apoptosis and have an inhibitory effect on chondrogenesis. Another obvious possibility is age-related changes in the factors secreted by damaged tissues that could affect stem cell recruitment, proliferation, or differentiation independently of direct aging effects on stem cells. Although there is abundant in vitro evidence that stem cells are exquisitely sensitive to such environmental clues (51), there is as yet no direct experimental evidence for aging effects on injury-induced cytokine or growth factor levels in vivo. Nevertheless, this hypothesis must be tested before any firm conclusions can be drawn regarding the stem cell–specific effects of aging on osteochondral repair. The culture system we used involves nonphysiologic conditions and potential stresses to cells that they would not encounter in vivo. Thus, it is possible that our results reflect age-related differences in the responses of cells to stressful culture conditions rather than to preexisting differences in gene expression patterns. Moreover, although we used a standard protocol for BMSC isolation and worked with early passaged cells, it is difficult to tell for certain whether the cells were representative of BMSC populations in vivo. Finally, though our in vitro study provided evidence for maturation and aging effects on the chondrogenic potential of rat BMSCs, due to the complexity of conditions in fractured joints we cannot conclude at present that such effects significantly impair cartilage regeneration in vivo. Despite these caveats, we believe that our findings justify further studies in an in vivo model to directly test the role of stem cell aging on cartilage repair and on long-term recovery from cartilage injury. Decision Editor: Huber R. Warner, PhD Figure 1. Open in new tabDownload slide Expression of mesenchymal markers in rat bone marrow-derived mesenchymal stem cells (BMSCs). A, Ethidium bromide–stained agarose gel shows reverse transcription–polymerase chain reaction products representing bone morphogenic protein receptors a and b (BMPR1a, BMPR1b), CD105, Sca-1, c-Kit, and GAPDH. RNA samples were obtained from cultures of BMSCs from 1-week-old (1w), 12-week-old (12w), or 1-year-old (1y) rats. MW = 100 bp molecular weight ladders. B, Densitometric analysis of the gel in A. Intensities of the bands representing marker genes were normalized to GAPDH intensity (Relative Intensity) Figure 1. Open in new tabDownload slide Expression of mesenchymal markers in rat bone marrow-derived mesenchymal stem cells (BMSCs). A, Ethidium bromide–stained agarose gel shows reverse transcription–polymerase chain reaction products representing bone morphogenic protein receptors a and b (BMPR1a, BMPR1b), CD105, Sca-1, c-Kit, and GAPDH. RNA samples were obtained from cultures of BMSCs from 1-week-old (1w), 12-week-old (12w), or 1-year-old (1y) rats. MW = 100 bp molecular weight ladders. B, Densitometric analysis of the gel in A. Intensities of the bands representing marker genes were normalized to GAPDH intensity (Relative Intensity) Figure 2. Open in new tabDownload slide Spontaneous aggregation of bone marrow-derived mesenchymal stem cells (BMSCs) in monolayer culture. A, Monolayer culture of BMSCs in serum-containing medium 2 days after the first passage of cells from primary culture. There were no apparent age-related differences in cell shape. Bar = 200 μm. B, Low-density monolayer culture after 3 days in serum-free chondrogenic medium. Micrograph shows the polygonal chondrocyte-like shape acquired by BMSCs exposed to chondrogenic medium. C, Low-magnification image of an aggregate formed by 1-week-old BMSCs after 10 days in chondrogenic medium. The original monolayer culture has contracted and consolidated into the single mass shown. This was the case for all aggregate cultures. The gross appearance of the aggregate is typical of aggregates formed by BMSCs from all ages. Bar = 2 mm Figure 2. Open in new tabDownload slide Spontaneous aggregation of bone marrow-derived mesenchymal stem cells (BMSCs) in monolayer culture. A, Monolayer culture of BMSCs in serum-containing medium 2 days after the first passage of cells from primary culture. There were no apparent age-related differences in cell shape. Bar = 200 μm. B, Low-density monolayer culture after 3 days in serum-free chondrogenic medium. Micrograph shows the polygonal chondrocyte-like shape acquired by BMSCs exposed to chondrogenic medium. C, Low-magnification image of an aggregate formed by 1-week-old BMSCs after 10 days in chondrogenic medium. The original monolayer culture has contracted and consolidated into the single mass shown. This was the case for all aggregate cultures. The gross appearance of the aggregate is typical of aggregates formed by BMSCs from all ages. Bar = 2 mm Figure 3. Open in new tabDownload slide Effects of age on cartilage matrix formation in aggregate cultures. Low-magnification images show the overall distribution of safranin-O staining in sections of aggregate cultures after 2 weeks in chondrogenic medium. Bone marrow-derived mesenchymal stem cells (BMSCs) were obtained from 1-week-old (A), 12-week-old (B), and 1-year-old (C) rats. Bar in A = 500 μm. High-magnification images show cell morphology and pericellular safranin-O staining in BMSCs from 1-week-old (D), 12-week-old (E), and 1-year-old (F) rats. Bar in D = 50 μm. Histogram shows the results of image analysis to determine the percentage of safranin-O–positive cells in different BMSC cultures (G). Columns show means for each age based on analysis of at least three sections from three different aggregate cultures (n = 9). Error bars indicate standard deviations. Bars and asterisks show statistically significant differences in positive cell counts (p <.05) Figure 3. Open in new tabDownload slide Effects of age on cartilage matrix formation in aggregate cultures. Low-magnification images show the overall distribution of safranin-O staining in sections of aggregate cultures after 2 weeks in chondrogenic medium. Bone marrow-derived mesenchymal stem cells (BMSCs) were obtained from 1-week-old (A), 12-week-old (B), and 1-year-old (C) rats. Bar in A = 500 μm. High-magnification images show cell morphology and pericellular safranin-O staining in BMSCs from 1-week-old (D), 12-week-old (E), and 1-year-old (F) rats. Bar in D = 50 μm. Histogram shows the results of image analysis to determine the percentage of safranin-O–positive cells in different BMSC cultures (G). Columns show means for each age based on analysis of at least three sections from three different aggregate cultures (n = 9). Error bars indicate standard deviations. Bars and asterisks show statistically significant differences