Health and Aging Problem

6) In muscular system, there is: muscle fiber loss, muscle atrophy, and decrease of muscle strength (Johnson et al., 2013; Mercken et al., 2017) that can lead to general fatigue.

7) In integumentary system, there is: loss and disorganization of collagen and decreased skin elasticity that can lead to delayed wound healing (Corseti et al., 2014; Quan and Fisher, 2015) or lower protection against UV light.

8) In immune system, there is decline in number of macrophage precursors and macrophage activity, number of basophils, T-cells, functional decline in eosinophils and increase in natural killer cells (NK-cells), that leads to decreased ability for fighting diseases and increased level of cytotoxicity and inflammation throughout the whole body (Montecino-Rodriguez, Berent-Maoz & Dorshkind, 2013; Kapetanovic, Bokil & Sweet, 2015).

9) In nervous system, there is loss of neuronal cells and degeneration of neurons that leads to synaptic efficacy impairment and altered communication with the whole body (Bouchard and Vileda, 2015; Brosel et al., 2016; Van Houcke et al., 2015).

10) In endocrine system, there is a decrease in level of growth hormone (GH) and insulin-like growth factor 1(IGF-1) and increase of cortisol release with age that can lead to various hormonal changes, nutrient sensing, and even sarcopenia (López-Otín et al., 2013; Vitale, Salvioli & Franceschi, 2013; Vitale, Cesari & Mari, 2016).

11) In reproductive system, changes in e.g. number and quality of sperm and eggs can lead to impairment of reproductive abilities (Gunes, 2016; Tilly and Sinclair, 2013).

Most changes related to ageing are at first localized in a certain system, but because body systems are connected - age related changes in one system influence other systems and failure of one system may lead to failures in others. Some systems are of course more important in homeodynamics (e.g. simple clot in blood stream may lead to massive failure of all systems), but all systems age and influence one another. For example, inflammation influences ageing bones (Abdelmagid, Barbe & Safadi, 2015), ageing muscles can increase risk of having cancer (Demontis et al, 2013), hypertension can cause decline of cognitive abilities and brain functioning (Buford, 2016), and maybe even more, because brain might be a central regulator of lifespan (Bouchard and Vileda, 2015).

And that is just the simplified picture of what is already known regarding ageing body. So what causes ageing? We still don’t know. But scientists have learned enough to find some interventions that can slow down ageing process, at least in model animals.

PART II. HOW CAN WE SLOW DOWN (OR MAYBE EVEN REVERSE) AGE-RELATED CHANGES

II.1. Interventions and therapeutics in ageing

First of all, there are different most common reasons for age-related changes and causes of death in different communities and it might be because of different socioeconomic factors or lifestyle choices. In many situations, even simple interventions may help to prolong healthy life if the reasons are correlated to, for example, unhealthy diet (malnutrition, hypercaloric diet, fat or protein excess in diet), environmental toxicants or not good enough health care (lack of early cancer or heart problems detection and diagnosis) (López-Otín et al., 2016; Sadana et al., 2016).

It is however still rather difficult to extend lifespan of people leading healthy lifestyle, having proper diet and monitoring their health condition. Nevertheless, there are some interesting interventions that work in model animals and might work for human and there are some ideas regarding implementing these findings to human interventions.

One of the approaches to anti-ageing interventions is proposed within strategies for engineered negligible senescence (SENS) and tries to address seven types of changes seen here as the most important in ageing: cell loss, cell death resistance, cell overproliferation, intracellular ‘junk’, extracellular ‘junk’, tissue stiffening, and mitochondrial defects. According to this strategy: cell loss could be minimized by stem cell therapies; cell death resistance by identifying and destruction of these cells with cytotoxic drugs or viruses; cell overproliferation by deletion of telomerase and ALT genes throughout the body; intracellular ‘junk’ by introducing novel enzymes capable of degrading the accumulated toxic molecules; extracellular ‘junk’ by vaccines enhancing immune response to ‘junk’ located in ECM; tissue stiffening by drugs or enzymes capable of cleaving the crosslinks responsible for this stiffening; and mitochondrial defects by introducing translocation-ready variants of the most important genes from mitochondrial DNA to nuclear genome (Zealley and de Grey, 2013). This approach has been, however, when it first appeared, criticized by various biogerontologists (Warner et al., 2005) mostly for being too speculative and too general in approach (not noticing various important details determining the possible success or failure of undertaken research) and for its too optimistic timeline regarding future successes in the field (without taking under consideration time needed from finding possible solution(s), through testing it in model animals, clinical trials, etc.). The main author of the approach answered to these allegations in his response (de Grey, 2005), but his answer didn’t convince everyone. Nowadays many scientists are involved in the projects led under SENS banner, but many still remain skeptical.

