New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Abstract

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Keywords: Raman spectroscopy; aging; dynamical network biomarkers theory; resilience.

Figure 1

Schematic representation of aging trajectories and the DNB theory. (A) Representative loss of resilience along aging trajectories is drawn over the potential energy landscape. (i) Represents the homeostatic (young or healthy) state with fast and small fluctuations in the physiological parameter. (ii) Represents the allostatic state with slow and large fluctuations. (iii) Represents the allostatic overload (old or disease) state after the system crosses the tipping points and falls into a different state. This allostatic overload state is considered to be stable, and thus it is difficult to return to the original state. (B) The concept of the DNB theory and the pre-disease state. The DNB theory can detect early warning signals, which are sign of critical transition. The time-point just before the critical transition to the disease state (symptom onset) is named the pre-disease state.

Figure 2

A hypothetical diagram of pre-senescence cell identification using Raman spectroscopy and the DNB theory. Given that development of senescent cells is accompanied by the cell fate transition from the proliferative to the non-proliferative state, “pre-senescence state” may exist just before the critical transition to the senescence state. If that is a case, pre-senescent cells can be identified by the DNB theory and Raman spectroscopic observation. Furthermore, M-DNB model may help to identify the master regulator genes, which commit to this critical transition. These genes may serve as novel senotherapeutic targets, in addition to the current senotherapies, including senolytics and senomorphics.

Figure 3

Identification and verification of the DNB genes for metabolic syndrome. The DNB analysis is capable of identifying the cluster of the genes that show large fluctuations with the strongest correlations just before the critical transition. These genes were further analyzed by the control theory, then RNAi screening was performed using the fruit fly model.

Figure 4

The current applications of Raman spectroscopy and the DNB theory. Non-invasive and label-free imaging using Raman spectroscopy has been used to distinguish not only senescent cells, but also various cells and human diseases. The DNB theory has been successfully predicted the cell fate transition of several cell types as well as symptom onset. A combination of Raman spectroscopy and the DNB theory may help to predict the emergence of senescent cells and age-related loss of resilience.