in positive cell counts (p <.05) Figure 4. Open in new tabDownload slide Immunofluorescence staining for cartilage matrix proteins in aggregate cultures. Results are shown for cartilage link protein (A–C), type II collagen (D–F), and aggrecan (G–I). First column: staining on a culture from 1-week-old rats (A,D, and G); second column: staining on a culture from 12-week-old rats; third column: staining on a culture from 1-year-old rats. All micrographs are the same magnification. Bar = 200 μm Figure 4. Open in new tabDownload slide Immunofluorescence staining for cartilage matrix proteins in aggregate cultures. Results are shown for cartilage link protein (A–C), type II collagen (D–F), and aggrecan (G–I). First column: staining on a culture from 1-week-old rats (A,D, and G); second column: staining on a culture from 12-week-old rats; third column: staining on a culture from 1-year-old rats. All micrographs are the same magnification. Bar = 200 μm Figure 5. Open in new tabDownload slide Effects of age on chondrocyte marker gene expression by rat bone marrow-derived mesenchymal stem cells (BMSCs). Real-time polymerase chain reaction analysis for expression of type II collagen (A), aggrecan (B), link protein (C), and Sox9 (D). Columns show mean ΔΔCt values, which indicate expression levels in BMSCs from 1-week-old and 12-week-old rats relative to BMSCs from 1-year-old rats. Error bars indicate standard deviations Figure 5. Open in new tabDownload slide Effects of age on chondrocyte marker gene expression by rat bone marrow-derived mesenchymal stem cells (BMSCs). Real-time polymerase chain reaction analysis for expression of type II collagen (A), aggrecan (B), link protein (C), and Sox9 (D). Columns show mean ΔΔCt values, which indicate expression levels in BMSCs from 1-week-old and 12-week-old rats relative to BMSCs from 1-year-old rats. Error bars indicate standard deviations Table 1. Oligonucleotide Primers for RT-PCR Analysis of Mesenchymal Cell Markers Gene Names, Accession Numbers, Associated Primer Sequences, and Expected PCR Product Lengths Are Shown. Description . Accession Number . Primer Sequence . Product (bp) . GAPDH     Forward NM_017008 AGCCCAGAACATCATCCCTG 181     Reverse CACCACCTTCTTGATGTCATC c-Kit     Forward D12524 TTGGCAAAGAAGACAACGAC 276     Reverse GCACAGACACCACTGGGATA cd105     Forward AY562420 GACGGTAACGGTGAAACT 903     Reverse ATGTAGACACGGAGGAGAAA Sca1     Forward X91619 ACCAAGGAAGCACGAAAG 455     Reverse CAGACGGGAAGGCAAATA BMPr1a     Forward NM_030849 CCATTGCTTTGCCATTAT 530     Reverse TTTCACCACGCCATTTAC BMPr1b     Forward BC092609 ACATTCCACCCAACACCC 293     Reverse GCACTCGTCACTGCTCCA Description . Accession Number . Primer Sequence . Product (bp) . GAPDH     Forward NM_017008 AGCCCAGAACATCATCCCTG 181     Reverse CACCACCTTCTTGATGTCATC c-Kit     Forward D12524 TTGGCAAAGAAGACAACGAC 276     Reverse GCACAGACACCACTGGGATA cd105     Forward AY562420 GACGGTAACGGTGAAACT 903     Reverse ATGTAGACACGGAGGAGAAA Sca1     Forward X91619 ACCAAGGAAGCACGAAAG 455     Reverse CAGACGGGAAGGCAAATA BMPr1a     Forward NM_030849 CCATTGCTTTGCCATTAT 530     Reverse TTTCACCACGCCATTTAC BMPr1b     Forward BC092609 ACATTCCACCCAACACCC 293     Reverse GCACTCGTCACTGCTCCA Open in new tab Table 1. Oligonucleotide Primers for RT-PCR Analysis of Mesenchymal Cell Markers Gene Names, Accession Numbers, Associated Primer Sequences, and Expected PCR Product Lengths Are Shown. Description . Accession Number . Primer Sequence . Product (bp) . GAPDH     Forward NM_017008 AGCCCAGAACATCATCCCTG 181     Reverse CACCACCTTCTTGATGTCATC c-Kit     Forward D12524 TTGGCAAAGAAGACAACGAC 276     Reverse GCACAGACACCACTGGGATA cd105     Forward AY562420 GACGGTAACGGTGAAACT 903     Reverse ATGTAGACACGGAGGAGAAA Sca1     Forward X91619 ACCAAGGAAGCACGAAAG 455     Reverse CAGACGGGAAGGCAAATA BMPr1a     Forward NM_030849 CCATTGCTTTGCCATTAT 530     Reverse TTTCACCACGCCATTTAC BMPr1b     Forward BC092609 ACATTCCACCCAACACCC 293     Reverse GCACTCGTCACTGCTCCA Description . Accession Number . Primer Sequence . Product (bp) . GAPDH     Forward NM_017008 AGCCCAGAACATCATCCCTG 181     Reverse CACCACCTTCTTGATGTCATC c-Kit     Forward D12524 TTGGCAAAGAAGACAACGAC 276     Reverse GCACAGACACCACTGGGATA cd105     Forward AY562420 GACGGTAACGGTGAAACT 903     Reverse ATGTAGACACGGAGGAGAAA Sca1     Forward X91619 ACCAAGGAAGCACGAAAG 455     Reverse CAGACGGGAAGGCAAATA BMPr1a     Forward NM_030849 CCATTGCTTTGCCATTAT 530     Reverse TTTCACCACGCCATTTAC BMPr1b     Forward BC092609 ACATTCCACCCAACACCC 293     Reverse GCACTCGTCACTGCTCCA Open in new tab Table 2. Oligonucleotide Primers for Real-Time Polymerase Chain Reaction Analysis of Cartilage Matrix Gene Expression. Description . Accession Number . Primer Sequence . GAPDH     forward NM_017008 GGCACAGTAAGGCTGAGAAT     reverse TCTCGCTCCTGGAAGATGGT Col2     forward L48440 GAGTGGAAGAGCGGAGACTACTG     reverse CTCCATGTTGCAGAAGACTTTCA Aggrecan     forward NM_022190 TTGTGACTCTGCGGGTCATC     reverse GTCCCTAGGAGGGCCTTCAG Link     forward NM_019189 GATGGTGCTCAGATTGCGAAA     reverse CGGTCATAGCCCAGAAGCTT Sox9     forward AB073720 AGAGCGTTGCTCGGAACTGT     Reverse TCCTGGACCGAAACTGGTAAA Description . Accession Number . Primer Sequence . GAPDH     forward NM_017008 GGCACAGTAAGGCTGAGAAT     reverse TCTCGCTCCTGGAAGATGGT Col2     forward L48440 GAGTGGAAGAGCGGAGACTACTG     reverse CTCCATGTTGCAGAAGACTTTCA Aggrecan     forward NM_022190 TTGTGACTCTGCGGGTCATC     reverse GTCCCTAGGAGGGCCTTCAG Link     forward NM_019189 GATGGTGCTCAGATTGCGAAA     reverse CGGTCATAGCCCAGAAGCTT Sox9     forward AB073720 AGAGCGTTGCTCGGAACTGT     Reverse TCCTGGACCGAAACTGGTAAA Note: Gene names, accession numbers, and associated primer sequences are indicated. Open in new tab Table 2. Oligonucleotide Primers for Real-Time Polymerase Chain Reaction Analysis of Cartilage Matrix Gene Expression. Description . Accession Number . Primer Sequence . GAPDH     forward NM_017008 GGCACAGTAAGGCTGAGAAT     reverse TCTCGCTCCTGGAAGATGGT Col2     forward L48440 GAGTGGAAGAGCGGAGACTACTG     reverse CTCCATGTTGCAGAAGACTTTCA Aggrecan     forward NM_022190 TTGTGACTCTGCGGGTCATC     reverse GTCCCTAGGAGGGCCTTCAG Link     forward NM_019189 GATGGTGCTCAGATTGCGAAA     reverse CGGTCATAGCCCAGAAGCTT Sox9     forward AB073720 AGAGCGTTGCTCGGAACTGT     Reverse TCCTGGACCGAAACTGGTAAA Description . Accession Number . Primer Sequence . GAPDH     forward NM_017008 GGCACAGTAAGGCTGAGAAT     reverse TCTCGCTCCTGGAAGATGGT Col2     forward L48440 GAGTGGAAGAGCGGAGACTACTG     reverse CTCCATGTTGCAGAAGACTTTCA Aggrecan     forward NM_022190 TTGTGACTCTGCGGGTCATC     reverse GTCCCTAGGAGGGCCTTCAG Link     forward NM_019189 GATGGTGCTCAGATTGCGAAA     reverse CGGTCATAGCCCAGAAGCTT Sox9     forward AB073720 AGAGCGTTGCTCGGAACTGT     Reverse TCCTGGACCGAAACTGGTAAA Note: Gene names, accession numbers, and associated primer sequences are indicated. Open in new tab Table 3. Microarray Analysis of Aging Effects on BMSC Gene Expression Results are Shown for BMSCs from 1 Week-Old (1W), 12-Week-Old (12W), or 1-Year-Old (1Y) Rats, and Are Reported in Terms of Signal Intensities After Gene Chip Normalization. . . . . Signal Intensity . . . Probe I.D. . Name . Accession No. . Description . 