The other approach regarding slowing down ageing addresses changes according to nine hallmarks of ageing. One of the main feature of these hallmarks is that, if ameliorated they should retard ageing and extend healthy lifespan. And thus genomic instability and telomere attrition is said to be influenced with caloric restriction; epigenetic alterations with caloric restriction mimetics; loss of proteostasis and deregulated nutrient sensing with amino acid restriction; mitochondrial dysfunctions with metformin; cellular senescence and stem cell exhaustion with inhibition of trophic signaling; and altered intercellular communication with, for example, exercise (López-Otín et al., 2016).

Both of these approaches use very broad terms for classes of interventions and try to address not one problem regarding ageing body, but all of the most important problems (as defined by authors of those propositions). The tested interventions themselves in both approaches are however much more specific. Some interventions regarding these changes have already been proved to be working in model animals and other are being tested.

The interventions that might help in healthy ageing may be more general or more specific. The general interventions include dietary restriction (DR): caloric, protein and amino acid restriction or exercise, the more specific include usage of caloric restriction mimetics and other drugs (López-Otín et al., 2016). The most often tested general intervention is caloric restriction (CR), that is described as reduced calorie intake not causing malnutrition, which is commonly known to extend lifespans of model animals. There is still a debate regarding the mechanism of its influence on age-related changes, because it might impact plenty of processes (weight reduction, elimination of white adipose tissue, promotion of efficient quality control of organelles, inhibition of malignant transformation, etc.) and affect various pathways important in ageing, especially IIS pathway with such compounds involved like: mTOR, AMPK, AKT, SIRT1 and others (da Costa et al. 2016, López-Otín et al., 2016). Thus, CR is a good starting point for treating different compounds involved in pathways correlated to ageing, as potential anti-ageing targets to various inhibitors or activators.

Therapeutics and supplements extending lifespans of model animals connected to DR are, for example: rapamycin (mTOR inhibitor), metformin (activator of AMPK), resveratrol (activator for SIRT1 expression, NF-kB inhibitor), nicotinamide mononucleotide (increases NAD+ levels), acarbose, sperimidine, etc. (López-Otín et al., 2016).

And there is much more other treatments addressing various problems, like stem cell therapies, gene therapies, hormonal therapies, therapies focused on breaking AGEs (with usage of aminoguanidine, benfotiamine, aspirin, metformin, ALT-711), heterochronic parabiosis, transfusion of young plasma, various rapalogs, antioxidants, mitochondria-based therapies, senolytic agents (that induce apoptosis in senescent cells), SAMolytic agents (targeting senescent associated macrophages), etc. tested on different models with different success rate (da Costa et al. 2016; de Magalhães, Stevens & Thornton, 2017; Kaeberlein et al., 2015; Kirkland et al., 2017).

And it would be great if all of them were working for human as good as they are working for model animals, but they don’t. The problem with all these interventions is that because of differences between species and much more complex ageing in human, although most of them are working extraordinary for some model animals, they are not always working for humans as good. And the other problem is that even in animal trials there are often some side effects, like: with hormone therapies there are risks of alterations in metabolism or higher blood pressure; with telomere-based therapies that try to increase level of telomerase, there is risk of promoting tumor growth; with antioxidants there is also higher risk of acceleration cancer development, stem cell therapies still need to be tested on the tissue-specific level, etc. (da Costa et al., 2016; de Magalhães et al., 2017; Kaeberlein et al., 2015). This might be because, for example, various compounds that change metabolic pathways or nutrient sensing might have important role in more than one pathway (Efeyan, Comb & Sabatini, 2015).

