Mitochondrial genomes are retained by selective constraints on protein targeting

http://www.pnas.org/content/early/2015/07/17/1421372112

Björkholm P, Harish A, Hagström E, Ernst AM, Andersson SG

Mitochondria originated through the fusion of two early bacterial forms of life: an endosymbiont giving rise to mitochondria, and a host to modern-day eukaryotic cells. Each of these original organisms contained their own genomes; however, over evolutionary time, there has been genetic transfer from mitchondrial DNA to nuclear DNA.

Several competing hypotheses exist to explain why any particular gene may be retained by mitochondria. One hypothesis is that a core set of genes must be retained by these organelles to retain local control over respiration (a similar argument exists for chloroplasts in photosynthesis, this is the CoRR hypothesis).

In this study, the authors provide evidence for the hydrophobicity of the gene products to determine gene retention. They predict that mitochondrially-encoded proteins have a larger insertion free energy than nuclear-encoded proteins. They show experimentally that when these proteins are expressed in the nucleus, they tend to be recognised by the signal recognition particle (SRP), and targeted to the endoplasmic reticulum, rather than mitochondria. This is due to the SRP's ability to bind to a hydrophobic domain. This may be problematic for gene therapies attempting to alleviate mitochondrial genetic diseases, by expression of such genes in the nucleus.

The authors discuss that hydrophobic proteins are unlikely to fold properly in the cytoplasm, and their import into double-membraned organelles like mitochondria, would be difficult (and potentially toxic if unfolded proteins were to accumulate). The authors emphasise that the hydrophobicity hypothesis is not mutually exclusive to the CoRR hypothesis, and many selective pressures are likely to operate on organelle genomes.

Describing the randomness in populations of mtDNA (and other stuff) within and over cell cycles

http://rspa.royalsocietypublishing.org/content/471/2180/20150050.full

Cell biology is a unpredictable world*! Molecules in the cell undergo diffusive motion, constant jostling, and interference from other molecules, meaning that precisely describing the motion of every atom is very hard and rarely useful. Instead, it's often more useful to consider biological processes as occurring "randomly", forgetting the precise details of all these complicating effects and just thinking about a reasonable "coarse-grained" model for their influence. In this (demonstrably powerful) picture, important machines in our cells -- including mitochondria, and particularly mtDNA -- replicate and degrade in processes that can be described as random; and when cells divide, the partitioning of these machines between the resulting cells also looks random. The number of machines we have in our cells is important, but how can we work with numbers in this unpredictable environment?

In our cells, elements including mtDNA are produced (red), replicate (orange), and degrade (purple) randomly with time, as well as being randomly partitioned when cells split and divide (blue). Our mathematical approach describes how the total number of machines is likely to behave and change with time and as cells divide.

Tools called "generating functions" are useful in this situation. A generating function is a mathematical function (like G(z) = z², but generally more complicated) that encodes all the information about a random system. To find the generating function for a particular system, one needs to consider all the random things that can happen to change the state of that system, write them down in an equation (the "master equation") describing them all together, then use a mathematical trick to push that equation into a different mathematical space, where it is easier to solve. If that "transformed" equation can be solved, the result is the generating function, from which we can then get all the information we could want about a random system: the behaviour of its mean and variance, the probability of making any observation at any time, and so on.

We've gone through this mathematical process for a set of systems where individual cellular machines can be produced, replicated, and degraded randomly, and split at cell divisions in a variety of different ways. The generating functions we obtain allow us to follow this random cellular behaviour in new detail. We can make probabilistic statements about any aspect of the system at any time and after any number of cell divisions, instead of relying on assumptions that the system has somehow reached an equilibrium, or restricting ourselves to a single or small number of divisions. We've applied this tool to questions about the random dynamics of mitochondrial DNA in cells that divide (like our cells) or "bud" (like yeast cells), but the approach is very general and we hope it will allow progress in many more biological situations.

* See, for example, http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2012.00586.x/abstract ( free version http://arxiv.org/abs/1208.2250 )

Elucidating the mechanism of the mtDNA bottleneck

http://elifesciences.org/content/early/2015/06/04/eLife.07464

Our mitochondrial DNA (mtDNA) provides instructions for building vital machinery in our cells. MtDNA is inherited from our mothers, but the process of inheritance -- which is important in predicting and dealing with genetic disease -- is poorly understood. This is because mitochondrial behaviour during development (the process through which a fertilised egg becomes an independent organism) is rather complex. If a mother's egg cell begins with a mixed population of mtDNA -- say with some type A and some type B -- we usually observe hard-to-predict mtDNA differences between cells in the daughter. So if the mother's egg cell starts off with 20% type A, egg cells in the daughter could range (for example) from 10%-30% of type A, with each different cell having a different proportion of A. This increase in variability, referred to as the mtDNA bottleneck, is important for the inheritance of disease. It allows cells with higher proportions of mutant mtDNA to be removed; but also means that some cells in the next generation may contain a dangerous amount of mutant mtDNA. Crucially, how this increase in variability comes about during development is debated. Does variability increase because of random partitioning of mtDNAs at cell divisions? Is it due to the decreased number of mtDNAs per cell, increasing the magnitude of genetic drift? Or does something occur during later development to induce the variability? Without knowing this in detail, it is hard to propose therapies or make predictions addressing the inheritance of disease.