1 Week . 12 Weeks . 1 Year . Extracellular matrix related     1370864_at COLIA1 Z78279 Collagen, type 1, alpha 1 89,529 78,763 88,327     1371226_at CG2A1A AF305418 Procollagen, type II, alpha 1 281 122 158     1370959_at Col3a1 BI275716 Collagen, type III, alpha 1 97,886 90,437 99,196     1372439_at Col4a1 AI176393 Procollagen, type IV, alpha 1 (predicted) 13,011 16,844 26,712     1369955_at Col5a1 NM_134452 Collagen, typeV, alpha 1 22,561 21,158 19,793     1371369_at Col6a2 BI287851 Procollagen, type VI, alpha 2 (predicated) 8673 8471 8049     1374226_at Col6a1 AI408498 Procollagen, type VII, alpha 1 (predicated) 72 288 402     1374172_at Col7a2 AI010883 Procollagen, type VIII, alpha 2 (predicted) 40,814 35,629 32,957     1388973_at Col9a1 BM388861 Procollagen, type IX, alpha 1 (predicted) 153 25 29     1382855_at Col9a3 BI295963 Similar to alpha-3 type IX collagen 700 366 230     1370944_at Col10a1 AI230238 Collagen, type X, alpha 1 251 158 138     1392915_at Col11a1 BM389291 Procollagen, type XI, alpha 1 63,673 71,443 67,900     1370927_at Col12a1 BE108345 Procollagen, type XII, alpha 1 39,680 40,426 41,852     1379345_at Col14 BM386752 Procollagen, type XV (predicted) 1296 432 841     1387355_at Agc1 BM384639 Aggrecan 1 1248 495 164     1368685_at Ng2 NM_031022 Chondroitin sulfate proteoglycan 4 2672 2735 3556     1387080_at bamacan NM_031583 Chondroitin sulfate proteoglycan 6 4900 3498 3942     1270125_at Crtl1 NM_019189 Cartilage link protein 1 210 495 167     1370956_at Dcn BM390253 Decorin 4237 43,903 41,346     1367594_at BSPG1 NM_017087 Biglycan 36,628 25,251 24,940     1387137_at Comp NM_012834 Cartilage oligomeric matrix protein 1147 768 409     1370234_at Fn1 AA893484 Fibronectin 1 76,650 64,057 72,100     1387548_at Has2 NM_013153 Hyaluronan synthase 2 2065 1157 1078     1368171_at Rrg1 NM_017061 Lysyl oxidase 69,667 67,238 67,451 Growth factor related     1368945_at BMP2 NM_017178 Bone morphogenic protein-2 996 550 781     1387080_at BMP6 AW141680 Bone morphogenic protein-6 1766 1571 2776     1367631_at CTGRP NM_022266 Connective tissue growth factor 101,286 88,487 106,912     1368325_at Egf NM_012842 Epidermal growth factor 198 199 252     1370699_a_at ErbB-1 AF187818 Epidermal growth factor receptor 2676 1996 2060     1393314_at Fgf1 BI289840 Fibroblast growth factor 1 4838 7778 5536     1387606_at bFGF/Fgf-2 NM_019305 Fibroblast growth factor 2 366 328 331     1372447_at Fgfr1 BI275155 Fibroblast growth factor receptor 1 5711 7363 7806     1373829_at Fgfr2 AI412658 Fibroblast growth factor receptor 2 1666 81484 5619     1377506_at Gdf1 BI289525 Growth differentiation factor 1 (predicted) 785 274 351     1370153_at Gdf15 NM_019216 Growth differentiation factor 15 352 155 84     1370333_a_at Igf1 M15481 Insulin-like growth factor 1 2918 1508 2197     1368123_at JTK13 NM_052807 Insulin-like growth factor 1 receptor 3291 2414 2819     1367571_a_at IGFII NM_031511 Insulin-like growth factor 2 685 712 948     1386872_at Igf2r BI296290 Insulin-like growth factor 2 receptor 8391 4615 5105     1386881_at IGF-BP3 NM_012588 Insulin-like growth factor binding protein 3 1004 565 986     1370960_at Igfbp5 BE104060 Insulin-like growth factor-binding protein 5 506 5093 5680     1370941_at Pdgfra AI232379 Platelet-derived growth factor receptor, alpha 4586 6666 6245     1379375_at Pdgfa BE100812 Platelet-derived growth factor, alpha 4743 4940 5249     1392672_at Scgf AI576758 Stem cell growth factor 2451 6277 3784     1381449_s_at Tgfa BG670310 Transforming growth factor alpha 4577 4436 5546     1370887_at Tgfb1i1 BI279862 Transforming growth factor beta 1 induced 1 12,445 7277 10,286     1370082_at Tgfb1 NM_021578 Transforming growth factor, beta 1 3049 3266 3129     1387172_a_at Tgfb2 NM_031131 Transforming growth factor, beta 2 6647 5653 7285     1367859_at Tgfb3 NM_013174 Transforming growth factor, beta 3 21,923 12,922 18,125     1369504_at Tgfbr1 NM_012775 Transforming growth factor, beta receptor 1 864 468 601     1372466_at Tgfbr2 AI408571 Transforming growth factor, beta receptor II 18,724 17,629 23,080     1387484_at Betaglycan NM_017256 Transforming growth factor, beta receptor III 324 546 548     1368650 Tieg NM_031135 Transforming growth factor beta inducible early growth response 2770 1158 1437     1370081_a_at Vegf AF080594 Vascular endothelial growth factor 3219 2482 2917 Cytokine related     1398256_at II1B NM_031512 Interleukin 1 beta 157 144 125     1369665_a_at IL-18 AJ222813 Interleukin 18 1793 649 756     1369191_at Ilg6; Ifnb2 NM_012589 Interleukin 6 277 125 166     1387691_at TNFa NM_012675.1 Tumor necrosis factor alpha 52 152 48     1384842_s_at Tnfrsf6 AI231531 Tumor necrosis factor receptor superfamily, member 6 2608 995 1939     1371289_at iNos U16359 Inducible nitric oxide synthase gene 79 847 703 Matrix proteinases     1369825_at MMP2 NM_031054 Matrix metalloproteinase 2 281 316 515     1368657_at MMP3 NM_133523 Matrix metalloproteinase 3 854 2200 1228     1368766_at MMP7 NM_012864 Matrix metalloproteinase 7 19 42 37     1398275_at MMP7 NM_031055 Matrix metalloproteinase 9 54 18 12     1368713_at MMP10 NM_133514 Matrix metalloproteinase 10 119 16 96     1367858_at MMP11 NM_012980 Matrix metalloproteinase 11 303 270 303     1368530_at MMP12 NM_053963 Matrix metalloproteinase 12 130 97 229     1388204_at MMP13 M60616 Matrix metalloproteinase 13 313 736 636     1368590_at MMP16 NM_080776 Matrix metalloproteinase 16 819 950 616     1391095_at MMP19 BI294977 Matrix metalloproteinase 19 528 829 930     1368961_at MMP23 NM_053606 Matrix metalloproteinase 23 6025 7795 9523     1389833_at MMP24 BF285924 Matrix metalloproteinase 24 1754 1495 1878     1369832_at tMDCI NM_020302 A disintegrin and metalloprotease domain 3 59 219 379     1367712_at TIMP-1 NM_053819 Tissue inhibitor of metalloproteinase 1 36,065 43,051 48,239     1387005_at Ctss NM_017320 Cathepsin S 12,150 3,046 3,339     1368223_at Adamts1 NM_024400 A disintegrin and metalloprotease 1 11,468 16,521 13,710     1368224_at SPI3 NM_031531 Serine protease inhibitor 238 21,504 18,070     1387269_s_at Plaur AF007789 Plasminogen activator, urokinase receptor 2979 3714 11,388 . . . . Signal Intensity . . . Probe I.D. . Name . Accession No. . Description . 