In future, we must better understand these processes which tested procedures and therapeutics affect and all the procedures and therapeutics should be tested with accordance to genetics and epigenetics of specific cells and tissues that might influence the efficacy of the tested compounds. We also need proper biomarkers of ageing in these different tissues to assess efficacy of tissue-specific ageing treatments.

Future interventions should be created differently for children and adolescents (primary preventive services), young adults and adults (secondary preventive services), and older adults (tertiary preventive services and treatments) (Sadana et al., 2016).

We should also be addressing changes in different systems differently based on the specific changes done to the system and remember that in some situations, interventions on organ or system level might be more effective than interventions on cellular and tissue level.

So, before getting great therapeutics and procedures, there is still a lot to do and there is a question if we can speed up the process of getting new knowledge and new anti-ageing therapies.

PART III. HOW CAN WE SPEED UP THE PROGRESS IN AGEING RESEARCH

III.1. Materials and methods in ageing research.

III.1.a. Model organisms

Lots of ageing research is done on model organisms. There are several model organisms usually used in this research (Kim, Nam & Valenzano, 2016). There are: Saccharomyces cerevisae (yeast, unicellular eukaryote), Caenorhabditis elegans (transparent nematode), and Drosophila melanogaster (fruit fly) that can be grown very fast (all their typical life spans are less than 5 weeks), but are very simple organisms and thus are good mostly for first tests before testing more complicated organisms. There are also a bit more complex organisms that have been used - e.g. two types of fishes: Danio rerio (Zebrafish) and Nothobranchius furzeri (Turquoise killifish). Finally, there are, most similar to humans, but with relatively long life spans (and thus testing them takes a lot of time): Mus musculus (mice) and other mammals. Unfortunately not everything can be tested in these model organisms, because, for example, there are differences in genes, cellular life cycles, tissue specific processes, etc. between e.g. C. elegans, M. musculus and H. sapiens.

Besides from gaining knowledge based on testing one model organism, there is a lot to learn from comparative studies of different mammalian species (Croco, Marchionni & Lorenzini, 2016).

Different species may have completely different ways of controlling age-related changes like peculiar jellyfish Turritopsis dohrnii which can go back to polyp phase from adult stage (Lisenkova et al., 2017; Martel et al., 2016) or better ways to fight age-related diseases like Heterocephalus glaber (naked mole-rats) that is much better in it than other rodents (Delaney, 2017). From comparison of different species we may learn how fidelity of protein synthesis correlates with maximum lifespan and try to find different ways various species developed for less error-prone translation mechanism (Ke et al., 2017) or rethink cell differentiation (Alvarado and Yamanaka, 2014). and thus it could be fruitful to have more studies like this.

In order to get more precise understanding of the process whether it is in jellyfish, yeast or mice, we need new testing procedures that can help us collect more data regarding not only quantified features but also spatiotemporal ones. In order to get that data we need more tools that can help both in observation of existing processes and in experimental interventions. We should also remember about animal rights and try to do as much as possible in computer simulations, cell colonies, etc.

III.1.b. New procedures and interventions useful for ageing research

Generally, everything that is used in other fields of biology is already being used or can be used in the future in ageing research. New procedures and interventions that might be interesting for ageing research come from different fields and regard different levels of body organization.

On molecular level, there is for example Genome Project-Write with which scientists are trying to synthesize different genomes (Boeke et al., 2016), there are new techniques using CRISPR/Cas9, with which scientists are now able to remove whole chromosome or maybe even repair defected gene in embryo (Adikusuma et al., 2017; Egli et al., 2017, Ma et al., 2017; Scott and Zhang 2017) or new epigenetic therapeutics created to fight against cancer (Ahuja, Sharma & Baylin, 2016) or Alzheimer (Li, Bao & Wang, 2016) that can all be used in ageing research.

On subcellular level, there is for example new way of controlling phase transition in cells with light (Shin et al., 2016) that can be used to test new hypotheses in less invasive way. There are also new interesting techniques being developed that let us see the biological processes step by step with spatiotemporal control (Lobingier et al., 2017).

