Journal of Applied Physiology Millar Instruments
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

J Appl Physiol 97: 1915-1922, 2004. First published July 2, 2004; doi:10.1152/japplphysiol.00505.2004
8750-7587/04 $5.00
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow All Versions of this Article:
97/5/1915    most recent
00505.2004v1
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (8)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Van Voorhies, W. A.
Right arrow Articles by Curtsinger, J. W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Van Voorhies, W. A.
Right arrow Articles by Curtsinger, J. W.

Testing the "rate of living" model: further evidence that longevity and metabolic rate are not inversely correlated in Drosophila melanogaster

Wayne A. Van Voorhies,1 Aziz A. Khazaeli,2 and James W. Curtsinger2

1Molecular Biology Program, New Mexico State University, Las Cruces, New Mexico 88003-8001; and 2Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, Minnesota 55108

Submitted 11 May 2004 ; accepted in final form 24 June 2004


   ABSTRACT
TOP
 ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 GRANTS
 REFERENCES
 
In a recent study examining the relationship between longevity and metabolism in a large number of recombinant inbred Drosophila melanogaster lines, we found no indication of the inverse relationship between longevity and metabolic rate that one would expect under the classical "rate of living" model. A potential limitation in generalizing from that study is that it was conducted on experimental material derived from a single set of parental strains originally developed over 20 years ago. To determine whether the observations made with those lines are characteristic of the species, we studied metabolic rates and longevities in a second, independently derived set of recombinant inbred lines. We found no correlation in these lines between metabolic rate and longevity, indicating that the ability to both maintain a normal metabolic rate and have extended longevity may apply to D. melanogaster in general. To determine how closely our measurements reflect metabolic rates of flies maintained under conditions of life span assays, we used long-term, flow-through metabolic rate measurements and closed system respirometry to examine the effects of variables such as time of day, feeding state, fly density, mobility of the flies, and nitrogen knockout on D. melanogaster metabolic rate. We found that CO2 production estimated in individual flies accurately reflects metabolic rates of flies under the conditions used for longevity assays.

aging; respiratory quotient

AGING IS A NEAR-UNIVERSAL PROCESS that results in a progressive decline in the physiological function of an organism over time. Currently, one of the most widely proposed mechanistic explanations for aging links the production of free radicals and other oxidants produced during aerobic respiration to biomolecular damage that results in senescence (5, 7, 33). The origins of this explanation for aging extend back nearly 100 years to studies showing that the longevity of an organism appeared to be inversely correlated to its mass-specific metabolic rate (30). This relationship, commonly referred to as the "rate of living theory," states that the metabolic rate of an organism is directly and causally linked to its longevity (26). The simplest interpretation of the rate of living theory predicts that factors that decrease an organism's metabolic rate will increase longevity, whereas factors that increase metabolic rate will decrease longevity (see Refs. 7, 31, 32, 39 for a more detailed discussion of the rate of living hypothesis and the importance of metabolic rate to aging). Contrary to predictions of the rate of living hypothesis, several recent studies have shown that metabolic rate is not a critical factor limiting longevity (19, 21, 27, 35, 36, 39).

We recently reported on a study examining the relationship between longevity and metabolic rate in a large number of recombinant inbred (RI) Drosophila melanogaster lines (39). That study found no indication of an inverse relationship between longevity and metabolic rate. A potential limitation of that study, however, is that it was conducted on RI lines derived from a single set of parental strains originally developed over 20 years ago (17, 18). The extended period of laboratory culture and narrow genetic base may have produced lines with an idiosyncratic relationship between longevity and metabolic rates.

To determine whether the lack of correlation between longevity and metabolic rate observed in those lines is a general characteristic of Drosophila, we measured metabolic rates and longevities in another independently derived set of RI lines that have only been in the laboratory for a few years. The advantages of inbred experimental material have been discussed before (39) and arise primarily from the ability to obtain replicated measurements on the same genotypes, thereby increasing statistical power. Here, we report that the second set of genotypes also exhibits a lack of correlation between metabolic rate and longevity, consistent with our previous observations. The conclusion that it is possible to both maintain a normal metabolic rate and have extended longevity may apply to D. melanogaster in general.

A potential concern in studies examining the relationship between longevity and metabolic rate is that there may be significant differences between the conditions under which metabolism and longevity are measured. In such a case, the metabolic rate measurements may not reflect the true metabolic rates of animals in the life span assays, and the relationship between these two variables would thus be distorted. Although in our previous study (39) we measured metabolic rates using conditions that were as similar as possible to those used for determining longevity, some experimental variables differed between the two assays. For example, the metabolic rate measurements were conducted on individual, unfed flies, which were in conditions that differed from those under which longevity was assayed. Metabolic rate measurements obtained in small, empty glass vials may not be representative of metabolic rates of flies maintained in population cages or vials with food and other flies. We used both long-term, flow-through, metabolic rate measurements and closed-system respirometry to study the effects of several experimental variables that might confound our metabolic measurements: time of day, feeding state, fly density, presence/absence of food medium, fly mobility, and immobilizing flies with nitrogen. We found that CO2 production as measured on individual flies accurately reflects the metabolic rates of flies maintained under conditions used for survival measurements.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 GRANTS
 REFERENCES
 
Fly stocks.   We measured metabolic rates and longevities of virgin male flies from 28 RI lines originally generated in the laboratory of Dr. S. V. Nuzhdin (University of California, Davis). As described by Kopp et al. (14), wild flies were collected in Winters, CA, and a single F1 male was mated with a single F1 female from the same population. F2 progeny were sib-mated for 25 generations to produce genetically homogeneous lines. Genetic homogeneity was verified by scoring multiple genetic markers in each line with replication (14). In 1998 ~150 of these RI lines were sent to the Curtsinger laboratory and maintained in small numbers in 8-dram vials. For this experiment, we used 28 RI lines; 14 were randomly chosen from the longest-lived quartile, and 14 were randomly chosen from the 40th to 60th percentiles. Fly stocks were sent from the University of Minnesota to New Mexico State University, and next-generation progeny were used for metabolic measurements. Individuals used in the study were collected from eggs laid by approximately five female flies from each line. Females were allowed to lay eggs for 48 h, after which the adults were removed. On emergence, virgin male offspring were collected under light CO2 anesthesia and placed in new food vials at a density of 15–20 flies per vial. Metabolic rates were measured on individual flies at three different ages. Each fly was used for a single age-specific metabolic measurement and then frozen for later weighing.