1 Week . 12 Weeks . 1 Year . Extracellular matrix related     1370864_at COLIA1 Z78279 Collagen, type 1, alpha 1 89,529 78,763 88,327     1371226_at CG2A1A AF305418 Procollagen, type II, alpha 1 281 122 158     1370959_at Col3a1 BI275716 Collagen, type III, alpha 1 97,886 90,437 99,196     1372439_at Col4a1 AI176393 Procollagen, type IV, alpha 1 (predicted) 13,011 16,844 26,712     1369955_at Col5a1 NM_134452 Collagen, typeV, alpha 1 22,561 21,158 19,793     1371369_at Col6a2 BI287851 Procollagen, type VI, alpha 2 (predicated) 8673 8471 8049     1374226_at Col6a1 AI408498 Procollagen, type VII, alpha 1 (predicated) 72 288 402     1374172_at Col7a2 AI010883 Procollagen, type VIII, alpha 2 (predicted) 40,814 35,629 32,957     1388973_at Col9a1 BM388861 Procollagen, type IX, alpha 1 (predicted) 153 25 29     1382855_at Col9a3 BI295963 Similar to alpha-3 type IX collagen 700 366 230     1370944_at Col10a1 AI230238 Collagen, type X, alpha 1 251 158 138     1392915_at Col11a1 BM389291 Procollagen, type XI, alpha 1 63,673 71,443 67,900     1370927_at Col12a1 BE108345 Procollagen, type XII, alpha 1 39,680 40,426 41,852     1379345_at Col14 BM386752 Procollagen, type XV (predicted) 1296 432 841     1387355_at Agc1 BM384639 Aggrecan 1 1248 495 164     1368685_at Ng2 NM_031022 Chondroitin sulfate proteoglycan 4 2672 2735 3556     1387080_at bamacan NM_031583 Chondroitin sulfate proteoglycan 6 4900 3498 3942     1270125_at Crtl1 NM_019189 Cartilage link protein 1 210 495 167     1370956_at Dcn BM390253 Decorin 4237 43,903 41,346     1367594_at BSPG1 NM_017087 Biglycan 36,628 25,251 24,940     1387137_at Comp NM_012834 Cartilage oligomeric matrix protein 1147 768 409     1370234_at Fn1 AA893484 Fibronectin 1 76,650 64,057 72,100     1387548_at Has2 NM_013153 Hyaluronan synthase 2 2065 1157 1078     1368171_at Rrg1 NM_017061 Lysyl oxidase 69,667 67,238 67,451 Growth factor related     1368945_at BMP2 NM_017178 Bone morphogenic protein-2 996 550 781     1387080_at BMP6 AW141680 Bone morphogenic protein-6 1766 1571 2776     1367631_at CTGRP NM_022266 Connective tissue growth factor 101,286 88,487 106,912     1368325_at Egf NM_012842 Epidermal growth factor 198 199 252     1370699_a_at ErbB-1 AF187818 Epidermal growth factor receptor 2676 1996 2060     1393314_at Fgf1 BI289840 Fibroblast growth factor 1 4838 7778 5536     1387606_at bFGF/Fgf-2 NM_019305 Fibroblast growth factor 2 366 328 331     1372447_at Fgfr1 BI275155 Fibroblast growth factor receptor 1 5711 7363 7806     1373829_at Fgfr2 AI412658 Fibroblast growth factor receptor 2 1666 81484 5619     1377506_at Gdf1 BI289525 Growth differentiation factor 1 (predicted) 785 274 351     1370153_at Gdf15 NM_019216 Growth differentiation factor 15 352 155 84     1370333_a_at Igf1 M15481 Insulin-like growth factor 1 2918 1508 2197     1368123_at JTK13 NM_052807 Insulin-like growth factor 1 receptor 3291 2414 2819     1367571_a_at IGFII NM_031511 Insulin-like growth factor 2 685 712 948     1386872_at Igf2r BI296290 Insulin-like growth factor 2 receptor 8391 4615 5105     1386881_at IGF-BP3 NM_012588 Insulin-like growth factor binding protein 3 1004 565 986     1370960_at Igfbp5 BE104060 Insulin-like growth factor-binding protein 5 506 5093 5680     1370941_at Pdgfra AI232379 Platelet-derived growth factor receptor, alpha 4586 6666 6245     1379375_at Pdgfa BE100812 Platelet-derived growth factor, alpha 4743 4940 5249     1392672_at Scgf AI576758 Stem cell growth factor 2451 6277 3784     1381449_s_at Tgfa BG670310 Transforming growth factor alpha 4577 4436 5546     1370887_at Tgfb1i1 BI279862 Transforming growth factor beta 1 induced 1 12,445 7277 10,286     1370082_at Tgfb1 NM_021578 Transforming growth factor, beta 1 3049 3266 3129     1387172_a_at Tgfb2 NM_031131 Transforming growth factor, beta 2 6647 5653 7285     1367859_at Tgfb3 NM_013174 Transforming growth factor, beta 3 21,923 12,922 18,125     1369504_at Tgfbr1 NM_012775 Transforming growth factor, beta receptor 1 864 468 601     1372466_at Tgfbr2 AI408571 Transforming growth factor, beta receptor II 18,724 17,629 23,080     1387484_at Betaglycan NM_017256 Transforming growth factor, beta receptor III 324 546 548     1368650 Tieg NM_031135 Transforming growth factor beta inducible early growth response 2770 1158 1437     1370081_a_at Vegf AF080594 Vascular endothelial growth factor 3219 2482 2917 Cytokine related     1398256_at II1B NM_031512 Interleukin 1 beta 157 144 125     1369665_a_at IL-18 AJ222813 Interleukin 18 1793 649 756     1369191_at Ilg6; Ifnb2 NM_012589 Interleukin 6 277 125 166     1387691_at TNFa NM_012675.1 Tumor necrosis factor alpha 52 152 48     1384842_s_at Tnfrsf6 AI231531 Tumor necrosis factor receptor superfamily, member 6 2608 995 1939     1371289_at iNos U16359 Inducible nitric oxide synthase gene 79 847 703 Matrix proteinases     1369825_at MMP2 NM_031054 Matrix metalloproteinase 2 281 316 515     1368657_at MMP3 NM_133523 Matrix metalloproteinase 3 854 2200 1228     1368766_at MMP7 NM_012864 Matrix metalloproteinase 7 19 42 37     1398275_at MMP7 NM_031055 Matrix metalloproteinase 9 54 18 12     1368713_at MMP10 NM_133514 Matrix metalloproteinase 10 119 16 96     1367858_at MMP11 NM_012980 Matrix metalloproteinase 11 303 270 303     1368530_at MMP12 NM_053963 Matrix metalloproteinase 12 130 97 229     1388204_at MMP13 M60616 Matrix metalloproteinase 13 313 736 636     1368590_at MMP16 NM_080776 Matrix metalloproteinase 16 819 950 616     1391095_at MMP19 BI294977 Matrix metalloproteinase 19 528 829 930     1368961_at MMP23 NM_053606 Matrix metalloproteinase 23 6025 7795 9523     1389833_at MMP24 BF285924 Matrix metalloproteinase 24 1754 1495 1878     1369832_at tMDCI NM_020302 A disintegrin and metalloprotease domain 3 59 219 379     1367712_at TIMP-1 NM_053819 Tissue inhibitor of metalloproteinase 1 36,065 43,051 48,239     1387005_at Ctss NM_017320 Cathepsin S 12,150 3,046 3,339     1368223_at Adamts1 NM_024400 A disintegrin and metalloprotease 1 11,468 16,521 13,710     1368224_at SPI3 NM_031531 Serine protease inhibitor 238 21,504 18,070     1387269_s_at Plaur AF007789 Plasminogen activator, urokinase receptor 2979 3714 11,388 Note: BMSC = bone marrow stem cell. Open in new tab Table 3. Microarray Analysis of Aging Effects on BMSC Gene Expression Results are Shown for BMSCs from 1 Week-Old (1W), 12-Week-Old (12W), or 1-Year-Old (1Y) Rats, and Are Reported in Terms of Signal Intensities After Gene Chip Normalization. . . . . Signal Intensity . . . Probe I.D. . Name . Accession No. . Description . 1 Week . 12 Weeks . 