On cellular level, scientists are searching for new ways of selecting the target, for example – so important in ageing - senescent cells (Baar et al., 2017).

On organ level, new organs can be grown with CRISPR/Cas9 that give us new possibilities (Driehuis and Clevers, 2017) and we have new ways of testing hypotheses thanks to creating organs on chips and reverse engineering processes (Ingber, 2016).

There are also some new techniques of introducing different therapeutics to organism (to let them act longer (Liu et al., 2017) or to be more precise (Sun et al., 2017)) that can be used in future ageing research.

This is, of course, only a very small subset of new interesting techniques, but even that subset shows how different procedures can be useful in ageing research.

In order to move faster with research, we still need more techniques to speed up the process of gathering new data and deciding which interventions might be the best. The general database with interesting procedures and interventions could be very useful for researchers (especially new ones, but not only) in deciding about future research and techniques that might be the best suited for it. Also, some specialized programs could help in exploring all the gathered data.

III.1.c. Data analysis in ageing research

The way to analyze data from experiments regarding ageing is very important aspect of research process. In recent years this process has been getting more automated than before with the usage of Artificial Intelligence (AI). AI, generally, may help in various aspects of medical procedures (surgical robots), computer-aided diagnosis (e.g. predicting 'cancer patients' prognoses), understanding complicated models, predicting influence of some drugs on patients, etc. (Hamet and Tremblay, 2017; Musib et al., 2017). AI can be also used for creating graphs and incorporating data from databases, for literature mining, integration of knowledge (Kwon, Natori & Tanokura, 2017) and maybe even to create scientific discoveries (Kitano, 2016).

To be fully capable of all these, AI has to be equipped with machine learning (ML) algorithms (suited for supervised and unsupervised learning) that can infer from data. In general, according to Ravi et al. “machine learning in bioinformatics can be divided into three areas: prediction of biological processes, prevention of diseases and personalized treatment. These areas are found in genomics, pharmacogenomics and epigenomics.” (Ravi et al., 2017, p. 6) and all these areas appear in ageing research.

Lately, most interesting ML techniques are deep learning methods. These used in analysis of age-related changes (fed with different input data, like Microarray data, data regarding molecule compounds, MRI/CT Images, PET scans, X-ray images, EEG, ECG, etc.) are for example: deep autoencoders (DA) used to cancer diagnosis; deep belief networks (DBN) used to analysis of compound-protein interactions, anomaly detection, human activity recognition; simple deep neural networks (DNN) used to drug design, Alzheimer diagnosis, tumor detection; convolutional neural networks (CNN) used to neural cells classification, organ segmentation, human activity recognition; recurrent neural networks (RNN) used to human behaviour monitoring, etc. (Leung et al., 2016; Ravi et al., 2017).

Other interesting supervised learning algorithms, apart from deep learning methods, used in ageing research are: support vector machines, decision trees, random forests, kNN's, logistic regression, LASSO regression, etc.. The data they have been already used with, includes gene expression levels, protein-protein interactions, physiological biomarkers, methylome profiles, etc. and they have been used to classification of genes involved in ageing, prognosis of dementia (Alzheimer/MCI), DNA repair mechanisms, predicting chemical compounds that could extend healthy life, etc. (Barado et al., 2017; Dallora et al., 2017; Fabris, de Magalhães & Freitas, 2017).

All these techniques have their strengths but also limitations (training time, optimization for large datasets, etc.), so sometimes hybrid approaches or maybe even completely new ML techniques might be better.

There is still a lot of new programs needed for automation of various tasks that take a lot of time while done manually. There is also need for making usage of ML more “user friendly” for researchers with not much background in computer programming.

III.1.d. Databases and models regarding ageing

With all these experiments done on different model animals, analyzed by various AI systems or humans, there is a lot of data we’ve created and there will be more. In order to properly understand ageing in its nuances it would be fruitful to gather all the data from different experiments and create a systemic model of changes that happen during ageing in human organism. Different approaches are possible for creating general model and models regarding specific aspects of ageing phenomenon (like computational models of mitochondrial dynamics, telomere model, models of metabolic signaling, etc. (Mooney, Morgan & Mc Auley, 2016). There are already some attempts to connect different facts regarding genes, ageing and age-related diseases with GeroNet (Yang et al., 2016) or Digital Ageing Atlas (Craig et al., 2015), and to map age-related changes on different levels of organismal structure (Furber, 2017) that could be further developed.