A standard D. melanogaster laboratory wild-type stock, strain w1118, was used to examine the effects of fly density, temporal variation, feeding state, measurement conditions, fly mobility, and nitrogen-induced immobolization on metabolic rate measurements. All stocks were maintained on a standard cornmeal Drosophila food source, which was shipped overnight twice weekly from the University of Minnesota. The w1118 lines were maintained on an identical food source with the exception that it was stored frozen at –80°C until used. No differences were apparent between flies reared on thawed food or food that had never been frozen. Flies were maintained in plastic vials covered with cotton plugs and were transferred to new food vials approximately every 2 days. The flies were housed at 24°C under continuous light (24-h photoperiod), with 60–70% relative humidity conditions. Flies were kept on a continuous photoperiod to minimize external light cues, which could potentially affect circadian cycles. Incubator temperature, light intensity, and humidity were monitored with Hobo data loggers (Bourne, MA).

Metabolic measurements.   Resting metabolic rates were measured on individual flies from each RI line at three ages (5, 7, and 11 days postemergence). The methods used to assay metabolic rates were essentially identical to those used in our previous study (39). The w1118 flies used in flow-through respirometry measurements, the fed/unfed metabolic comparisons, and the confined/unconfined group measurements were 5–11 days postemergence.

For closed-system metabolic measurements, flies were immobilized in a gas stream of humidified nitrogen gas. Individual flies were then placed in 2.2-ml glass measurement chambers either by gently transferring flies via a glass pipet with mouth suction or by using a fine-tipped paint brush to move the fly into the chamber. The chamber was then sealed with a rubber stopper. This process took <1 min; flies quickly recovered and began moving within 1 min of immobilization. The chambers were then flushed for 15 s at a flow of 90 ml/min with CO2-free, H2O-saturated (100% relative humidity) room air introduced via a 22-gauge needle, with a second needle inserted to vent the chamber, and left sealed for ~1 h. At the end of the sampling period, a 1-ml (STPD) gas sample was removed from the chamber with a Hamilton SampleLock syringe (Hamilton, Reno, NV) and directly injected into a 150 ml/min (±1%) STPD, CO2-free carrier air stream. Flow of the carrier air stream was controlled with a mass flowmeter (Sierra Instruments, Monterrey, CA) and scrubbed of H2O vapor with a magnesium perchlorate filter before entering into a Li-Cor 6251 CO2 gas analyzer (Li-Cor, Lincoln, NE). The Li-Cor analyzer has a sensitivity of ≤0.1 part per million (ppm) and an accuracy of ≤1 ppm. It took ~10 s to inject the 1-ml sample through the septa into the carrier. The chamber was then reflushed with CO2-free air, and a second air sample was taken 1 h later. The amount of CO2 produced by each fly was calculated using Datacan software (Sable Systems International, Henderson, NV).

Second metabolic measurements were typically less variable than first readings, as our laboratory previously reported (39). For this reason, only the second metabolic measurements are used in correlational analysis between metabolic rate and longevity. A total of eight flies from each line was typically measured for each sampling period. Each set of experiments also included several empty chambers to control for background CO2 production and the presence of CO2 in the air used to flush the chambers, and to determine whether there was significant gas leakage in the metabolic chambers. Analysis of blank chambers indicated that background CO2 levels or chamber leakage was of minor consequence. Over a 1-h period, a single fly typically produced between 2 and 3 µl of CO2. The reading from an empty chamber over this time was <5% of this amount and was very consistent both within and between sets of measurements. The CO2 gas analysis system was zeroed daily against CO2-free air and calibrated regularly against a 51-ppm certified gas standard (Air Products, Long Beach, CA).

To assay temporal variation in metabolic measurements and the effect of high concentrations of nitrogen on metabolic rate, high-resolution metabolic rate data were obtained using flow through respirometry on groups of ~25 unmated, 5–7 day postemergent w1118 flies. These flies were placed in 25-ml glass metabolic chambers, which were flushed with CO2-free, H2O-saturated air at 20 ml/min STPD. Airflow into the chambers was controlled with mass flowmeters (Sierra Instruments, Monterey, CA). CO2 and H2O vapor were removed from the inflowing air using a combination of a purge-gas generator (MTI, Westminster, CO) and Drierite/ascarite scrubber columns. After the airstream was passed through the mass flowmeter, the airstream was rehydrated by passing it through a series of glass syringes filled with sterile H2O and cotton wool. The H2O vapor content of the airstream entering the metabolic chamber was at essentially 100% relative humidity and contained <1 ppm of CO2 as measured with a Li-Cor 6262 CO2/H2O analyzer. After flowing through the metabolic chamber, the airstream was dried with magnesium perchlorate filters before entering the CO2 analyzers. The metabolic rates of two groups of flies were measured simultaneously with a Li-Cor 6251 and 6262 analyzer operated independently of each other. Metabolic data were collected at 15-s resolution after the chamber was allowed to equilibrate with inflowing air (~15 min after flies were placed in the chambers). To ensure the flies remained well fed during the metabolic measurements, ~100 mg of standard Drosophila food medium was placed in chambers with the flies. The CO2 production by the medium was measured both before and after it was placed in the metabolic chambers. Fly mortality was checked at the end of the metabolic measurements.