1 Year . Extracellular matrix related     1370864_at COLIA1 Z78279 Collagen, type 1, alpha 1 89,529 78,763 88,327     1371226_at CG2A1A AF305418 Procollagen, type II, alpha 1 281 122 158     1370959_at Col3a1 BI275716 Collagen, type III, alpha 1 97,886 90,437 99,196     1372439_at Col4a1 AI176393 Procollagen, type IV, alpha 1 (predicted) 13,011 16,844 26,712     1369955_at Col5a1 NM_134452 Collagen, typeV, alpha 1 22,561 21,158 19,793     1371369_at Col6a2 BI287851 Procollagen, type VI, alpha 2 (predicated) 8673 8471 8049     1374226_at Col6a1 AI408498 Procollagen, type VII, alpha 1 (predicated) 72 288 402     1374172_at Col7a2 AI010883 Procollagen, type VIII, alpha 2 (predicted) 40,814 35,629 32,957     1388973_at Col9a1 BM388861 Procollagen, type IX, alpha 1 (predicted) 153 25 29     1382855_at Col9a3 BI295963 Similar to alpha-3 type IX collagen 700 366 230     1370944_at Col10a1 AI230238 Collagen, type X, alpha 1 251 158 138     1392915_at Col11a1 BM389291 Procollagen, type XI, alpha 1 63,673 71,443 67,900     1370927_at Col12a1 BE108345 Procollagen, type XII, alpha 1 39,680 40,426 41,852     1379345_at Col14 BM386752 Procollagen, type XV (predicted) 1296 432 841     1387355_at Agc1 BM384639 Aggrecan 1 1248 495 164     1368685_at Ng2 NM_031022 Chondroitin sulfate proteoglycan 4 2672 2735 3556     1387080_at bamacan NM_031583 Chondroitin sulfate proteoglycan 6 4900 3498 3942     1270125_at Crtl1 NM_019189 Cartilage link protein 1 210 495 167     1370956_at Dcn BM390253 Decorin 4237 43,903 41,346     1367594_at BSPG1 NM_017087 Biglycan 36,628 25,251 24,940     1387137_at Comp NM_012834 Cartilage oligomeric matrix protein 1147 768 409     1370234_at Fn1 AA893484 Fibronectin 1 76,650 64,057 72,100     1387548_at Has2 NM_013153 Hyaluronan synthase 2 2065 1157 1078     1368171_at Rrg1 NM_017061 Lysyl oxidase 69,667 67,238 67,451 Growth factor related     1368945_at BMP2 NM_017178 Bone morphogenic protein-2 996 550 781     1387080_at BMP6 AW141680 Bone morphogenic protein-6 1766 1571 2776     1367631_at CTGRP NM_022266 Connective tissue growth factor 101,286 88,487 106,912     1368325_at Egf NM_012842 Epidermal growth factor 198 199 252     1370699_a_at ErbB-1 AF187818 Epidermal growth factor receptor 2676 1996 2060     1393314_at Fgf1 BI289840 Fibroblast growth factor 1 4838 7778 5536     1387606_at bFGF/Fgf-2 NM_019305 Fibroblast growth factor 2 366 328 331     1372447_at Fgfr1 BI275155 Fibroblast growth factor receptor 1 5711 7363 7806     1373829_at Fgfr2 AI412658 Fibroblast growth factor receptor 2 1666 81484 5619     1377506_at Gdf1 BI289525 Growth differentiation factor 1 (predicted) 785 274 351     1370153_at Gdf15 NM_019216 Growth differentiation factor 15 352 155 84     1370333_a_at Igf1 M15481 Insulin-like growth factor 1 2918 1508 2197     1368123_at JTK13 NM_052807 Insulin-like growth factor 1 receptor 3291 2414 2819     1367571_a_at IGFII NM_031511 Insulin-like growth factor 2 685 712 948     1386872_at Igf2r BI296290 Insulin-like growth factor 2 receptor 8391 4615 5105     1386881_at IGF-BP3 NM_012588 Insulin-like growth factor binding protein 3 1004 565 986     1370960_at Igfbp5 BE104060 Insulin-like growth factor-binding protein 5 506 5093 5680     1370941_at Pdgfra AI232379 Platelet-derived growth factor receptor, alpha 4586 6666 6245     1379375_at Pdgfa BE100812 Platelet-derived growth factor, alpha 4743 4940 5249     1392672_at Scgf AI576758 Stem cell growth factor 2451 6277 3784     1381449_s_at Tgfa BG670310 Transforming growth factor alpha 4577 4436 5546     1370887_at Tgfb1i1 BI279862 Transforming growth factor beta 1 induced 1 12,445 7277 10,286     1370082_at Tgfb1 NM_021578 Transforming growth factor, beta 1 3049 3266 3129     1387172_a_at Tgfb2 NM_031131 Transforming growth factor, beta 2 6647 5653 7285     1367859_at Tgfb3 NM_013174 Transforming growth factor, beta 3 21,923 12,922 18,125     1369504_at Tgfbr1 NM_012775 Transforming growth factor, beta receptor 1 864 468 601     1372466_at Tgfbr2 AI408571 Transforming growth factor, beta receptor II 18,724 17,629 23,080     1387484_at Betaglycan NM_017256 Transforming growth factor, beta receptor III 324 546 548     1368650 Tieg NM_031135 Transforming growth factor beta inducible early growth response 2770 1158 1437     1370081_a_at Vegf AF080594 Vascular endothelial growth factor 3219 2482 2917 Cytokine related     1398256_at II1B NM_031512 Interleukin 1 beta 157 144 125     1369665_a_at IL-18 AJ222813 Interleukin 18 1793 649 756     1369191_at Ilg6; Ifnb2 NM_012589 Interleukin 6 277 125 166     1387691_at TNFa NM_012675.1 Tumor necrosis factor alpha 52 152 48     1384842_s_at Tnfrsf6 AI231531 Tumor necrosis factor receptor superfamily, member 6 2608 995 1939     1371289_at iNos U16359 Inducible nitric oxide synthase gene 79 847 703 Matrix proteinases     1369825_at MMP2 NM_031054 Matrix metalloproteinase 2 281 316 515     1368657_at MMP3 NM_133523 Matrix metalloproteinase 3 854 2200 1228     1368766_at MMP7 NM_012864 Matrix metalloproteinase 7 19 42 37     1398275_at MMP7 NM_031055 Matrix metalloproteinase 9 54 18 12     1368713_at MMP10 NM_133514 Matrix metalloproteinase 10 119 16 96     1367858_at MMP11 NM_012980 Matrix metalloproteinase 11 303 270 303     1368530_at MMP12 NM_053963 Matrix metalloproteinase 12 130 97 229     1388204_at MMP13 M60616 Matrix metalloproteinase 13 313 736 636     1368590_at MMP16 NM_080776 Matrix metalloproteinase 16 819 950 616     1391095_at MMP19 BI294977 Matrix metalloproteinase 19 528 829 930     1368961_at MMP23 NM_053606 Matrix metalloproteinase 23 6025 7795 9523     1389833_at MMP24 BF285924 Matrix metalloproteinase 24 1754 1495 1878     1369832_at tMDCI NM_020302 A disintegrin and metalloprotease domain 3 59 219 379     1367712_at TIMP-1 NM_053819 Tissue inhibitor of metalloproteinase 1 36,065 43,051 48,239     1387005_at Ctss NM_017320 Cathepsin S 12,150 3,046 3,339     1368223_at Adamts1 NM_024400 A disintegrin and metalloprotease 1 11,468 16,521 13,710     1368224_at SPI3 NM_031531 Serine protease inhibitor 238 21,504 18,070     1387269_s_at Plaur AF007789 Plasminogen activator, urokinase receptor 2979 3714 11,388 . . . . Signal Intensity . . . Probe I.D. . Name . Accession No. . Description . 1 Week . 12 Weeks . 