Genetics of longevity

В настоящем обзоре установлена связь между эволюционноконсервативной группой генов, формирующих молекулярный осциллятор, ответственный за генерацию циркадных ритмов, и генетическими детерминантами процесса старения, а также ассоциированными с ними путями внутриклеточной сигнализации. Проведен анализ механизмов формирования возраст-зависимых патологий с позиций циркадной генетики. Систематизированы данные исследований профилей экспрессии циркадных генов животных, обнаруживающих различные темпы старения, от ускоренного до пренебрежимого.

The review establishes a link between a group of genes which are conserved in evolution and form a molecular oscillator responsible for generation of circadian rhythms and genetic determinants of aging including associated pathways of intracellular signaling. An analysis of mechanisms of development of age dependent pathologies is conducted from the viewpoint of circadian genetics. Systematic data of circadian gene expression studies in animals demonstrating different rates of aging from accelerated to negligible are presented.

Aging is currently stimulating intense interest of both researchers and the general public. In developed countries, the average life expectancy has increased by roughly 30 years within the last century, and human senescence has been delayed by around a decade. Although aging is arguably the most familiar aspect of human biology, its proximate and ultimate causes have not been elucidated fully and understood yet. Nowadays there are two main approaches to the ultimate causes of aging. These are deterministic and stochastic models. The proximate theories constitute a distinct group of explanations. They focus on mechanistic causes of aging. In this view, there is no reason to believe that there is only one biological mechanism responsible for aging. The aging process is highly complex and results from an accumulation of random molecular damage. Currently, the disposable soma theory (DST), proposed by Thomas Kirkwood, is the most influential and coherent line of reasoning in biogerontology. This model does not postulate any particular mechanism underpinning somatic defense. Therefore, it is compatible with various models, including mechanistic and evolutionary explanations. Recently, however, an interesting theory of hyperfunction of mTOR as a more direct cause of aging has been formulated by Mikhail Blagosklonny, offering an entirely different approach to numerous problems and paradoxes in current biogerontology. In this view, aging is quasi-programmed, which means that it is an aimless continuation of developmental growth. This mTOR-centric model allows the prediction of completely new relationships. The aim of this article is to present and compare the views of both parties in the dispute, based on the results of some recent experimental studies, and the contemporary knowledge of selected major aspects of human aging and longevity.

Many diets and nutritional advice are circulating, often based on short-or medium-term clinical trials and primary outcomes, like changes in LDL cholesterol or weight. It remains difficult to assess which dietary interventions can be effective in the long term to reduce the risk of aging-related disease and increase the (healthy) lifespan. At the same time, the scientific discipline that studies the aging process has identified some important nutrient-sensing pathways that modulate the aging process, such as the mTOR and the insulin/insulin-like growth factor signaling pathway. A thorough understanding of the aging process can help assessing the efficacy of dietary interventions aimed at reducing the risk of aging-related diseases. To come to these insights, a synthesis of biogerontological, nutritional, and medical knowledge is needed, which can be framed in a new discipline called 'nutrigerontology'.

Metallothionein (MT) is a low molecular weight protein with anti-apoptotic properties that has been demonstrated to scavenge free radicals in vitro. MT has not been extensively investigated within the context of aging biology. The purpose of this review, therefore, is to discuss findings on MT that are relevant to basic aging mechanisms and to draw attention to the possible role of MT in pro-longevity interventions. MT is one of just a handful of proteins that, when overexpressed, has been demonstrated to increase mouse lifespan. MT also protects against development of obesity in mice provided a high fat diet as well as diet-induced oxidative stress damage. Abundance of MT is responsive to caloric restriction (CR) and inhibition of the insulin/insulin-like signaling (IIS) pathway, and elevated MT gene expression has been observed in tissues from fasted and CR-fed mice, long-lived dwarf mice, worms maintained under CR conditions, and long-lived daf-2 mutant worms. The dysregulation of MT in these systems is likely to have tissue-specific effects on aging outcomes. Further investigation will therefore be needed to understand how MT contributes to the response of invertebrates and mice to CR and the endocrine mutations studied by aging researchers.