The systemic approach could give us the map of all changes in different body parts, on different levels of organization and graph with described pathways of changes made by molecules to cells, cells to tissues, organs and systems. This kind of model could help develop new interventions against ageing.

In order to create this model, existing and future databases and atlases can be utilized.

On molecular level, there is already ChEMBL – database of bioactive molecules (Gaulton et al., 2017) that could be used; data from Human Genome Project (IHGSC, 2004), data from other projects focused on better differentiation of variations between human genomes of healthy and sick people, like: 1000Genomes, Exome Aggregation Consortium project, Human Genome Variation Archive (Lopez et al., 2017), data regarding human epigenome from Human Epigenome Atlas (Kundaje et al., 2015) and other (Leung et al., 2016). On subcellular level data could be incorporated from Human Protein Atlas (Thul et al., 2017), that helps in localizing precise distribution of more than 12.000 human proteins at a cell level and map with more than 100.000 protein-protein interactions (Fassenden, 2017). On cellular and tissue level data from future Human Cell Atlas and Human Tissue Atlas (Regev et al., 2017) can be introduced to the model. There is also data from Human Microbiome Project (Lloyd-Price 2017) that should be incorporated to the model. And of course different timelines of cells developments and replacements would have to be taken under consideration. There are also various models regarding metabolomics (Ghasemi, 2016), human interactome (Huttlin et al., 2017; Kotlyar et al., 2016) with special emphasis on differences within interactomes (Washburn, 2016), and data regarding age-related changes, diseases, genes and antiageing drugs from GeneAge (with more than 2000 genes that affect longevity in model organisms), CellAge, DrugAge (with more than 400 compounds that could increase longevity in model organisms) (de Magalhães et al., 2017; Human Ageing Genomic Resources, 2017; Zhang et al., 2016) and Digital Ageing Atlas (with more than 3000 molecular, physiological, pathological, and psychological changes) (Craig et al., 2015) that could be connected with other data in the model.

The full graph with all the changes is going to be very complex. It will be thus important to decide which are the most important aspects of ageing in order to build models that can be processed in a reasonable time and to improve techniques to analyze big data with regard to time and space.

In regard to models of ageing there are several ways, ML can be used, for example, CNN's can be used for analyzing certain parts of human body from sub-cellular level to cellular, tissue, and organ level and RNN's for time lapse simulations of the graph. ML can also be used to find the most important clusters of features connected to ageing processes and help focusing on them in created graphs and models.

In future, when there is much more data to analyze, we could also try using in ageing research new types of processors developed for quantum computation (Linke et al., 2017) and so already start preparing software dedicated to ageing research to work on these computers (Zeng et al., 2017) like quantum machine learning techniques that can be much better than non-quantum traditional ML techniques (Biamonte et al., 2017) and help us analyze such big models as potential general ageing models

III.2. Structure in ageing research

In order to make faster progress in the field of biogerontology and slowing down (or even reversing) ageing, we should make our research more structured, collaborative, and more computer-assisted.

Structure helps in learning and in future discoveries. If scientists can create better ways to structure their work and the data they gather (better than in articles like this one (e.g. databases, models)) it might help to increase the speed of developments regarding ageing, and make it easier to develop new interventions even without the help of powerful AI’s (but with AI’s help it can be much faster, so AI’s are much appreciated).

The structuring of coming research might be done in three aspects - structuring: the research, the data, and the teaching.

III.2.a. Structure of the research

Structuring the research on global level may help in couple ways. We could structure several things.

First of all, we could structure the questions there are to ask and try to describe aims for future research (as seen right now), like: should we and can we create one big project - much bigger than Human Genome Project or Human Cell Atlas? Could we and should we create testing maps or world-wide consortia that will take care of each gap in our knowledge and special programs that will provide the most promising hypotheses regarding fighting ageing? Can we be satisfied with imperfect solutions that will help improve health only a bit? Should we look for ways to address problems on the most basic level, or at higher levels, etc.

