Scientists from MIPT, together with colleagues from the USA, the Hong Kong-based company Gero and the Northern Federal University in Archangelsk, have published a work shedding light on the aging process that, in part, explains the secret behind the remarkable life expectancy seen in certain animal species.
The article published by the authors in the journal Scientific Reports presents a mathematical model describing the behavior of a so-called gene network. After analyzing their model, the scientists were able to determine the accumulation of errors in the mechanisms regulating cell processes and as a result were able to assess the associated fatality rates in organisms.
The data obtained provided evidence that, in the majority of cases, regulation errors accumulate exponentially with age. This correlates well with the long-known Gompertz Law, which states that fatality rates (including in humans) grow exponentially with age. At the same time, the authors of the article have successfully taken into account the role of the repair systems that correct errors in biochemical mechanisms; if they are sufficiently active, it is possible to prevent the avalanche-like growth in the number of breakdowns and thus ensure the very remarkable longevity that has intrigued biologists for so long.
A long life without aging – it does exist
Unbelievable longevity can be found in many animals. Naked mole-rats, Heterocephalus glaber, living in East African underground burrows can live as long as thirty years: ten times longer than ordinary rats or mice. As life expectancy usually decreases with a decrease in size, many scientists believe that if a human had such biochemical mechanisms they could live for several centuries without ever suffering from aging and in addition, almost never falling ill with cancer.
Several species of turtle also have long lifespans. Although not large, about 25 cm in length, the painted turtle can live up to 60 years and again, signs of aging do not appear until death itself. The phenomenon of “negligible senescence” has until now not been understood and for this reason the authors of the new publication have even included it in the headline – the article is titled “Stability analysis of a model gene network links aging, stress resistance, and negligible senescence.”
We underline that “stress” in this case should be understood not in the psychological sense: we are talking here not of nervous tension, but about different influences that are capable of disrupting the activity of the gene network. The gene network in its turn consists of the totality of genes and proteins that encode these genes and can themselves affect gene activity, which is the synthesis of new protein molecules.
What is the gene network and why is it studied
The simplest gene network can be described as follows: two genes, A and B, each of which encodes its own protein. The genes by definition represent a segment of DNA. However, DNA alone is insufficient for the synthesis of a protein. Special enzymes are needed to copy the information from the DNA to an intermediary media, ribonucleic acid (RNA), then the intermediary molecule of RNA attaches to ribosomes, and then the ribosomes synthesize the required protein from amino acids.
The protein can also carry out a function in the cell (act as a “building block,” an enzyme or a receptor on the membrane surface), or interact with DNA to block or stimulate the process of replication. If gene A codes for a protein that interacts with the DNA in the place where gene B is located, we see an interaction between the two genes. Such connections between genes are very important for the normal function of a cell, and it is very easy to demonstrate this using the example of these two hypothetical genes.
Protein A coded by gene A can initiate the synthesis of protein B by enabling it to read the necessary information from the DNA. And here, protein B can, on the contrary, block access to gene A and thus the accumulation of protein A in the cell is limited. As soon as too much A has accumulated, the B protein synthesized under its influence ceases the production of new A molecules. If, on the other hand, too much B accumulates, then the A protein necessary for the synthesis of molecules of protein B disappears – in such a system the concentrations of A and B remain within the required limits.
Real gene networks contain hundreds and sometimes thousands of genes, and the interactions between them can be much more complicated than in the above example. It is common to come across a situation where “C and D form a complex CD, which initiates the expression* of E,F and G, and G suppresses the synthesis of A, while F is responsible for the synthesis of B receptors, which enable the cells to react with the hormone H and using the proteins J,K,L and M increase the synthesis of D and G.” Maps of just those gene networks that are known today, if put on paper, would cover the wall of a gym, and the prediction using such maps in the behavior of cells can only be made with the use of sufficiently powerful computers.
* Gene expression is the name used for the synthesis of proteins using information copied by a gene.
Gene networks, the totality of interconnected genes and proteins, describe virtually every major process in an organism. The transformation of an ordinary cell into a cancerous one, the self-destruction of cells (though not in tumor cells, which is described by uncontrolled growth), the acquisition of specializations by embryonic stem cells during growth; even the assimilation of new information by the brain – these are all connected to gene networks.
