Today I'll point out a two open access papers on the mapping of genetics, epigenetics, aging, and age-related disease. There is a lot of this sort of work taking place these days: it is ever easier to raise funding for any sort of work on genetics, and this is the beginning of the age of practical gene therapy. Intervening in the aging process to slow or reverse aging, as opposed to trying to patch over the late stage consequences of specific age-related diseases without actually touching the processes of aging itself, remains a comparatively small initiative within the aging research community. Most funded work on aging goes towards cataloging and mapping, a part of the great life science initiative to produce a comprehensive atlas of living biology from top to bottom: how our cells and tissues work, and how every function of every biological system changes over time, understood all the way down to the roles of individual molecular interactions. This is an enormous project, staggering in scope, with the present vast databases of molecular biochemistry just a sketch of the outline of the whole when held up against the bigger picture. Barring revolutionary advances in automation and computation this project will be nowhere near complete even several decades from now.
To the extent that factions within the scientific community prioritize complete understanding of aging above interventions in aging based on what we do already know, they have decided that no significant progress on lengthening life will be made in our lifetimes. If this view dominates, the future will be much the same as the recent past, in that the steady upward slope of small gains in adult life expectancy will continue, with the bulk of these benefits arising from incidental side-effects of the standard medical approach to late-stage aging and age-related disease. The patches will get better, but a patched and damaged system still fails; the patch can only delay the inevitable. The most important debate in medical research today is between those who prioritize full understanding and slow progress towards slowing aging versus those who want to take the current catalog of known differences between old and young tissue and fix them in advance of full understanding. That will cost a lot less and achieve answers on the relevance of this damage to aging and rejuvenation far more rapidly than any other methodology.
Unfortunately, gathering greater support and adoption of work on biological repair and rejuvenation is still an uphill battle, despite successes in the making such as senescent cell clearance, an approach gathering more attention these days. The majority of research into aging looks a lot more like the open access papers linked here, which is to say it is interesting, largely focused on genetics, generates a lot of data, and is of little practical use in the near term. Altering gene expression levels in the hopes of improving the situation for older people is somewhat like adjusting the fuel balance of a rusted and worn engine in the hopes that it will run a little longer. It misses the point, the direct and most useful thing that could be done to improve matters. The grand map of molecular biochemistry is absolutely something that should be constructed, and will be of great use to the next generation of biotechnologies - but to focus on that entirely is to sacrifice countless lives, when the research and development community could also be building the first generation of therapies that will help bring an end to degeneration aging.
Although our knowledge of aging has greatly expanded in the past decades, it remains elusive why and how aging contributes to the development of age-related diseases (ARDs). In particular, a global mechanistic understanding of the connections between aging and ARDs is yet to be established. We rely on a network modelling named "GeroNet" to study the connections between aging and more than a hundred diseases. By evaluating topological connections between aging genes and disease genes in over three thousand subnetworks corresponding to various biological processes, we show that aging has stronger connections with ARD genes compared to non-ARD genes in subnetworks corresponding to "response to decreased oxygen levels", "insulin signalling pathway", "cell cycle", etc.
Based on subnetwork connectivity, we can correctly "predict" if a disease is age-related and prioritize the biological processes that are involved in connecting to multiple ARDs. Using Alzheimer's disease (AD) as an example, GeroNet identifies meaningful genes that may play key roles in connecting aging and ARDs. The top modules identified by GeroNet in AD significantly overlap with modules identified from a large scale AD brain gene expression experiment, supporting that GeroNet indeed reveals the underlying biological processes involved in the disease.
In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the "gerontome". Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remains a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease (ARD) genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms.
We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.
We first characterized functions and pathways overrepresented in pro- and anti-longevity genes. Major anti-longevity pathways and processes include insulin signaling, growth hormone signaling and mTOR signaling. Key pro-longevity pathways include p53, cell cycle and autophagy. Although such pathways and processes are known to be related to aging, it is interesting that they are classified as anti-and pro-longevity in our systematic analysis of the genetics of aging. Differentiation between anti-longevity and pro-longevity genes and processes can provide additional clues about aging-related processes and can help identify other genes with a similar effect on aging. In order to find relations between aging and ARD, we compared aging-related gene sets with ARD genes. Limitations of our study include the fact that possibly many genes associated with longevity and disease remain to be identified, and the causal genes in many genetic associations with disease are still unknown. In spite of these caveats, our results show an association between aging and ARDs at the genetic level, although this is surprisingly species-specific with a stronger overlap in mice than in invertebrates (flies and worms) and practically no overlap in yeast.
The overlap analyses of anti- and pro-longevity genes shows differences in musculoskeletal, nervous system and cardiovascular diseases. The identified overlaps suggest that the musculoskeletal and nervous systems are related to pro-longevity genes while anti-longevity genes seem more associated with cardiovascular diseases. Looking at ARD classes which overlap with human aging-related genes, a significant overlap is verified for all classes as expected, except for immune system diseases. The nutritional and metabolic diseases, the neoplasms, the cardiovascular diseases and the nervous system diseases have the most significant overlap with human aging-related genes. Eye diseases, respiratory tract diseases (which we considered a negative control) and immune system diseases had the least overlap, but it is important to mention that these are (together with musculoskeletal diseases) the age-related disease classes with fewer genes.
The main conclusion from this work is that aging and age-related diseases are related and share more genes than expected by chance. Human aging-related genes showed a considerable overlap with ARDs. These overlaps are driven by a small subset of aging-related genes which are associated with various age-related diseases and are hubs in networks. Besides, the extent of overlaps decreases with the increase of evolutionary distance, and yeast aging-related genes show practically no overlap with ARDs. Novel differences in overlapping age-related disease classes between anti- and pro-longevity genes were observed: Nervous system and musculoskeletal diseases seem more associated with pro-longevity, while cardiovascular diseases have a stronger association with anti-longevity genes. Moreover, network analyses suggest the existence of intermediate genes which promote the associations between aging and age-related disease genes.