1 Year . Extracellular matrix related     1370864_at COLIA1 Z78279 Collagen, type 1, alpha 1 89,529 78,763 88,327     1371226_at CG2A1A AF305418 Procollagen, type II, alpha 1 281 122 158     1370959_at Col3a1 BI275716 Collagen, type III, alpha 1 97,886 90,437 99,196     1372439_at Col4a1 AI176393 Procollagen, type IV, alpha 1 (predicted) 13,011 16,844 26,712     1369955_at Col5a1 NM_134452 Collagen, typeV, alpha 1 22,561 21,158 19,793     1371369_at Col6a2 BI287851 Procollagen, type VI, alpha 2 (predicated) 8673 8471 8049     1374226_at Col6a1 AI408498 Procollagen, type VII, alpha 1 (predicated) 72 288 402     1374172_at Col7a2 AI010883 Procollagen, type VIII, alpha 2 (predicted) 40,814 35,629 32,957     1388973_at Col9a1 BM388861 Procollagen, type IX, alpha 1 (predicted) 153 25 29     1382855_at Col9a3 BI295963 Similar to alpha-3 type IX collagen 700 366 230     1370944_at Col10a1 AI230238 Collagen, type X, alpha 1 251 158 138     1392915_at Col11a1 BM389291 Procollagen, type XI, alpha 1 63,673 71,443 67,900     1370927_at Col12a1 BE108345 Procollagen, type XII, alpha 1 39,680 40,426 41,852     1379345_at Col14 BM386752 Procollagen, type XV (predicted) 1296 432 841     1387355_at Agc1 BM384639 Aggrecan 1 1248 495 164     1368685_at Ng2 NM_031022 Chondroitin sulfate proteoglycan 4 2672 2735 3556     1387080_at bamacan NM_031583 Chondroitin sulfate proteoglycan 6 4900 3498 3942     1270125_at Crtl1 NM_019189 Cartilage link protein 1 210 495 167     1370956_at Dcn BM390253 Decorin 4237 43,903 41,346     1367594_at BSPG1 NM_017087 Biglycan 36,628 25,251 24,940     1387137_at Comp NM_012834 Cartilage oligomeric matrix protein 1147 768 409     1370234_at Fn1 AA893484 Fibronectin 1 76,650 64,057 72,100     1387548_at Has2 NM_013153 Hyaluronan synthase 2 2065 1157 1078     1368171_at Rrg1 NM_017061 Lysyl oxidase 69,667 67,238 67,451 Growth factor related     1368945_at BMP2 NM_017178 Bone morphogenic protein-2 996 550 781     1387080_at BMP6 AW141680 Bone morphogenic protein-6 1766 1571 2776     1367631_at CTGRP NM_022266 Connective tissue growth factor 101,286 88,487 106,912     1368325_at Egf NM_012842 Epidermal growth factor 198 199 252     1370699_a_at ErbB-1 AF187818 Epidermal growth factor receptor 2676 1996 2060     1393314_at Fgf1 BI289840 Fibroblast growth factor 1 4838 7778 5536     1387606_at bFGF/Fgf-2 NM_019305 Fibroblast growth factor 2 366 328 331     1372447_at Fgfr1 BI275155 Fibroblast growth factor receptor 1 5711 7363 7806     1373829_at Fgfr2 AI412658 Fibroblast growth factor receptor 2 1666 81484 5619     1377506_at Gdf1 BI289525 Growth differentiation factor 1 (predicted) 785 274 351     1370153_at Gdf15 NM_019216 Growth differentiation factor 15 352 155 84     1370333_a_at Igf1 M15481 Insulin-like growth factor 1 2918 1508 2197     1368123_at JTK13 NM_052807 Insulin-like growth factor 1 receptor 3291 2414 2819     1367571_a_at IGFII NM_031511 Insulin-like growth factor 2 685 712 948     1386872_at Igf2r BI296290 Insulin-like growth factor 2 receptor 8391 4615 5105     1386881_at IGF-BP3 NM_012588 Insulin-like growth factor binding protein 3 1004 565 986     1370960_at Igfbp5 BE104060 Insulin-like growth factor-binding protein 5 506 5093 5680     1370941_at Pdgfra AI232379 Platelet-derived growth factor receptor, alpha 4586 6666 6245     1379375_at Pdgfa BE100812 Platelet-derived growth factor, alpha 4743 4940 5249     1392672_at Scgf AI576758 Stem cell growth factor 2451 6277 3784     1381449_s_at Tgfa BG670310 Transforming growth factor alpha 4577 4436 5546     1370887_at Tgfb1i1 BI279862 Transforming growth factor beta 1 induced 1 12,445 7277 10,286     1370082_at Tgfb1 NM_021578 Transforming growth factor, beta 1 3049 3266 3129     1387172_a_at Tgfb2 NM_031131 Transforming growth factor, beta 2 6647 5653 7285     1367859_at Tgfb3 NM_013174 Transforming growth factor, beta 3 21,923 12,922 18,125     1369504_at Tgfbr1 NM_012775 Transforming growth factor, beta receptor 1 864 468 601     1372466_at Tgfbr2 AI408571 Transforming growth factor, beta receptor II 18,724 17,629 23,080     1387484_at Betaglycan NM_017256 Transforming growth factor, beta receptor III 324 546 548     1368650 Tieg NM_031135 Transforming growth factor beta inducible early growth response 2770 1158 1437     1370081_a_at Vegf AF080594 Vascular endothelial growth factor 3219 2482 2917 Cytokine related     1398256_at II1B NM_031512 Interleukin 1 beta 157 144 125     1369665_a_at IL-18 AJ222813 Interleukin 18 1793 649 756     1369191_at Ilg6; Ifnb2 NM_012589 Interleukin 6 277 125 166     1387691_at TNFa NM_012675.1 Tumor necrosis factor alpha 52 152 48     1384842_s_at Tnfrsf6 AI231531 Tumor necrosis factor receptor superfamily, member 6 2608 995 1939     1371289_at iNos U16359 Inducible nitric oxide synthase gene 79 847 703 Matrix proteinases     1369825_at MMP2 NM_031054 Matrix metalloproteinase 2 281 316 515     1368657_at MMP3 NM_133523 Matrix metalloproteinase 3 854 2200 1228     1368766_at MMP7 NM_012864 Matrix metalloproteinase 7 19 42 37     1398275_at MMP7 NM_031055 Matrix metalloproteinase 9 54 18 12     1368713_at MMP10 NM_133514 Matrix metalloproteinase 10 119 16 96     1367858_at MMP11 NM_012980 Matrix metalloproteinase 11 303 270 303     1368530_at MMP12 NM_053963 Matrix metalloproteinase 12 130 97 229     1388204_at MMP13 M60616 Matrix metalloproteinase 13 313 736 636     1368590_at MMP16 NM_080776 Matrix metalloproteinase 16 819 950 616     1391095_at MMP19 BI294977 Matrix metalloproteinase 19 528 829 930     1368961_at MMP23 NM_053606 Matrix metalloproteinase 23 6025 7795 9523     1389833_at MMP24 BF285924 Matrix metalloproteinase 24 1754 1495 1878     1369832_at tMDCI NM_020302 A disintegrin and metalloprotease domain 3 59 219 379     1367712_at TIMP-1 NM_053819 Tissue inhibitor of metalloproteinase 1 36,065 43,051 48,239     1387005_at Ctss NM_017320 Cathepsin S 12,150 3,046 3,339     1368223_at Adamts1 NM_024400 A disintegrin and metalloprotease 1 11,468 16,521 13,710     1368224_at SPI3 NM_031531 Serine protease inhibitor 238 21,504 18,070     1387269_s_at Plaur AF007789 Plasminogen activator, urokinase receptor 2979 3714 11,388 Note: BMSC = bone marrow stem cell. Open in new tab This work was supported by award P50 AR48939 from the National Institutes of Health Specialized Center on Research for OA (http://poppy.obrl.uiowa.edu/Specialized Center of Research/SCOR.htm). References 1 Dirschl DR, Marsh JL, Buckwalter JA, et al. Articular fractures. J Am Acad Orthop Surg. 2004 ; 12 : 416 -423. 2 Marsh JL, Buckwalter J, Gelberman R, et al. Articular fractures: does an anatomic reduction really change the result? J Bone Joint Surg Am. 2002 ; 84-A : 1259 -1271. 3 Marsh JL, Weigel DP, Dirschl DR. Tibial plafond fractures. How do these ankles function over time? J Bone Joint Surg Am. 2003 ; 85-A : 287 -295. 4 Etter C, Ganz R. Long-term results of tibial plafond fractures treated with open reduction and internal fixation. Arch Orthop Trauma Surg. 1991 ; 110 : 277 -283. 5 Marsh JL. External fixation is the treatment of choice for fractures of the tibial plafond. J Orthop Trauma. 1999 ; 13 : 583 -585. 6 Matta JM. Fractures of the acetabulum: accuracy of reduction and clinical results in patients managed operatively within three weeks after the injury. J Bone Joint Surg Am. 1996 ; 78 : 1632 -1645. 