Effect of anesthetic on metabolic rate.   The metabolic rates of flies that had been exposed for 2 min to a 100% nitrogen environment was measured to determine whether the use of 100% nitrogen to immobilize flies before they were placed in the metabolic chamber had a significant long-term effect on metabolic function. Approximately 25, 5-day postemergent w1118 unmated male flies were placed in separate 25-ml metabolic chambers with food, without being immobilized with nitrogen. The chambers were then connected to separate CO2 analyzers, and a 100-min baseline metabolic rate was recorded for both groups. One group was then exposed to humidified, 100% nitrogen at a flow of 100 ml/min for 2 min. The flies were completely immobilized at the end of this exposure. Experiments with other groups of flies indicate that the CO2 production of flies in 100% nitrogen is near zero. After 2 min, the chamber was flushed with normoxic air, and the metabolic rate of the flies was recorded for several more hours. Fly survivorship was assayed at the end of the experiment.

Relationship between CO2 production and oxygen consumption.   To determine the relationship between CO2 production and oxygen consumption, both variables were measured in 23 RI lines of D. melanogaster. These RI lines had been used in our earlier study (39). Groups of several dozen flies were sealed in 50-ml glass metabolic chambers for ~1 h. At the end of this time, the air in the chamber was flushed into a paramagnetic oxygen analyzer (Sable Systems PA-1) linked in series to a Li-Cor CO2 analyzer, and both oxygen consumption and CO2 production were determined.

Effect of feeding on metabolic rates.   Closed-system respirometry was also used to determine the effect of feeding status on CO2 production and oxygen consumption. Twelve groups of 20, 5–7 day postemergent w1118 male flies were sealed in 50-ml glass metabolic chambers with either a small amount of standard Drosophila food medium or H2O. The air in the chambers was sampled using a multiplexer (Sable Systems) to flush the chambers at 1-h intervals into a CO2 analyzer (Li-Cor) linked in series to a oxygen analyzer (Sable Systems Oxzilla). The oxygen concentration of air used to flush the system was controlled by filling and sampling the chambers with air of a defined oxygen concentration contained in a 40-liter cylinder.

Confined flies.   Eighty 11-day-old postemergent w1118 male or female flies were individually placed into 2.2-ml glass metabolic vials. The movement of half of the flies (randomly chosen) was then restrained by loosely filling the vials with rayon wool, which prevented all gross movement. Metabolic rates were measured twice over a 2-h period. Individuals came from a common group of flies maintained as a mixed-sex population for their first 8 days after emergence. The flies were separated into different sexes 3 days before the metabolic measurements.

Effect of group size on metabolic rate.   Metabolic rates of individual flies were compared with metabolic rates of flies in groups of 20 to estimate effects of interactions between flies on metabolic rate. At the start of the experiment, multiple vials of male flies were combined into two vials. The flies were immobilized with humidified nitrogen and quickly placed into either 50-ml metabolic chambers at 20 flies/chamber or 2.2-ml chambers at 1 fly/chamber. CO2 production of the grouped and individual flies was sampled twice at 1-h intervals.

Longevity measurements.   Life spans of flies in vials and population cages were measured in the Minnesota laboratory following previously described methods (39). Briefly, stocks were expanded in half-pint bottles, and adult male flies emerging within a 6-h period were collected and transferred into specially designed 4.6-liter population cages. Each RI line was placed in a separate cage, and mortality was recorded daily until the last death. Approximate densities were determined by weight; exact numbers of flies in each population were calculated at the conclusion of the survival experiments by summing all recorded deaths. Life spans were also measured on unmated male flies maintained on standard Drosophila food medium in 8-dram shell vials at a density of 15 flies/vial. Dead individuals were counted and removed from the vials daily until the last death. Flies were transferred to new vials every other day and maintained at 24°C with constant illumination and 60–70% relative humidity in the same incubator that housed the population cages used in the longevity assay. Longevity assays of all lines were done simultaneously to minimize environmental variations.

Fly weights.   After recording the second CO2 measurement, the fly was left in the sealed metabolic chamber and stored at –80°C until weighing. Flies were thawed and weighed on a Sartorius M2P microbalance (Sartorius, Goettingen, Germany). A total of 628 individual flies from the 29 different lines was weighed. In addition to individual body weights, line means were calculated by pooling the three ages. To determine whether freezing temperature was an important factor affecting fly weight, a subset of flies was weighed and refrozen either in a frosting or frost-free –20°C freezer, or at –80°C. Individuals from this group were then periodically thawed and reweighed.

Data analysis.   Multiple ANOVA was conducted with SyStat (version 5.2.1) statistical analysis software. Data were graphed and least square regression lines fitted with Cricket Graph III. Excel's data analysis tools were also used to calculate correlation coefficients. Statistical significance of correlation coefficients was computed from statistical tables (28).


   RESULTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 ACKNOWLEDGMENTS
 GRANTS
 REFERENCES
 
Metabolic rate measurements.   There was almost a twofold range of metabolic rates observed in the RI lines for each age studied (Fig. 1). Significant differences in interline metabolic rates existed at all ages (multiple ANOVA with weight included as a covariate, day 5: F = 6.7, n = 239; day 7: F = 11.6, n = 142; day 11: F = 6.3, n = 181; P << 0.001 for all analyses). The specific factors responsible for this variation are not known, but the variation in metabolic rates is not explained by differences in body mass. As in our previous study (38), we found no robust correlation between metabolic rates of individual flies and their body masses. Although there was a significant correlation between the second metabolic rate estimate for the 5-day metabolic data, no significant correlation was observed for the 7- or 11-day-old metabolic data. The Pearson product-moment correlation coefficients for the second metabolic rate and body mass were r = 0.42, P << 0.001, n = 266 (for 5-day-old flies); r = 0.03, P >> 0.10, n = 164 (for 7-day-old flies); and r = 0.02, P >> 0.10, n = 209 (for 11-day-old flies). One potential limitation of this study is that metabolic rates were only measured on relatively young flies. Numerous studies, however, have shown that after ~1 wk of age, metabolic rate remains relatively constant in Drosophila (2, 11, 21, 27, 39). This indicates that the metabolic rate measurements done on younger flies should be representative of that in older flies.