Survivorship experiments play a central role in aging research and are performed to evaluate whether interventions alter the rate of aging and increase lifespan. The accelerated failure time (AFT) model is seldom used to analyze survivorship data, but offers a potentially useful statistical approach that is based upon the survival curve rather than the hazard function. In this study, AFT models were used to analyze data from 16 survivorship experiments that evaluated the effects of one or more genetic manipulations on mouse lifespan. Most genetic manipulations were found to have a multiplicative effect on survivorship that is independent of age and well-characterized by the AFT model ''deceleration factor ". AFT model deceleration factors also provided a more intuitive measure of treatment effect than the hazard ratio, and were robust to departures from modeling assumptions. Age-dependent treatment effects, when present , were investigated using quantile regression modeling. These results provide an informative and quantitative summary of survivorship data associated with currently known long-lived mouse models. In addition, from the standpoint of aging research, these statistical approaches have appealing properties and provide valuable tools for the analysis of survivorship data.

Longevity and rejuvenation therapies are becoming a reality, yet we continue to obviate them as viable options. In this article, I explore some common misconceptions about the aims and limits of life-extension research, and the necessity to reconsider it as a worthwhile pursuit which could extend our lifespan indefinitely in the coming years.

Aging is currently stimulating intense interest of both researchers and the general public. In developed countries, the average life expectancy has increased by roughly 30 years within the last century, and human senescence has been delayed by around a decade. Although aging is arguably the most familiar aspect of human biology, its proximate and ultimate causes have not been elucidated fully and understood yet. Nowadays there are two main approaches to the ultimate causes of aging. These are deterministic and stochastic models. The proximate theories constitute a distinct group of explanations. They focus on mechanistic causes of aging. In this view, there is no reason to believe that there is only one biological mechanism responsible for aging. The aging process is highly complex and results from an accumulation of random molecular damage. Currently, the disposable soma theory (DST), proposed by Thomas Kirkwood, is the most influential and coherent line of reasoning in biogerontol...

Heat shock proteins (HSPs) maintain proteostasis and may protect against age-associated pathology caused by protein malfolding. In Caenorhabditis elegans, the lifespan extension and thermotolerance in
mutants with impaired insulin/IGF signals depend partly on HSP elevation. Less is known about the role of HSPs in the increased lifespan of mice with defects in GH/IGF-I pathways. Wemeasured HSPmRNAs in
liver, kidney, heart, lung, muscle and cerebral cortex from long-lived Pit1(dw/dw) Snell dwarf mice. We found many significant differences in HSP mRNA levels between dwarf and control mice, but these effects were complex and organ-specific. We noted 15 instances where HSP mRNAs were lower in Pit1(dw/dw) liver, kidney, or heart tissues, and 14/15 of these were also seen in Ghr(-/-) mice, which lack GH receptor. In contrast, of 12 examples where HSP mRNAs were higher in Snell liver, kidney, or heart, none were altered in Ghr(-/-) mice. Four liver mRNAs were depressed in both Pit1(dw/dw) and Ghr(-/-) mice, and each of these was elevated by GH injection in Ames (Prop1(df/df)) dwarf mice, consistent with the hypothesis that these declines depended on GH and/or IGF-I. Contributions of chaperones to longevity in mice may be more complex than those inferred from C. elegans.