7 Saterbak AM, Marsh JL, Nepola JV, Brandser EA, Turbett T. Clinical failure after posterior wall acetabular fractures: the influence of initial fracture patterns. J Orthop Trauma. 2000 ; 14 : 230 -237. 8 Honkonen S. Degenerative arthritis after tibial plateau fractures. J Orthop Trauma. 1995 ; 9 : 273 -277. 9 Stevens B. Age and joint fracture 1. J Bone Joint Surg Br. 2001 ; 99 : 99 -109. 10 Volpin G, Dowd G, Stein H. Degenerative arthritis after intra-articular fractures of the knee. Long-term results. J Bone Joint Surg Br. 1990 ; 72 : 634 -638. 11 Caplan AI. Mesenchymal stem cells: cell-based reconstructive therapy in orthopedics. Tissue Eng. 2005 ; 11 : 1198 -1211. 12 Caterson EJ, Nesti LJ, Albert T, Danielson K, Tuan R. Application of mesenchymal stem cells in the regeneration of musculoskeletal tissues. MedGenMed. 2001 ; 5 : E1 . 13 Van Zant G, Liang Y. The role of stem cells in aging. Exp Hematol. 2003 ; 31 : 659 -672. 14 Young HE, Duplaa C, Romero-Ramos M, et al. Adult reserve stem cells and their potential for tissue engineering. Cell Biochem Biophys. 2004 ; 40 : 1 -80. 15 Buckwalter JA, Brown TD. Joint injury, repair, and remodeling: roles in post-traumatic osteoarthritis. Clin Orthop Relat Res. 2004 ; 423 : 7 -16. 16 Barry FP. Mesenchymal stem cell therapy in joint disease. Novartis Found Symp. 2003 ; 249 : 86 -96. 17 Haynesworth SE, Goshima J, Goldberg VM, Caplan AI. Characterization of cells with osteogenic potential from human marrow. Bone. 1992 ; 13 : 81 -88. 18 Huibregtse BA, Johnstone B, Goldberg VM, Caplan AI. Effect of age and sampling site on the chondro-osteogenic potential of rabbit marrow-derived mesenchymal progenitor cells. J Orthop Res. 2000 ; 18 : 18 -24. 19 Im GI, Kim DY, Shin JH, Hyun CW, Cho WH. Repair of cartilage defect in the rabbit with cultured mesenchymal stem cells from bone marrow. J Bone Joint Surg Br. 2001 ; 83 : 289 -294. 20 Peterson B, Zhang J, Iglesias R. Healing of critically sized femoral defects, using genetically modified mesenchymal stem cells from human adipose tissue. Tissue Eng. 2005 ; 11 : 120 -129. 21 Tatebe M, Nakamura R, Kagami H, Okada K, Ueda M. Differentiation of transplanted mesenchymal stem cells in a large osteochondral defect in rabbit. Cytotherapy. 2005 ; 7 : 520 -530. 22 Yoo JU, Barthel TS, Nishimura K, et al. The chondrogenic potential of human bone-marrow-derived mesenchymal progenitor cells. J Bone Joint Surg Am. 1998 ; 80 : 1745 -1757. 23 Mackay AM, Beck SC, Murphy JM, Barry FP, Chichester CO, Pittenger MF. Chondrogenic differentiation of cultured human mesenchymal stem cells from marrow. Tissue Eng. 1998 ; 4 : 415 -428. 24 Pittenger MF, Mackay AM, Beck SC, et al. Multilineage potential of adult human mesenchymal stem cells. Science. 1999 ; 284 : 143 -147. 25 Pountos I, Giannoudis PV. Biology of mesenchymal stem cells. Injury. 2005 ; 36S : S8 -S12. 26 Schmitt B, Ringe J, Haupl T, et al. BMP2 initiates chondrogenic lineage development of adult human mesenchymal stem cells in high-density culture. Differentiation. 2003 ; 7 : 1567 -1577. 27 Sekiya I, Colter DC, Prockop DJ. BMP-6 enhances chondrogenesis in a subpopulation of human marrow stromal cells. Biochem Biophys Res Commun. 2001 ; 284 : 411 -418. 28 Sekiya I, Larson BL, Vuoristo JT, Reger RL, Prockop DJ. Comparison of effect of BMP-2, -4, and -6 on in vitro cartilage formation of human adult stem cells from bone marrow stroma. Cell Tissue Res. 2005 ; 320 : 269 -276. 29 Solchaga LA, Penick K, Porter JD, Goldberg VM, Caplan AI, Welter JF. FGF-2 enhances the mitotic and chondrogenic potentials of human adult bone marrow-derived mesenchymal stem cells. J Cell Physiol. 2005 ; 203 : 398 -409. 30 Digirolamo CM, Stokes D, Colter D, Phinney DG, Class R, Prockop DJ. Propagation and senescence of human marrow stromal cells in culture: a simple colony-forming assay identifies samples with the greatest potential to propagate and differentiate. Br J Haematol. 1999 ; 107 : 275 -281. 31 Mets T, Verdonk G. The theory of transition probability and the division pattern of WI-38 cells. Cell Biol Int Rep. 1978 ; 2 : 561 -564. 32 Stenderup K, Justesen J, Clausen C, Kassem M. Aging is associated with decreased maximal life span and accelerated senescence of bone marrow stromal cells. Bone. 2003 ; 33 : 919 -926. 33 Nishida S, Endo N, Yamagiwa H, Tanizawa T, Takahashi HE. Number of osteoprogenitor cells in human bone marrow markedly decreases after skeletal maturation. J Bone Miner Metab. 1999 ; 17 : 171 -177. 34 Owen M, Friedenstein AJ. Stromal stem cells: marrow-derived osteogenic precursors. Ciba Found Symp. 1988 ; 136 : 42 -60. 35 Pittenger MF, Mbalaviele G, Black M, et al. Mesenchymal stem cells. In: Koller MR, Palson BO, Masters JRW, eds. Human Cell Culture. Dordrecht, The Netherlands: Kluwer Academic Publishers; 2001;189:207–242. 36 Colter DC, Sekiya I, Prockop DJ. Identification of a subpopulation of rapidly self-renewing and multipotential adult stem cells in colonies of human marrow stromal cells. Proc Natl Acad Sci U S A. 2001 ; 98 : 7841 -7845. 37 Liu L, DiGirolamo CM, Navarro PA, Blasco MA, Keefe DL. Telomerase deficiency impairs differentiation of mesenchymal stem cells. Exp Cell Res. 2004 ; 294 : 1 -8. 38 Shi S, Gronthos S, Chen S. Bone formation by human postnatal bone marrow stromal stem cells is enhanced by telomerase expression. Nat Biotechnol. 2002 ; 20 : 587 -591. 39 Zimmermann S, Voss M, Kaiser S, Kapp U, Waller CF, Martens UM. Lack of telomerase activity in human mesenchymal stem cells. Leukemia. 2003 ; 17 : 1146 -1149. 40 Foster LJ, Zeemann PA, Li C, Mann M, Jensen ON, Kassem M. Differential expression profiling of membrane proteins by quantitative proteomics in a human mesenchymal stem cell line undergoing osteoblast differentiation. Stem Cells Dev. 2005 ; 23 : 1367 -1377. 41 Tondreau T, Lagneaux L, Dejeneffe M, et al. Isolation of BM mesenchymal stem cells by plastic adhesion or negative selection: phenotype, proliferation kinetics and differentiation potential. Cytotherapy. 2004 ; 6 : 372 -379. 42 Fehrer C, Lepperdinger G. Mesenchymal stem cell aging. Exp Gerontol. 2005 ; 40 : 926 -930. 43 Bergman RJ, Gazit D, Kahn AJ, Gruber H, McDougall S, Hahn TJ. Age-related changes in osteogenic stem cells in mice. J Bone Miner Res. 1996 ; 11 : 568 -577. 44 D'Ippolito G, Schiller PC, Ricordi C, Roos BA, Howard GA. Age-related osteogenic potential of mesenchymal stromal stem cells from human vertebral bone marrow. J Bone Miner Res. 1999 ; 14 : 1115 -1122. 45 Dressler MR, Butler DL, Boivin GP. Effects of age on the repair ability of mesenchymal stem cells in rabbit tendon. J Orthop Res. 2005 ; 23 : 287 -293. 