View larger version (16K):
[in this window]
[in a new window]
  		Fig. 1. Correlations between metabolic rate and longevity in 28 Drosophilia melanogaster lines measured at 3 ages. The average metabolic rate of each recombinant inbred (RI) line is plotted against the average longevity of the line as measured in vials ({circ}) or cages ({square}). Linear fits were computed by least squares analysis. Dashed line represents vial longevity vs. metabolic rate, and the solid line represents the cage longevity vs. metabolic rate. No significant correlation exists between vial longevity or cage longevity and metabolic rate for any of the time points (P >> 0.10). The Pearson product moment correlation coefficients for the vial or cage longevity and metabolic rate were r = 0.25 and 0.19 (5-day-old flies; n = 28), r = 0.00 and 0.10 (7-day-old flies; n = 21), and r = 0.15 and 0.00 (11-day-old flies; n = 27).

 
Pooling metabolic data for all ages, we found a small but statistically significant correlation between metabolic rate and body mass. Body mass accounted for only 3% of the variation in metabolic rates, and the exponent of the log-log regression of metabolic rate vs. body mass is 0.33. Because of the lack of a consistent correlation between metabolic rate and body mass, it is inappropriate to express metabolic rate in mass specific units, and therefore metabolic rates are expressed per whole animal (10, 25). To account for the potential effects of body mass on metabolic rate, body mass is included as a covariate in multiple ANOVA (25).

There were no significant differences in the average metabolic rates of the lines between measurements of 5-, 7-, and 11-day-old postemergent flies [ANOVA, F = 1.65, P >> 0.10, degrees of freedom (df) = 57]. For individual flies, there was a high correlation between first and second metabolic rate measurements (Fig. 2). This indicates that a single metabolic measurement of an individual fly provides a reasonably consistent measure of the metabolic rate of that particular individual.



View larger version (19K):
[in this window]
[in a new window]
  		Fig. 2. Correlation between first and second metabolic measurements on individuals from inbred D. melanogaster lines measured at different ages. The sample period was ~1 h for both the first and second measurements. Linear fits were computed by least squares analysis. Highly significant correlations exist between the two metabolic measurements for all of the time points. The Pearson product-moment correlation coefficients were r = 0.62 (5-day-old flies; P << 0.001, n = 266), r = 0.87 (7-day-old flies; P << 0.001, n = 162), and r = 0.81 (11-day-old flies; P << 0.001, n = 208).

 
Longevity data.   There was a large range of interline longevities. Line means varied two- to threefold depending on whether vial or cage longevity was used. When kept in vials, the postemergent mean longevities ranged from 27 to 77 days; the range in cages was 27–68 days. There were no major interline differences in developmental rates, and all lines completed development from egg to adulthood within 2 days of each other. Longevity assays were done for populations in both vial and cage conditions to determine how robust the mean longevity of a line was under different environmental conditions. Culture conditions did not have a significant effect on mean longevities: ~52 days for both the vial and cage assays (paired t-test, t = 0.12, P >> 0.10, n = 27). Within lines, vial and cage longevity measurements are highly correlated (r = 0.68, P = 0.001). The conditions of the flies in vials were very similar to the conditions under which metabolic rates were measured, as the flies could freely walk, but not fly, in the vials.

Correlation between metabolic rate and longevity.   At none of the ages studied was there a statistically significant correlation between the average metabolic rates of the RI lines and their mean longevities. The correlation between average metabolic rate and mean longevities was calculated for each of the three sampling points (5-, 7-, and 11-day-old postemergent flies) against both cage and vial longevities. The Pearson product-moment correlation coefficients for these six correlational analyses ranged from 0.00 to 0.25 (P values ranged from 0.95 to 0.20). There is a notable lack of negative correlations.

Fly weight.   The average male body mass of the 27 lines was 0.74 mg and ranged from 0.83 to 0.63 mg. The correlation between the cage or vial longevity of the lines and average mass of the lines was not significant, indicating that differences in body mass do not explain differences in line longevities (r2 = 0.05 for average cage longevity vs. average weight, F = 1.4, df = 25, 26; and r2 = 0.01 for average vial longevity vs. average weight, F = 0.1, df = 25, 26, P >> 0.10 for both correlations).

The temperature at which flies were stored before weighing had a substantial effect on estimated weight. The mass of flies frozen at –80°C was found to be stable (the second weight of the fly was within 2% of the original weight) for a minimum of 3 yr. However, flies sealed in identical vials but placed at –20°C lost >50% of their initial body mass within 2 wk of storage. The weight of flies stored for 1 yr at –20° was 37 ± 1% (n = 12) of the original wet weight. The dry weight of the same flies dried at 65°C for 48 h was 32 ± 1% (n = 11) of the original weight. The weight loss of flies stored at –20°C was approximately the same in defrosting and non-defrosting freezers.

Effect of 100% nitrogen exposure on metabolic rate.   The use of 100% nitrogen to immobilize flies before placing them in metabolic chambers has only a minor effect on the long-term metabolic rate measurements (Fig. 3A). For this experiment, the flies were exposed to 2 min of 100% nitrogen, several times longer than the exposure used to knock out the flies for the metabolic rate measurements on individual flies.



View larger version (27K):
[in this window]
[in a new window]
  		Fig. 3. High time resolution, flow-through respirometry measurements of unmated, 5-day-old w1118 male flies. A: effect of nitrogen anesthesia on metabolic rate. Lines represent data from groups of flies exposed to 100% N2 for 2 min and a control group in normoxic conditions; n = 5 for both groups. B: metabolic rates in groups of flies. The first reading started within 15 min of the flies being placed in the chamber; n = 6. C: long-term metabolic rates in 5 groups of flies. All plots show means ± SE of 5–6 independent groups of flies. For clarity, the metabolic rate data were plotted from 15-min averages. D: comparison of CO2 production and oxygen consumption in 27 RI lines of D. melanogaster. The equation of the least squares fitted line is y = 0.91x + 2.8; r2 = 0.99.