Second of all, we could decide which aims are most important and most interesting in shorter and longer distance for ageing research. This could include creating a strategy that would answer all the problems regarding ageing changes (we see right now) and decision about what to test first, because we don't have enough resources to test everything and create model of every change regarding ageing. There are trillions of changes that need to be tested and changes that need interventions. The question is - if there is a proper way of addressing them, so to help people after 75 years old and younger the same time? Can we choose the best aims for treating old adults, young adults and children?

Third of all, we could describe the needs regarding new techniques for data gathering and data analysis that could help in achieving chosen aims, like: we still need to gather more data regarding specific aspects of ageing. In order to do it, we need more precise spatiotemporal techniques of observations and experimental procedures to use on different levels of body organization. We need better methods for data analysis that will allow us to acquire more information from less data and with less computational power required. We also need new tools that will allow us analyze even more data that will come, like with quantum computation which is now going into next phase of translation from research to usage. We also need better ways to deal with data on human-computer interaction level, because it is already almost impossible for one scientist (or even a group in one lab) to read about all the compounds connected to ageing from different databases (like the DrugAge (already > 400) or GenAge (already > 2000)), so there is need for some kind of AI to help. This AI should be however created in the way, scientists would understand not only the outcome of its work, but the analysis that are done in the background as well, so to be aware why AI is proposing the specific compounds or interventions. After structuring these needs, biogerontologists could present it to scientists from other fields, like: physicists, computer scientists, chemists, mathematicians, etc. - so to start structured interdisciplinary collaboration.

Structuring the research means also structuring collaboration, which can be done with, for example, online meetings open to all the interested scientists with information about progress, ideas, problems to solve. These meetings could be a part of a bigger project including the site open to suggestions and links to private databases from scientists that could be gathered and clustered by AI, etc.. With this kind of collaboration we could make knowledge move much faster between scientists and it could help in creating better and bigger projects based on the newest advancements from various areas of research.

III.2.b. Structure of the data

There are already some initiatives that try to structure the data into databases and atlases. Now there might be also a need of structure to all these in spatiotemporal model. It might be done as one big model with backbone from data regarding ageing systems. All the other data regarding organs, tissues, and cells might be built on top of that (inside the systems). This model could include changes done to the organism with time. It should allow access on different levels of generality for scientists to create various research projects.

The data can be incorporated from all the already existing models, atlases, databases, and from newer research. In order to do it, proper AI should be created and standards for presenting data in AI-friendly way, new APIs, etc..

III.2.c. Structure of the teaching

Structuring the teaching involves teaching already working scientists who sometimes have to spend a lot of time to learn new useful techniques (especially regarding computer science) and teaching future scientists. Structuring the teaching requires structured data regarding all the techniques with easy to understand tutorials, teaching the teachers how to present all these data in a comprehensive and interesting way, etc.

CONCLUSIONS

(1) To summarize all this, it is still unclear to scientists whether we will be able to extend our lives past the line of 123 years of living or not (the length of life of Jeanne Calment - the oldest verified person - was 122 years and 164 days). It is however clear to a lot of scientists that we can extend healthy lifespan of the years we have to live.

(2) There is already a lot we know about ageing of different species and a lot we can do to change the pace of ageing of these species and one day we might be able to understand ageing of human organism completely on very specific levels that could help us create better anti-ageing treatments.

(3) For better and faster progress there is still a lot to do in matters of data gathering, data analysis, and translation of knowledge to application. In order to do it, we need to look at our ageing in more complex way and, if possible and needed, use the newest tools from various fields of science.

(4) We also need to structure the research process better for all the data coming in future years. There is need for structuring the research itself, structuring the data and structuring the teaching.

(5) These are only couple of important aspects regarding biology of ageing research that need to be resolved in order to help us speed up the progress of making our lives healthier for longer time (so we could hopefully benefit from them ourselves whether we are 19 or 91 right now). It can’t be said today that one of already existing interventions or all of them combined together might be that Holy Grail of ageing research, but with global collaboration we have much better chance of success in finding proper interventions.

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