46 Roskoski R, Jr. Signaling by Kit protein-tyrosine kinase-the stem cell factor receptor. Biochem Biophys Res Commun. 2005 ; 337 : 1 -13. 47 Spangrude GJ, Heimfeld S, Weissman IL. Purification and characterization of mouse hematopoietic stem cells. Science. 1988 ; 241 : 58 -62. 48 Zaucke F, Dinser R, Maurer P, Paulsson M. Cartilage oligomeric matrix protein (COMP) and collagen IX are sensitive markers for the differentiation state of articular primary chondrocytes. Biochem J. 2001 ; 358 : 17 -24. 49 Hefferan TE, Subramaniam M, Khosla S, Riggs BL, Spelsberg TC. Cytokine-specific induction of the TGF-beta inducible early gene (TIEG): regulation by specific members of the TGF-beta family. J Cell Biochem. 2000 ; 78 : 380 -390. 50 Kume S, Kato S, Yamagishi S, et al. Advanced glycation end-products attenuate human mesenchymal stem cells and prevent cognate differentiation into adipose tissue, cartilage, and bone. J Bone Miner Res. 2005 ; 20 : 1647 -1658. 51 Bianchi G, Muraglia A, Daga A, Corte G, Cancedda R, Quarto R. Microenvironment and stem properties of bone marrow-derived mesenchymal cells. Wound Repair Regen. 2001 ; 9 : 460 -466. Copyright 2007 by The Gerontological Society of America

Zheng, Hongjun; Martin, James A.; Duwayri, Yazan; Falcon, Gilbert; Buckwalter, Joseph A.

doi: N/Apmid: N/A

Damaged articular cartilage rarely heals or regenerates in middle-aged and elderly adults, suggesting that the chondrogenic potential of mesenchymal stem cells declines with age. To test this hypothesis, we measured the responses of rat bone marrow-derived mesenchymal stem cells (BMSCs) to chondrogenic induction in vitro. BMSCs from immature rats (1 week old), young adult rats (12 weeks old), and old adult rats (1 year old) were analyzed for cartilage extracellular matrix (ECM) production. Histologic analysis showed strong cartilage ECM formation by BMSCs from 1-week-old rats, but not by BMSCs from 12-week-old or 1-year-old rats. Real-time polymerase chain reaction revealed age-related declines in messenger RNA encoding type II collagen, aggrecan, and link protein, three major cartilage ECM components. Microarray analysis indicated significant age-related differences in the expression of genes that influence cartilage ECM formation. These findings support the hypothesis that the chondrogenic potential of mesenchymal stem cells declines with age.

Hongjun Zheng, James A. Martin, Yazan Duwayri, Gilbert Falcon, Joseph A. Buckwalter

doi: biomedgerontology;62/2/136pmid: N/A

Damaged articular cartilage rarely heals or regenerates in middle-aged and elderly adults, suggesting that the chondrogenic potential of mesenchymal stem cells declines with age. To test this hypothesis, we measured the responses of rat bone marrow-derived mesenchymal stem cells (BMSCs) to chondrogenic induction in vitro. BMSCs from immature rats (1 week old), young adult rats (12 weeks old), and old adult rats (1 year old) were analyzed for cartilage extracellular matrix (ECM) production. Histologic analysis showed strong cartilage ECM formation by BMSCs from 1-week-old rats, but not by BMSCs from 12-week-old or 1-year-old rats. Real-time polymerase chain reaction revealed age-related declines in messenger RNA encoding type II collagen, aggrecan, and link protein, three major cartilage ECM components. Microarray analysis indicated significant age-related differences in the expression of genes that influence cartilage ECM formation. These findings support the hypothesis that the chondrogenic potential of mesenchymal stem cells declines with age. Copyright 2007 by The Gerontological Society of America « Previous | Next Article » Table of Contents This Article J Gerontol A Biol Sci Med Sci (2007) 62 (2): 136-148. » Abstract Free Full Text (HTML) Free Full Text (PDF) Free Classifications Journal of Gerontology: Biological Sciences Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Similar articles in PubMed Add to my archive Download citation Request Permissions Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Zheng, H. Articles by Buckwalter, J. A. Search for related content PubMed PubMed citation Articles by Zheng, H. Articles by Martin, J. A. Articles by Duwayri, Y. Articles by Falcon, G. Articles by Buckwalter, J. A. Related Content Load related web page information Share Email this article CiteULike Delicious Facebook Google+ Mendeley Twitter What's this? Search this journal: Advanced » Current Issue November 2015 70 (11) Alert me to new issues The Journal About the journal Translational Articles Free Editors’ Choice Articles Impact Factor Articles The Journals of Gerontology, Series A Supplements Special Issues Rights & permissions We are mobile – find out more Journal Career Network Published on behalf of The Gerontological Society of America Impact Factor: 5.416 5-Yr impact factor: 5.406 Editorial Boards The Journals of Gerontology, Series A: Biological Sciences Rafael de Cabo, PhD, Editor View full editorial board The Journals of Gerontology, Series A: Medical Sciences Stephen B. Kritchevsky, PhD View full editorial board For the Media GSA Press Room For Authors Instructions to authors Services for authors Submit Now: Biological Sciences Submit Now: Medical Sciences Self-Archiving Policy Online Submission Open access options for authors - visit Oxford Open WhsSvhnOkaAwYG81FJCYgwG7z1LnIP2F true Looking for your next opportunity? Looking for jobs... jQuery_1_11 = jQuery.noConflict(true); Corporate Services What we offer Advertising sales Reprints Supplements Classified Advertising Sales Alerting Services Email table of contents CiteTrack XML RSS feed

Magalhães, João Pedro de; Costa, Joana; Church, George M.

doi: N/Apmid: N/A

Comparative studies of aging are often difficult to interpret because of the different factors that tend to correlate with longevity. We used the AnAge database to study these factors, particularly metabolism and developmental schedules, previously associated with longevity in vertebrate species. Our results show that, after correcting for body mass and phylogeny, basal metabolic rate does not correlate with longevity in eutherians or birds, although it negatively correlates with marsupial longevity and time to maturity. We confirm the idea that age at maturity is typically proportional to adult life span, and show that mammals that live longer for their body size, such as bats and primates, also tend to have a longer developmental time for their body size. Lastly, postnatal growth rates were negatively correlated with adult life span in mammals but not in birds. Our work provides a detailed view of factors related to species longevity with implications for how comparative studies of aging are interpreted.