 
High-resolution metabolic rate measurements.   Metabolic rate measurements executed at a high temporal resolution show that the metabolic rates remain relatively constant over both the short-term (Fig. 3B) and a 24-h period (Fig. 3C). There was no evidence of a large circadian effect on metabolic rate. All flies were alive at the end of a 4-h sample period, with <4% of the flies dying after 25 h in the metabolic chamber. At the end of 24 h, the food placed with the flies contributed <3% of the measured CO2.

Relationship between CO2 production and oxygen consumption.   As seen in Fig. 3D, CO2 production was almost perfectly correlated with oxygen consumption. A respiratory quotient (RQ) value of 0.95 was used to convert CO2 measurements of the RI lines to SI energy units with an energy equivalent of 20.1 kJ/liter oxygen. The RQ of the w1118 line used in the metabolic control experiments was directly determined to be ~1.00 for fed flies and 0.95 for flies that had not been fed for 3 h.

Effect of food deprivation on metabolic rate.   The metabolic rates of fed and unfed flies was very similar over a 4-h sampling period, especially when oxygen consumption was used as the measure of metabolic rate (Fig. 4). There was a noticeable shift in the RQ of the unfed flies over the sample interval. Both the fed and unfed groups started with a RQ near 1.00, consistent with the use of carbohydrates as the primary metabolic substrate. There was a significant decrease in the RQ of the unfed group over time, indicating a shift to alternative substrates, such as lipids, to supply metabolic energy. All flies were alive at the end of the sample period, and CO2 production from the food medium was a negligible part (<5%) of measured CO2.



View larger version (38K):
[in this window]
[in a new window]
  		Fig. 4. Comparison of metabolic rates in fed and unfed groups of unmated w1118 male D. melanogaster. Metabolic rates were measured as both CO2 production (A) and oxygen consumption (B) on groups of 20 flies sealed in metabolic chambers. Flies were either placed with a small amount of standard Drosophila medium (dark bars) or H2O (light bars). Means and SE are plotted for 6 groups of fed flies and 5 groups of unfed flies. Data were normalized to the 2nd metabolic reading.

 
Effective of confinement on metabolic rate.   The mean metabolic rates of both male and female flies confined by rayon balls were up to 50% higher than unconfined flies (Fig. 5). Second metabolic measurements of confined flies were also typically higher than first readings on the same individual, reversing the pattern observed in unconfined flies (female flies, 1st measurement, Student’s t = 5.9, df = 38, P << 0.001; 2nd measurement, t = 9.8, df = 38, P << 0.001; male flies, 1st measurement, t = 6.2, df = 38, P << 0.001; 2nd measurement, t = 5.9, df = 38, P << 0.001). It is not clear what is responsible for the increased metabolic rate of confined flies. Physically constraining gross fly movement could cause an increase in metabolic rate through the fly struggling to move or by other factors such as general increase in metabolic stress. The average body mass of unconfined male flies was slightly but significantly greater than that of confined male flies (0.62 ± 0.002 vs. 0.56 ± 0.002 mg, t = 3.8, df = 38, P << 0.001), whereas the mean body mass of unconfined and confined female flies was very similar (1.07 ± 0.002 vs. 1.04 ± 0.002 mg, t = 0.7, df = 38, P >> 0.10). This indicates that differences in metabolic reading were not caused by large differences in body size between the groups, particularly since the mass of the unconfined flies was slightly greater than the confined flies. The differences in metabolic rates were not due to rayon wool releasing CO2 into the chamber, because the rayon wool did not produce detectable amounts of CO2. There were no deaths among flies in any of the groups during this experiment.



View larger version (20K):
[in this window]
[in a new window]
  		Fig. 5. Metabolic rates of confined and unconfined flies. Metabolic rates were compared between male and female flies in 2-ml metabolic vials and then either left unconfined (black bars) or constrained with rayon wool (gray bars). MR, metabolic rate.

 
Effect of group size on metabolic rate.   As shown in Table 1, per fly metabolic rates of flies in groups were not significantly different from those of flies measured individually. All flies in all groups were alive at the end of the sampling period.


View this table:
[in this window]
[in a new window]
  		Table 1. Metabolic rates of male w1118 flies measured either on individual flies or as groups of 20 flies

 

   DISCUSSION
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
ACKNOWLEDGMENTS
 GRANTS
 REFERENCES
 
The RI lines assayed here exhibited a wide range of average metabolic rates and longevities, but there were no significant correlations between the two traits, a result consistent with our previous study and other Drosophila studies (2, 21, 39). Thus the increased longevity of the long-lived RI lines does not appear to be achieved through a reduction in metabolic activity. One limitation of our study is that, for practical reasons, we did not measure life spans and metabolic rates on the same individual flies. Our observation that line means show zero or slightly positive correlation between the variables of interest does not rule out the possibility that those same variables are inversely related within lines. Our data cannot directly address this issue, but Hulbert et al. (11) have recently measured both life spans and metabolic rates in 65 individual D. melanogaster and reported a slightly positive (but nonsignificant) correlation, consistent with our observations on line means. It has long been proposed that lifetime energy expenditure is relatively constant among a class of animals (26, 30), a proposition that predicts an inverse relationship between life span and metabolic rate. More recent research has shown the limitations of this generalization in animals ranging from insects to mammals (4, 27, 34–36), although factors affecting the metabolic rate of an ectotherm, such as temperature, can be an important factor affecting longevity (20, 40, 43).