Would this method for uncovering hidden objects, feature discovery, feature selection and supervised learning work? According to my understanding perfect prediction by computation optimization must be impossible as long as essential input training data features for supervised learning are still lacking. Therefore, I emphasize not to invest much effort into algorithmic computational optimization before the feature discovery process allows selecting all needed features through variation before realistic predictions can be achieved by enhancing computations and parameters. For me this simple progression from completed feature selection to optimized algorithm is essential for more rapid discoveries. That is why I am still wondering why I have seen so many papers focusing on improving computational predictions without any kind of prior considerations about having properly completed the absolutely essential exhaustive feature selection process without which no subsequent computations can lead to satisfactorily predictions, which are reasonably consistent with our experimental observations and measurements. I am afraid that I am still the only one, to whom the writing above can make sense. I had expected much more enthusiasm, excitement and optimism about very likely accelerating our discovery rate by first focusing on uncovering still hidden objects and features through diverse variations of conditions, procedures, methods, techniques and measurements, followed by the exhaustive selection for all relevant needed features, followed by designing, developing, combining and optimizing the computational steps of our machine learning algorithm until our predictions match our experimentally obtained observations. Once this major machine learning objective has been achieved we have reached its final status beyond which we cannot improve it unless we can generate conditions, which cause our previously perfectly predicting machine learning algorithm to obviously fails because it is an absolute prerequisite for discovering more relevant training reflected by more dimensions. According to my current understanding any newly discovered essential input data feature inevitably causes a rise in the dimensionality of input data components, which must be considered together but never in isolation. For example, if I train with 100 input features our input variable must consist of exactly 100 components or parts, which together form a new level of single measuring points, which tends to be much different in controlling its manipulations and their overall effects from anything, which could possibly get anticipated, when trying to add up the effects of its 100 parts to a new total. This new total tends to consist of much different dimensions and often refers to completely unrelated kind of data than when combining all 100 components consisting of exactly the same input values for every of its 100 variables but by considering all 100 components like a single indivisible unit of measurement points, which often results in completely different kinds of unrelated seeming properties/features, which are not even closely reflecting the results, which we'd obtain if we executed each dimension on its own in isolation and sequential order. For example, stopping translation prematurely by three consecutive tryptophans has a much different impact, i.e. stop, then when translating each of the tryptophan in isolation separated by other amino acids, since this causes the nascent polypeptide chain to grow. Each tRNA charged with tryptophan, which complements mRNA triplets, causes the peptide to grow by a single tryptophan, which gets added to it. So when you try 2 tryptophans the polypeptide grows by two amino acids. But when you try 3 consecutive tryptophans, then-counter-intuitively-instead of the expected growth by 3 amino acids-translation prematurely stops. Stopping translation prematurely is of a much different dimension, level, effect and data kind, then when keep adding more amino acids to the growing peptide chain. If we consider the effect of complementary binding of a tRNA to its mRNA codon our peptide grows by one amino acid any charged tRNA adds another amino acid of one of 20 categorical values. Normally no amino acid can cause the translation to step prematurely, not even two amino acids as a pair. But three amino acids as an indivisible triplet, which must be considered as a new single value requiring all three tryptophans to be sequentially to be present, like a single indivisible data block, which must not be divided only in triplets, because only triplets, but no pair or singlet, can stop translation prematurely. Another example is predicting overall cellular protein composition. It depends on how many mRNA strands coding for a particular protein are in the cytoplasm. There is proportionality between number of cytoplasmic mRNA strands and total protein. Therefore, if the cell needs to double protein abundance it could double transcription and keeps everything else the same. But a much better and less step intensive, more economic way of doubling protein concentration is to double the length of the poly-(A)-tail. Extending the length of the poly-(A)-mRNA-tail may require about 100 additional adenines whereas doubling transcription requires about at least 500-instead of only 100-new nucleotides in addition to all needed transcriptional modification steps with their elaborate synthesis machinery. If the dividing yeast must raise its lipid synthesis by more than 10-fold during the short M-phase, it could increase transcription by a factor of 10, it could make the poly-(A)-mRNA-tail 10 times longer, or it could synthesized 10 times more new ribosomes to increase the ribosomal translation by a factor of 10 simply by reducing the distance of free uncovered mRNA nucleotides between adjacent ribosomes translating running down the same mRNA strand. If more than one ribosome is translating the same mRNA strand simultaneously, it is called a poly-ribosome. Hence, having 10 times more ribosomes binding to the same mRNA strand at the same time increases translation by a factor of 10 without needing any additional transcription. Above I have given three easy examples to get 10 times more proteins. Although all 3 methods have the same final result, i.e. 10 times more proteins, their mode of action, their required essential features, their dimensions and their minimally required parts, which must be considered like a single value, are totally different.

