Aging Chart: a community resource for rapid exploratory pathway analysis of age-related processes

Anti-Aging Drugs and the Related Signal Pathways

Aging is a multifactorial biological process involving chronic diseases that manifest from the molecular level to the systemic level. From its inception to 31 May 2022, this study searched the PubMed, Web of Science, EBSCO, and Cochrane library databases to identify relevant research from 15,983 articles. Multiple approaches have been employed to combat aging, such as dietary restriction (DR), exercise, exchanging circulating factors, gene therapy, and anti-aging drugs. Among them, anti-aging drugs are advantageous in their ease of adherence and wide prevalence. Despite a shared functional output of aging alleviation, the current anti-aging drugs target different signal pathways that frequently cross-talk with each other. At present, six important signal pathways were identified as being critical in the aging process, including pathways for the mechanistic target of rapamycin (mTOR), AMP-activated protein kinase (AMPK), nutrient signal pathway, silent information regulator factor 2-related enzyme 1 (SIRT1), regulation of telomere length and glycogen synthase kinase-3 (GSK-3), and energy metabolism. These signal pathways could be targeted by many anti-aging drugs, with the corresponding representatives of rapamycin, metformin, acarbose, nicotinamide adenine dinucleotide (NAD+), lithium, and nonsteroidal anti-inflammatory drugs (NSAIDs), respectively. This review summarized these important aging-related signal pathways and their representative targeting drugs in attempts to obtain insights into and promote the development of mechanism-based anti-aging strategies.

Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging

Artificial intelligence (AI) has emerged as a powerful approach for integrated analysis of the rapidly growing volume of multi-omics data, including many research and clinical tasks such as prediction of disease risk and identification of potential therapeutic targets. However, the potential for AI to facilitate the identification of factors contributing to human exceptional health and life span and their translation into novel interventions for enhancing health and life span has not yet been realized. As researchers on aging acquire large scale data both in human cohorts and model organisms, emerging opportunities exist for the application of AI approaches to untangle the complex physiologic process(es) that modulate health and life span. It is expected that efficient and novel data mining tools that could unravel molecular mechanisms and causal pathways associated with exceptional health and life span could accelerate the discovery of novel therapeutics for healthy aging. Keeping this in mind, the National Institute on Aging (NIA) convened an interdisciplinary workshop titled “Contributions of Artificial Intelligence to Research on Determinants and Modulation of Health Span and Life Span” in August 2018. The workshop involved experts in the fields of aging, comparative biology, cardiology, cancer, and computational science/AI who brainstormed ideas on how AI can be leveraged for the analyses of large-scale data sets from human epidemiological studies and animal/model organisms to close the current knowledge gaps in processes that drive exceptional life and health span. This report summarizes the discussions and recommendations from the workshop on future application of AI approaches to advance our understanding of human health and life span.

Effects of unpaired 1 gene overexpression on the lifespan of Drosophila melanogaster

Background

The JAK/STAT signaling pathway is involved in many aging-related cellular functions. However, effects of overexpression of genes controlling JAK/STAT signal transduction on longevity of model organisms have not been studied. Here we evaluate the effect of overexpression of the unpaired 1 (upd1) gene, which encodes an activating ligand for JAK/STAT pathway, on the lifespan of Drosophila melanogaster.

Results

Overexpression of upd1 in the intestine caused a pronounced shortening of the median lifespan by 54.1–18.9%, and the age of 90% mortality by 40.9–19.1% in males and females, respectively. In fat body and in nervous system of male flies, an induction of upd1 overexpression increased the age of 90% mortality and median lifespan, respectively. An increase in upd1 expression enhanced mRNA levels of the JAK/STAT target genes domeless and Socs36E.

