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Calorie restriction speeds mitochondrial turnover

Posted Dec 11 2008 9:10pm

Mitochondria are central to many theories of aging because they produce damaging reactive oxygen species (ROS) as a by-product of normal function. Over time, ROS can degrade mitochondrial DNA (mtDNA), interfering with cellular energy production.

The cell’s strategy for dealing with this damage is to recycle its mitochondria on a regular basis: some are destroyed while others replicate (whether these processes are ultimately under nuclear or mitochondrial control, or even whether they are random or directed, is still unknown). Mitochondria replicate far more often than the cells they live in: every couple of days in liver cells, and every few weeks in post-mitotic brain cells. Generally, the faster the mitochondria turn over, the better – it’s a good idea to replace mitochondrial populations before too much mtDNA damage accumulates.

In experiments, mitochondrial turnover is measured using a radio-isotope pulse-chase assay. The basic idea is to radioactively label some mitochondrial proteins, then check at regular time intervals to see whether those proteins are still present in mitochondria. Assuming that a mitochondrion degrades as a unit, if its proteins are no longer around, then we can infer that it has been degraded. Turnover rate is measured as the half-life of an exponential decay curve fitted to a plot of mitochondrial radioactivity vs. time.

Oddly, most of the data we have on tissue-specific rates of mitochondrial turnover are decades old, and estimates across studies vary wildly (up to an order of magnitude). A recent paper by Miwa et al. explains these discrepancies and also shows that calorie restriction speeds mitochondrial turnover:

Mitochondrial turnover in liver is fast in vivo and is accelerated by dietary restriction: application of a simple dynamic model

‘Mitochondrial dysfunction’, which may result from an accumulation of damaged mitochondria in cells due to a slowed-down rate of mitochondrial turnover and inadequate removal of damaged mitochondria during aging, has been implicated as both cause and consequence of the aging process and a number of age-related pathologies. Despite growing interest in mitochondrial function during aging, published data on mitochondrial turnover are scarce, and differ from each other by up to one order of magnitude. Here we demonstrate that re-utilization of the radioactively labelled precursor in pulse-chase assays is the most likely cause of significant overestimation of mitochondrial turnover rates. We performed a classic radioactive label pulse-chase experiment using 14 C NaHCO 3, whose 14 C is incorporated into various amino acids, to measure mitochondrial turnover in mouse liver. In this system, the activity of the urea cycle greatly limited arginine dependent label re-utilization, but not that of other amino acids. We used information from tissues that do not have an active urea cycle (brain and muscle) to estimate the extent of label re-utilization with a dynamic mathematical model. We estimated the actual liver mitochondrial half life as only 1.83 days, and this decreased to 1.16 days following 3 months of dietary restriction, supporting the hypothesis that this intervention might promote mitochondrial turnover as a part of its beneficial effects.

The main result of this paper is that calorie restriction makes mitochondria turn over a substantial 35% faster, at least in mouse liver. This provides another explanation for the recent finding that CR protects mtDNA from age-related damage.

What may prove even more important than that result, however, is the paper’s critique of standard pulse-chase assay analysis and novel methodology for quantifying mitochondrial turnover. Here’s a brief summary of the authors’ logic:

  • Label reuse means that turnover times are overestimated
    The problem with the usual pulse-chase assay is what the authors call “label re-utilization”: after a mitochondrion degrades, some of the labelled pieces of its proteins may be incorporated into a new protein and a different mitochondrion. This causes turnover time to be overestimated, because it looks like the original mitochondrion hasn’t been degraded. Miwa et al. attribute the large differences in earlier estimates of turnover rates to the practice of fitting a single exponential curve to plots of mitochondrial radioactivity vs. time – in other words, by failing to control for the effects of label reuse.
  • A two-component model can control for the effects of label reuse
    In the liver, the urea cycle converts most 14 C (from 14 C NaHCO 3 ) into the amino acid arginine; and little arginine is reused because arginase is very active in the liver. However, some of the 14 C gets incorporated into different amino acids – and these can be reused. This means that if we plot radioactivity as a function of time, a single exponential curve won’t fit the data very well. However, Miwa et al. show that a simple sum of two components – a fast exponential decay (from the arginine, the ‘signal’), and a slow linear decrease (from other amino acids that are re-used, the ‘noise’) – can do much better. Of course, it’s not surprising that a two-component model can beat a one-component one – more components means more parameters to tweak and better fitting flexibility. Using data from other tissues, the authors demonstrate that their model is more general than that – the theoretical slow component in liver mitochondria is very similar to the measured decay of 14 C in other tissues where there is no urea cycle (i.e. tissues where most of the 14 C doesn’t become arginine and the ‘noise’ component dominates). In the two-component model, turnover rate is measured as the half-life of the fast exponential process.

Perhaps one major reason why mitochondrial turnover rates were so little studied in recent years was purely methodological – no one knew what caused the discrepancies in earlier estimates, or what to do about the poor quality of exponential fits. Using two-component models, it should now be straightforward to derive rigorous estimates of turnover rates in a variety of experimental conditions relevant to aging.

      
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