Of obvious interest are the mechanisms that allow long-lived RI lines to maintain normal metabolic rates. In the context of the oxidative stress theory of aging, the extended longevity of the long-lived lines may be due to alterations in processes involved in repair and detoxification of oxidative damage or to metabolic adaptations that reduce the rate of production of reactive oxygen species. Arking and colleagues (1, 3) have proposed that the extended longevity in some long-lived lines may be due to a process in which there is upregulation of antioxidant defenses followed by alterations in metabolic function that reduce reactive oxygen species production. The importance of antioxidant levels to longevity is controversial. In general, it appears that, although pharmacological or genetic methods of increasing antioxidant levels can increase longevity in short-lived lines, the effects of such treatments vary between laboratories (6, 12) and do not always increase longevity in longer lived lines (21, 23, 24, 37).

This study demonstrates that there is a considerable range of variation in individual metabolic rates. This variation does not appear to be due to random fluctuations over time in individual flies. Instead, an individual fly has a characteristic and repeatable metabolic rate that can differ from that of other individuals of the same genotype. The specific factors responsible for this variation are not clear, but we do know that the variation is not explained by differences in body mass. It is also unclear what specific genetic factors are responsible for the interline differences in metabolic rates. A recent study by Montooth et al. (22) employing D. melanogaster RI lines found a nearly threefold range of mean metabolic rates between genotypes. There were, however, generally only weak correlations between metabolic enzyme activity levels and whole organism metabolic rates. This observation reinforces results from other studies that have generally found only a weak relationship between enzyme levels and metabolic activity levels and points out the potential limitations of using specific enzyme levels to predict metabolic activities (9, 15).

Factors affecting metabolic rate measurements.

A critical assumption made when the relationship between longevity and metabolic rate is examined is that the metabolic measurements accurately reflect metabolic rates of animals in the longevity assays. We used long-term, flow-through gas respirometry measurements to test this assumption. After an initial acclimatization period, metabolic rates remained relatively stable over both short-term and 24-h measurement periods. Although the presence of a circadian rhythm is well described in Drosophila (13, 42), we found no evidence of a large daily pattern in metabolic rate. The multigenerational rearing of flies under constant light may have minimized the effect of any circadian effect on metabolic rate in these lines.

The use of 100% nitrogen to immobilize flies before metabolic measurements has a very minor effect on their long-term metabolic rates. Likewise, the metabolic rate of unfed D. melanogaster is very similar to that of fed animals, although feeding does cause a shift to different metabolic substrates. In practical terms, this means that the short-term removal of Drosophila from food medium will not have a major short-term effect on its metabolic rate, particularly if changes in metabolic substrate utilization are taken into account.

An unexpected result of this study was the observation of substantial effects of confinement on metabolic measurements. Other investigators have measured metabolic rates on flies immobilized between cotton balls (29). We find that such measurements do not reflect metabolic rates under conditions typically used in longevity assays. Measurements of oxygen consumption on confined flies might explain reported differences in the metabolic efficiency of older flies (29). The conclusion that metabolic efficiency decreases with age was based on comparisons of the oxygen consumption of confined flies with the heat production of flies maintained in separate chambers. Because it is not clear whether flies used for heat-production measurements were similarly confined, direct comparison of the two metabolic measurements may lead to erroneous conclusions.

The effect of the freezing method on fly weight was also unexpected. Clearly, the accurate determination of the wet weight of Drosophila requires either that the fly be weighed immediately or stored at –80°C.

Because the metabolic rates of single D. melanogaster are relatively low, previous studies comparing metabolic rates and longevity in Drosophila typically measured metabolic rates in groups of flies. The use of CO2 production to measure metabolic rate allows metabolic measurements to easily be made on individual flies, which greatly increases the sample size and statistical power of experimental studies and allows the effects of individual differences in body size to be taken into account. A potential limitation, however, of the use of CO2 production to infer the metabolic rate of an organism is that the ratio of CO2 produced to oxygen consumed can vary due to factors such as metabolic substrate utilization or acid-base balance of the organism (8, 41). Data from this study demonstrate that CO2 measurements do provide an accurate proxy of oxygen consumption in D. melanogaster.

The relationship between longevity and metabolic rate in Drosophila has been studied for over 85 yr (16, 26). Numerous, sometimes conflicting, results have been published on the relationship between these two variables. Much of the confusion is likely due to the failure to measure metabolic rates under conditions that accurately match those used to assay longevity. Such measurements are essential to meaningfully determine the correlation between the longevity of an organism and its metabolic rate. Data from this study show that metabolic rate measurements of individual flies closely mirror metabolic rates of flies under conditions used to assay longevity. We also found that it is possible to both maintain a normal metabolic rate and have extended longevity. This result has now been found in two independently derived RI lines, supporting the general conclusion that extended longevity can be achieved in Drosophila without a concomitant reduction in metabolic function.


   ACKNOWLEDGMENTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
GRANTS
 REFERENCES
 
S. Nuzhdin generously provided the lines used in this study. Technical assistance from D. Tanner and S. Noorbaluchi helped greatly with the research, and two anonymous reviewers provided valuable comments.


   GRANTS
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 GRANTS
REFERENCES
 
This research was supported by National Institute on Aging Grants AG-11722, AG-08761, and AG-11659, and National Cancer Institute Grant MSI CCP NCI U56 CA96286.


   FOOTNOTES
 

Address for reprint requests and other correspondence: W. Van Voorhies, Molecular Biology Program, MSC 3MLS, New Mexico State Univ., Las Cruces, NM 88003-8001 (E-mail: wvanvoor{at}nmsu.edu).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