What was modeled and what were the results
In the present work, the scientists studied a gene network that was initially in a normal state and was then subjected to certainadverse stimuli, leading to the accumulation of regulatory errors. Feedback paths, which limit the expression (synthesis of a coded protein) of genes, cease working, and thus allow errors to develop in the regulation of gene function so that the synthesis of normal proteins becomes impossible: if such errors become too numerous, the organism dies. This accumulation of errors is countered byrepair systems (literally – “fixes”) that are found in all real cells: a large part of regulation errors are removed by special inner-cellular mechanisms.
The researchers did not model in detail whether the process of DNA damage was caused by radiation or chemically active substances that form in every cell because it is not this that is important for the model; what is important is how many “breakages” occur, the reason they arise can be ignored as far as the overall modeling is concerned.
Upon analyzing the behavior of the model in different conditions, the authors of the research came the conclusion that a repair system is usually capable of dealing with the majority of stresses (in the biochemical sense of the word), though errors still accumulate. Comparison of the model with data obtained from a real functional gene network in a drosophila fly revealed that, in living organisms, the older the organism, the larger the difference in gene expression from that found when young. The scientists also highlight that stress can harm not just the whole gene network, but also the repair mechanism, because the rate with which errors accumulate also increases linearly with the time passed since the system of “self-fixes” fails and ceases to counter the stress.
Put simply, to counter the avalanching growth in the number of breakdowns in a gene network, several methods can be used, though not all of these are equally effective. The scientists have demonstrated that it is possible for organisms to live long with very low error accumulation rates and, what is particularly important, have connected each element* in their equations with one oranother strategy for increasing an organism’s lifespan!
* If we look at the simple equation z = ax + by, it is clear that the rate z is dependent on two elements and the coefficient values are a and b; analyzing these elements we can see the behavior of the whole function. For example, if b has a very large negative value at the same time as a is positive but not large, z, with the growth of x and y, will die. But if y is negative it will grow as it moves down along the y axis.
How to live long and not fall ill according to this new model
To implement this model one could try, for example, to isolate the genes from environmental influences (how this can be realized in practice is another question); however, in this case the gene network will still with time demonstrate its earlier tendency to instability. The analysis of the equation selected by the researchers shows that it is better to try and find some way of extending life. Other research, to which the authors of the new article refer, shows that mutations in DNA as such are responsible for just a small percentage of the process of aging: the lion’s share is caused not by breakdowns in the DNA itself, but by faults in the regulation of gene activity.
A more effective approach is to reduce the number of interactions between proteins and DNA: the less that DNA collides with protein, the less likely that in the course of this interaction, something will go wrong. For some organisms – including the genetically engineered variant of the Caenorhabditis elegans worm — this strategy has been realized. But there are still seriousdoubts whether it can be transferred to humans.
You can also fight the “wear and tear” of protein molecules and, indirectly, DNA errors. Many organisms, including theaforementioned naked mole-rats, have gone down this road, achieving spectacular success. Previous research referred to by the scientists in their article demonstrates the presence in these African rodents of an especially effective system for destroyingdefective protein molecules: they are effectively broken down before the gene network is able to stop working. And if it is not possible to deal with the accumulated errors at the individual cell level, then the whole defective cell can be eliminated: in short,such a method can only be used by multi-cellular organisms.
Finally, the researchers have pointed out the connection between errors and genome size. When there are very many genes, the likelihood of mutation or some other breakdown is high. As many organisms need a large number of genes for normal development, it is not the DNA itself that is reduced but the number of active, that is expressed, genes. There are about 20 thousand genes in our DNA cells, but the majority of them are “silent” due to not being needed: the cells of heart muscles do not need neuron receptors, and neurons do not need those proteins that ensure the reduction of all cells under the influence of nerve impulses. Cell specialization assures the prevention of errors in the gene network.
Comments by the researchers
Valeriy Kogan: “As Phystech graduates we are very interested in the possibility of applying physical methods to biological tasks, the solutions of which may lead to the development of principally new medicines.”
Petr Fedichev: “We believe that the aging model we’ve proposed allows for a significant acceleration in the development of new therapies aimed at the significant enhancement of a healthy life-expectancy in humans.”