Heat shock proteins (HSPs) have proven to be effective tools for extending invertebrate lifespan, and in C. elegans daf‐2 mutants, longevity resulting from loss of insulin / insulin‐like signals is at least partly dependent upon elevated HSP expression. In mice, inhibition of the orthologous growth hormone / insulin‐like growth factor I (GH / IGF‐I) pathway has similar pro‐longevity effects. A recent study, however, suggests that loss of GH / IGF‐I signals in long‐lived mice does not broadly elevate HSP expression, but in fact decreases HSP expression in many tissue types, such as liver and kidney. The contribution of chaperones to the longevity of long‐lived mice with altered GH / IGF‐I signals may therefore differ from that described in C. elegans daf‐2 mutants. This result, in combination with other recent findings, underscores the possibility that systemic overexpression of chaperones will have dissimilar effects on longevity in vertebrate and invertebrate systems.

The E(z) histone methyltransferase heterozygous mutation in Drosophila is known to increase lifespan and stress resistance. However, the longevity mechanisms of E(z) mutants have not been revealed. Using genome-wide transcriptome analysis, we demonstrated that lifespan extension, increase of resistance to hyperthermia, oxidative stress and endoplasmic reticulum stress, and fecundity enhancement in E(z) heterozygous mutants are accompanied by changes in the expression level of 239 genes (p < 0.05). Our results demonstrated sex-specific effects of E(z) mutation on gene expression, which, however, did not lead to differences in lifespan extension in both sexes. We observed that a mutation in an E(z) gene leads to perturbations in gene expression, most of which participates in metabolism, such as Carbohydrate metabolism, Lipid metabolism, Drug metabolism, Nucleotide metabolism. Age-dependent changes in the expression of genes involved in pathways related to immune response, cell cycle, and ribosome biogenesis were found. Gene expression fluctuates in different tissues and the chromatin landscape changes during aging 1. Site-specific repression of the genome is known to be observed in aged individuals, as well as multifocal (or global) derepres-sion, the latter process dominates 2,3. Gene expression is usually influenced by the chromatin state, but the tran-scriptional profile also may influence epigenome, especially when sequences regulating chromatin-remodeling complexes are affected 4,5. The lowering of age-related gene expression profiles is known to be associated with heterochromatin formation and histone methylation. This statement is true for histone H3 lysine 9 (H3K9) and histone H3 lysine 27 (H3K27) loci in Drosophila and is connected to the pool of histone methyltransferases 3. The H3K9 age-related loss results in the Heterochromatin Protein 1 (HP1) concentration lowering, this phenomenon is strongly associated with heterochromatin decompactization and genomic instability 6. In addition, the epigenome-wide loss of H3K9 methylation has been observed in cell culture modeling Hutchinson-Gilford progeria syndrome and also in aged Drosophila melanogaster, as an exception, several chromatin islands are hypermethylated in both cases 3,6,7. Histone H3 lysine 9 methylation is carried out by dSETDB1, Su(var)3-9, G9a, and E(z) 8,9. The H3K27 meth-ylation levels are associated mainly with E(z) and ESC subunits of polycomb repressive complex 2 (PRC2). Wild type E(z) (Enhancer of zeste) is a gene coding protein that belongs to the class V-like SAM-binding methyltrans-ferase superfamily, histone-lysine methyltransferase family, and EZ subfamily. E(z) is also known to have transcription factor activity 5,10. The loss-of-function mutations in PRC2 components extend the lifespan 11 and take part in transgenera-tional inheritance mechanism detected for this effect, as well as the antagonistic enzyme UTX-1 demethylat-ing H3K27me3 12. PRC2, and especially its subunit E(z), are known as suppressors of stress-response genes (e.g. Odc1). This complex is working in an ensemble with trithorax (TRX), which has the opposite function 11. In the present work, we aim to unravel specific mechanisms which cause life extension, fecundity augmentation, and 1 engelhardt institute

A theoretical model of a nonlinear network that outlines the general aspects of mole-rat resistance to age-related diseases, such as cancer and the action of ROS was elaborated. According to our conjecture, it was shown that the protection is established because hyaluronic acid of high molecular mass forms a non-linear network of interactions. That network leads to self-organization away from the thermodynamical equilibrium, which appears through a " first order " phase transition as a supercritical bifurca-tion of Andronov-Hopf type. Finally, it is shown how the rate of entropy production is a Lyapunov function of the dynamics of the process.