   REFERENCES
TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 ACKNOWLEDGMENTS
 GRANTS
 REFERENCES
 

  1. Arking R, Buck S, Novoseltev VN, Hwangbo DS, and Lane M. Genomic plasticity, energy allocations, and the extended longevity phenotypes of Drosophila. Ageing Res Rev 1: 209–228, 2002.[CrossRef][Web of Science][Medline]
  2. Arking R, Buck S, Wells RA, and Pretzlaff R. Metabolic rates in genetically based long lived strains of Drosophila. Exp Gerontol 23: 59–76, 1988.[CrossRef][Web of Science][Medline]
  3. Arking R, Burde V, Graves K, Hari R, Feldman E, Zeevi A, Soliman S, Saraiya A, Buck S, Vettraino J, and Sathrasala K. Identical longevity phenotypes are characterized by different patterns of gene expression and oxidative damage. Exp Gerontol 35: 353–373, 2000.[CrossRef][Web of Science][Medline]
  4. Austad SN and Fischer KE. Mammalian aging, metabolism, and ecology: evidence from the bats and marsupials. J Gerontol 46: 47–53, 1991.
  5. Barja G. Rate of generation of oxidative stress-related damage and animal longevity. Free Radic Biol Med 33: 1167–1172, 2002.[CrossRef][Web of Science][Medline]
  6. Bayne AC and Sohal RS. Effects of superoxide dismutase/catalase mimetics on life span and oxidative stress resistance in the housefly, Musca domestica. Free Radic Biol Med 32: 1229–1234, 2002.[CrossRef][Web of Science][Medline]
  7. Beckman KB and Ames BN. The free radical theory of aging matures. Physiol Rev 78: 547–581, 1998.[Abstract/Free Full Text]
  8. Elia M. Energy equivalents of CO2 and their importance in assessing energy expenditure when using tracer techniques. Am J Physiol Endocrinol Metab 260: E75–E88, 1991.[Abstract/Free Full Text]
  9. Gibb AC and Dickson KA. Functional morphology and biochemical indices of performance: is there a correlation between metabolic enzyme activity and swimming performance? Integ Comp Biol 42: 199–207, 2002.
  10. Hayes JP. Mass-specific and whole-animal metabolism are not the same concept. Physiol Biochem Zool 74: 147–150, 2001.[CrossRef][Web of Science][Medline]
  11. Hulbert AJ, Clancy DJ, Mair W, Braeckman BP, Gems D, and Partridge L. Metabolic rate is not reduced by dietary-restriction or by lowered insulin/IGF-1 signalling and is not correlated with individual lifespan in Drosophila melanogaster. Exp Gerontol 39: 1137–1143, 2004.[CrossRef][Web of Science][Medline]
  12. Keaney M and Gems D. No increase in lifespan in Caenorhabditis elegans upon treatment with the superoxide dismutase mimetic EUK-8. Free Radic Biol Med 34: 277–282, 2003.[CrossRef][Web of Science][Medline]
  13. Konopka RJ and Benzer S. Clock mutants of Drosophila melanogaster. Proc Natl Acad Sci USA 68: 2112–2116, 1971.[Abstract/Free Full Text]
  14. Kopp A, Graze RM, Xu S, Carroll SB, and Nuzhdin SV. Quantitative trait loci responsible for variation in sexually dimorphic traits in Drosophila melanogaster. Genetics 163: 771–787, 2003.[Abstract/Free Full Text]
  15. Laurie-Ahlberg CC, Barnes PT, Curtsinger JW, Emigh TH, Karlin B, Morris R, Norman RA, and Wilton AN. Genetic variability of flight metabolism in Drosophila melanogaster. II. Relationship between power output and enzyme activity levels. Genetics 111: 845–868, 1985.[Abstract/Free Full Text]
  16. Loeb J and Northrop JH. On the influence of food and temperature upon the duration of life. J Biol Chem 32: 103–121, 1917.[Free Full Text]
  17. Luckinbill L and Clare MJ. Selection for lifespan in Drosophila melanogaster. Heredity 55: 9–18, 1985.
  18. Luckinbill LS, Arking R, Clare MJ, Cirocco WC, and Buck SA. Selection for delayed senescence in Drosophila melanogaster. Evolution 38: 996–1003, 1984.[CrossRef][Web of Science]
  19. Marden JH, Rogina B, Montooth KL, and Helfand SL. Conditional tradeoffs between aging and organismal performance of Indy long-lived mutant flies. Proc Natl Acad Sci USA 100: 3369–3373, 2003.[Abstract/Free Full Text]
  20. Miquel J, Lundgren PR, Bensch KG, and Atlan H. Effects of temperature on the life span, vitality and fine structure of Drosophila melanogaster. Mech Ageing Dev 5: 347–370, 1976.[CrossRef][Web of Science][Medline]
  21. Mockett RJ, Orr WC, Rahmandar JJ, Sohal BH, and Sohal RS. Antioxidant status and stress resistance in long- and short-lived lines of Drosophila melanogaster. Exp Gerontol 36: 441–463, 2001.[CrossRef][Web of Science][Medline]
  22. Montooth KL, Marden JH, and Clark AG. Mapping determinants of variation in energy metabolism, respiration and flight in Drosophila. Genetics 165: 623–635, 2003.[Abstract/Free Full Text]
  23. Orr WC, Mockett RJ, Benes JJ, and Sohal RS. Effects of overexpression of copper-zinc and manganese superoxide dismutases, catalase, and thioredoxin reductase genes on longevity in Drosophila melanogaster. J Biol Chem 278: 26418–26422, 2003.[Abstract/Free Full Text]
  24. Orr WC and Sohal RS. Does overexpression of Cu-Zn-SOD extend life span in Drosophila melanogaster? Exp Gerontol 38: 227–230, 2003.[CrossRef][Web of Science][Medline]
  25. Packard GC and Boardman TJ. The use of percentages and size-specific indices to normalize physiological data for variation in body size: wasted time, wasted effort? Comp Biochem Physiol A 122: 37–44, 1999.[CrossRef]
  26. Pearl R. The Rate of Living. New York: Knopf, 1928.
  27. Promislow DE and Haselkorn TS. Age-specific metabolic rates and mortality rates in the genus Drosophila. Aging Cell 1: 66–74, 2002.[CrossRef][Web of Science][Medline]
  28. Rohlf FJ, and Sokal RR. Statistical Tables. New York: Freeman, 1995.
  29. Ross RE. Age-specific decrease in aerobic efficiency associated with increase in oxygen free radical production in Drosophila melanogaster. J Insect Physiol 46: 1477–1480, 2000.[CrossRef][Web of Science][Medline]
  30. Rubner M. Das Problem der Lebensdauer und seine Beziehungen sum Wachstum und Ernahrung. Munich, Germany: Oldenbourg, 1908.
  31. Sohal RS. Oxidative stress hypothesis of aging. Free Radic Biol Med 33: 573–574, 2002.[CrossRef][Web of Science][Medline]
  32. Sohal RS and Allen RG. Relationship between oxygen metabolism, aging and development. Adv Free Rad Biol Med 2: 117–160, 1986.
  33. Sohal RS, Mockett RJ, and Orr WC. Mechanisms of aging: an appraisal of the oxidative stress hypothesis. Free Radic Biol Med 33: 575–586, 2002.[CrossRef][Web of Science][Medline]
  34. Speakman JR, Selman C, McLaren JS, and Harper EJ. Living fast, dying when? The link between aging and energetics. J Nutr 132: 1583–1597, 2002.
  35. Speakman JR, Talbot DA, Selman C, Snart S, McLaren JS, Redman P, Krol E, Jackson DM, Johnson MS, and Brand MD. Uncoupled and surviving: individual mice with high metabolism have greater mitochondrial uncoupling and live longer. Aging Cell 3: 87–95, 2004.[CrossRef][Web of Science][Medline]
  36. Speakman JR, van Acker A, and Harper EJ. Age-related changes in the metabolism and body composition of three dog breeds and their relationship to life expectancy. Aging Cell 2: 265–275, 2003.[CrossRef][Web of Science][Medline]
  37. Spencer CC, Howell CE, Wright AR, and Promislow DE. Testing an ‘aging gene’ in long-lived Drosophila strains: increased longevity depends on sex and genetic background. Aging Cell 2: 123–130, 2003.[CrossRef][Web of Science][Medline]
  38. Van Voorhies WA, Khazaeli AA, and Curtsinger JW. The relationship between body mass and metabolic rate in Drosophila melanogaster. J Insect Physiol 50: 445–453, 2004.[CrossRef][Web of Science][Medline]
  39. Van Voorhies WA, Khazaeli AA, and Curtsinger JW. Selected contribution: long-lived Drosophila melanogaster lines exhibit normal metabolic rates. J Appl Physiol 95: 2605–2613; discussion 2604, 2003.[Abstract/Free Full Text]
  40. Van Voorhies WA and Ward S. Genetic and environmental conditions that increase longevity in Caenorhabditis elegans decrease metabolic rate. Proc Natl Acad Sci USA 96: 11399–11403, 1999.[Abstract/Free Full Text]
  41. Walsberg GE and Wolf BO. Variation in the respiratory quotient of birds and implications for indirect calorimetry using measurements of carbon dioxide production. J Exp Biol 198: 213–219, 1995.[Abstract]
  42. Wilsbacher LD and Takahashi JS. Circadian rhythms: molecular basis of the clock. Curr Opin Genet Dev 8: 595–602, 1998.[CrossRef][Web of Science][Medline]
  43. Yan LJ and Sohal RS. Prevention of flight activity prolongs the life span of the housefly, Musca domestica, and attenuates the age-associated oxidative damage to specific mitochondrial proteins. Free Radic Biol Med 29: 1143–1150, 2000.[CrossRef][Web of Science][Medline]