Conclusions

Conditional overexpression of upd1 in different tissues of Drosophila imago induces pro-aging or pro-longevity effects in tissue-dependent manner. The effects of upd1 overexpression on lifespan are accompanied by the transcription activation of genes for the components of JAK/STAT pathway.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R² = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R² = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state

Populations in developed nations throughout the world are rapidly aging, and the search for geroprotectors, or anti-aging interventions, has never been more important. Yet while hundreds of geroprotectors have extended lifespan in animal models, none have yet been approved for widespread use in humans. GeroScope is a computational tool that can aid prediction of novel geroprotectors from existing human gene expression data. GeroScope maps expression differences between samples from young and old subjects to aging-related signaling pathways, then profiles pathway activation strength (PAS) for each condition. Known substances are then screened and ranked for those most likely to target differential pathways and mimic the young signalome. Here we used GeroScope and shortlisted ten substances, all of which have lifespan-extending effects in animal models, and tested 6 of them for geroprotective effects in senescent human fibroblast cultures. PD-98059, a highly selective MEK1 inhibitor, showed both life-prolonging and rejuvenating effects. Natural compounds like N-acetyl-L-cysteine, Myricetin and Epigallocatechin gallate also improved several senescence-associated properties and were further investigated with pathway analysis. This work not only highlights several potential geroprotectors for further study, but also serves as a proof-of-concept for GeroScope, Oncofinder and other PAS-based methods in streamlining drug prediction, repurposing and personalized medicine.

Towards natural mimetics of metformin and rapamycin

Aging is now at the forefront of major challenges faced globally, creating an immediate need for safe, widescale interventions to reduce the burden of chronic disease and extend human healthspan. Metformin and rapamycin are two FDA-approved mTOR inhibitors proposed for this purpose, exhibiting significant anti-cancer and anti-aging properties beyond their current clinical applications. However, each faces issues with approval for off-label, prophylactic use due to adverse effects. Here, we initiate an effort to identify nutraceuticals-safer, naturally-occurring compounds-that mimic the anti-aging effects of metformin and rapamycin without adverse effects. We applied several bioinformatic approaches and deep learning methods to the Library of Integrated Network-based Cellular Signatures (LINCS) dataset to map the gene- and pathway-level signatures of metformin and rapamycin and screen for matches among over 800 natural compounds. We then predicted the safety of each compound with an ensemble of deep neural network classifiers. The analysis revealed many novel candidate metformin and rapamycin mimetics, including allantoin and ginsenoside (metformin), epigallocatechin gallate and isoliquiritigenin (rapamycin), and withaferin A (both). Four relatively unexplored compounds also scored well with rapamycin. This work revealed promising candidates for future experimental validation while demonstrating the applications of powerful screening methods for this and similar endeavors.

Geroprotectors: A Unified Concept and Screening Approaches

Although the geroprotectors discovery is a new biomedicine trend and more than 200 compounds can slow aging and increase the lifespan of the model organism, there are still no geroprotectors on the market. The reasons may be partly related to the lack of a unified concept of geroprotector, accepted by the scientific community. Such concept as a system of criteria for geroprotector identification and classification can form a basis for an analytical model of anti-aging drugs, help to consolidate the efforts of various research initiatives in this area and compare their results. Here, we review the existing classification and characteristics of geroprotectors based on their effect on the survival of a group of individuals or pharmaceutics classes, according to the proposed mechanism of their geroprotective action or theories of aging. After discussing advantages and disadvantages of these approaches, we offer a new concept based on the maintenance of homeostatic capacity because aging can be considered as exponential shrinkage of homeostatic capacity leading to the onset of age-related diseases and death. Besides, we review the most promising current screening approaches to finding new geroprotectors. Establishing the classification of existing geroprotectors based on physiology and current understanding of the nature of aging is essential for putting the existing knowledge into a single system. This system could be useful to formulate standards for finding and creating new geroprotectors. Standardization, in turn, would allow easier comparison and combination of experimental data obtained by different research groups.

Deciphering hallmark processes of aging from interaction networks

BACKGROUND

Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death.

SCOPE OF REVIEW

The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors.

MAJOR CONCLUSIONS

Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging.

GENERAL SIGNIFICANCE

In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.