We set out to answer this question with maths! Several studies have provided data on this process by measuring the statistics of mixed mtDNA populations during development in mice. The different studies provided different interpretations of these results, proposing several different mechanisms for the bottleneck. We built a mathematical framework that was capable of modelling all the different mechanisms that had been proposed. We then used a statistical approach called approximate Bayesian computation to see which mechanism was most supported by the existing data. We identified a model where a combination of copy number reduction and random mtDNA duplications and deletions is responsible for the bottleneck. Exactly how much variability is due to each of these effects is flexible -- going some way towards explaining the existing debate in the literature.  We were also able to solve the equations describing the most likely model analytically. These solutions allow us to explore the behaviour of the bottleneck in detail, and we use this ability to propose several therapeutic approaches to increase the "power" of the bottleneck, and to increase the accuracy of sampling in IVF approaches.

A "bottleneck" acts to increase mtDNA variability between generations. But how is this bottleneck manifest? Our approach suggests that a combination of copy number reduction (pictured as a "true" copy number bottleneck), and later random turnover of mtDNA (pictured as replication and degradation), is responsible.

Our excellent experimental collaborators, lead by Joerg Burgstaller, then tested our theory by taking mtDNA measurements from a model mouse that differed from those used previously and which, could in principle have shown different behaviour. The behaviour they observed agreed very well with the predictions of our theory, providing encouraging validation that we have identified a likely mechanism for the bottleneck. New measurements also showed, interestingly, that the behaviour of the bottleneck looks similar in genetically diverse systems, providing evidence for its generality.

High-fat diet and FGF21 cooperatively promote aerobic thermogenesis in mtDNA mutator mice

http://www.pnas.org/content/early/2015/06/23/1509930112.full.pdf

Christopher E. Wall, Jamie Whyte, Jae M. Suh, Ronald M. Evans et al.

The POLG mutator mouse is a well-known model of premature aging. They express a proofreading-deficient version of POLG, causing them to introduce point-mutations and deletions in their mitochondrial genome. The aging phenotype is visible from 9 months onwards, and yet most mutations accumulate during embryogenesis. This study sought to characterise younger mutator mice, which bare much of the mtDNA damage of older mice, but relatively little respiratory chain dysfunction, and without a progerioid phenotype.

To do this, the authors challenged the mice with a high-fat diet (HFD). The expectation was that these mice would fair poorly under such a diet, but surprisingly the mice appeared healthier than controls. POLG mice were highly resistant to weight gain, and had much lower insulin levels relative to controls. These mice also had a substantially higher mitochondrial content and oxygen consumption rate in their brown adipose tissue, once given a HFD. POLG mice on a normal diet have an abnormally low body temperature (by ~4°C), but HFD allowed the mice to recover normal core temperature, through aerobic thermogenesis.  

The gene FGF21, which is thought to mediate the benefits of caloric restriction (but also signals mitochondrial stress), was substantially upregulated in POLG mice in both HFD and normal diets. Thus, the authors suggest that young POLG mice are in a metabolic state of starvation. Since calorie restriction is associated with longevity, they suggest that these observations indicate a compensatory response, to oppose their mutated mtDNA. However, as the mice age, they eventually succumb to the progerioid phenotype. 

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Thoughts:

The authors suggest in their discussion that lipids from a HFD are able to function as a preferential metabolic substrate. From this reasoning, does it follow that mice supplemented with a HFD should have a delayed/ameliorated progerioid phenotype? The discussion suggests not, but I wonder why this isn't the case...

Physical exercise improves brain cortex and cerebellum mitochondrial bioenergetics and alters apoptotic, dynamic and auto(mito)phagy markers.

 http://www.ncbi.nlm.nih.gov/pubmed/26116519?dopt=Abstract

Marques-Aleixo I, Santos-Alves E, Balça MM, Rizo-Roca D, Moreira PI, Oliveira PJ, Magalhães J, Ascensão A

Physical exercise does not only trigger the release of endorphins, it is good for your brain mitochondria!

Eighteen male rates were divided in three groups, 1) a group without physical activity, 2) a group with voluntary free wheel activity, and 3) a group with treadmill endurance training, 5 days a week for 12 weeks.
Some behavioural tests were performed, and eventually the brains of the decapitated rats were washed and analysed.

What were the results? From the behavioural point of view, the mice from group 3 (the most active group) showed a general increase in activity and more willingness to explore new spaces. What about the condition of the brains? In both group 2 and 3, they found:

• an increase in state III mitochondrial respiration
• an increase in efficiency of ATP synthesis in brain cortex and cerebellum mitochondria
• an increased content of complex I, III and V subunits in brain cortex and cerebellum mitochondria
• increases in complex I and V activity in brain cortex mitochondria
• a decrease in lipid peroxidation
• less oxidative stress
• reduced apoptosis in cerebellum mitochondria
• increased PGC1alpha and TFAM (which stimulate mitochondrial replication) in the brain cortex
• Increased mitochondrial fusion (Mfn1,2 were increased, and Drp1 decreased)
• Increased autophagy markers

It therefore appears that exercise improves mitochondrial health in the brain. A recent review discusses how important healthy mitochondrial functioning is in the brain, and how impairment of mitochondrial fusion dysregulates neuronal function.

In conclusion, take some time off work and go do some exercise.