This article has been cited by other articles:


Home page
 J. Exp. Biol.Home page
W. A. Van Voorhies
Metabolic function in Drosophila melanogaster in response to hypoxia and pure oxygen
J. Exp. Biol., October 1, 2009; 212(19): 3132 - 3141.
[Abstract] [Full Text] [PDF]

Home page
 J. Exp. Biol.Home page
O. Evans, E. P. Caragata, C. J. McMeniman, M. Woolfit, D. C. Green, C. R. Williams, C. E. Franklin, S. L. O'Neill, and E. A. McGraw
Increased locomotor activity and metabolism of Aedes aegypti infected with a life-shortening strain of Wolbachia pipientis
J. Exp. Biol., May 15, 2009; 212(10): 1436 - 1441.
[Abstract] [Full Text] [PDF]

Home page
 Proc R Soc BHome page
C. Selman, J. S McLaren, A. R Collins, G. G Duthie, and J. R Speakman
The impact of experimentally elevated energy expenditure on oxidative stress and lifespan in the short-tailed field vole Microtus agrestis
Proc R Soc B, August 22, 2008; 275(1645): 1907 - 1916.
[Abstract] [Full Text] [PDF]

Home page
 J. Neurosci.Home page
A. M. Celotto, A. C. Frank, S. W. McGrath, T. Fergestad, W. A. Van Voorhies, K. F. Buttle, C. A. Mannella, and M. J. Palladino
Mitochondrial Encephalomyopathy in Drosophila
J. Neurosci., January 18, 2006; 26(3): 810 - 820.
[Abstract] [Full Text] [PDF]

Home page
 GeneticsHome page
S. V. Nuzhdin, A. A. Khazaeli, and J. W. Curtsinger
Survival Analysis of Life Span Quantitative Trait Loci in Drosophila melanogaster
Genetics, June 1, 2005; 170(2): 719 - 731.
[Abstract] [Full Text] [PDF]

Home page
 GeneticsHome page
A. A. Khazaeli, W. Van Voorhies, and J. W. Curtsinger
Longevity and Metabolism in Drosophila melanogaster: Genetic Correlations Between Life Span and Age-Specific Metabolic Rate in Populations Artificially Selected for Long Life
Genetics, January 1, 2005; 169(1): 231 - 242.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow All Versions of this Article:
97/5/1915    most recent
00505.2004v1
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Web of Science (8)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Van Voorhies, W. A.
Right arrow Articles by Curtsinger, J. W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Van Voorhies, W. A.
Right arrow Articles by Curtsinger, J. W.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Visit Other APS Journals Online
Copyright © 2004 by the American Physiological Society.