Role of Dynamical Network Biomarkers Theory in Aging

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Kazutaka Akagi	--	2998	2023-09-25 07:08:37	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	2998	2023-09-27 02:49:11	\|

This entry is adapted from the peer-reviewed paper 10.3390/cells12182297

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. Given that the appearance of senescent cells is considered to be a cell fate transition from the proliferative state to the non-proliferative state, similar to the critical transitions that occur during cell differentiation and symptom onset, it can be detectable by the dynamical network biomarkers (DNB) theory, which detects early warning signals just before bifurcation points, such as “the pre-disease state”.

dynamical network biomarkers theory aging DNB theory

1. Introduction

Aging is characterized by a progressive loss of physiological integrity, which leads to tissue dysfunction and an increased vulnerability to death. Physiological integrity, (i.e., the intrinsic capacity or resilience of a body system) is proposed as a stronger predictor than the presence of morbidities of subsequent deteriorations in health status ^[1]. Younger individuals have a robust recovering capacity, termed “resilience”, against iterated insults and stress. Resilience declines with age, contributing to the emergence of diseases that manifest clinically ^[2]. This concept of age-related loss of resilience is correlated with the numerous discoveries and recent advances in the field of basic aging research. The hallmarks of aging, which include genomic instability, telomere attrition, epigenetic alterations, a loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis, have been identified as key drivers of aging ^[3]^[4]. These hallmarks are tightly connected to chronic diseases, but not to driving aging independently; rather, they are highly intertwined, and understanding the interplay between them is critically important ^[5]^[6].

Among these hallmarks, research on cellular senescence has shown remarkable progress in understanding the mechanisms of aging, as well as drug development. Therefore, the prediction, identification, characterization, and pharmacological elimination of senescent cells have gained significant attention, not only by researchers in the aging field, but also by the pharmaceutical industries. Cellular senescence is defined as the irreversible arrest of cell proliferation that occurs when cells are exposed to a variety of stressors, such as genotoxic agents, nutrient deprivation, hypoxia, mitochondrial dysfunction, or oncogene activation ^[7]^[8]. Although there are several common markers of senescent cells, including the induction of the senescence-associated secretory phenotype (SASP), γ-H2AX nuclear foci, phosphorylated p53, the presence of cyclin-dependent-kinase (CDK) inhibitors (p16 and p21), and senescence-associated β-galactosidase (SA-βgal) activity, as well as an increased cell size, all of which are widely used and studied in research, the lack of universal or specific markers is a major limitation for the identification and targeting of senescent cells in vitro and in vivo ^[9]. Furthermore, the phenotype of cellular senescence is highly heterogeneous and dynamic, and thus it is difficult to distinguish between the non-senescence state and the senescence state. Emerging technologies in the fields of multi-omics, imaging, bioinformatics, and machine learning have enabled the more precise identification of senescent cells ^[10]. This technological revolution gives us novel insights into understanding cellular senescence and organismal aging, but there are still some limitations and challenges ^[10].

2. Dynamical Network Biomarkers Theory

2.1. The Concept of DNB Theory

In view of the bifurcation theory of dynamical systems, when organisms have a high resilience in a homeostatic state (e.g., physiologically young or healthy), a body system is stable, with fast and small fluctuations in terms of the value of a physiological parameter or gene expression. During aging with a gradual loss of resilience, on the other hand, the system enters an allostatic state ^[11], which leads to a dynamic change in the parameter that shows slow and large fluctuations. Then, the system falls into a different state via bifurcation, which is also known as the “critical transition”, and the thresholds at which this transition occurs called “tipping points” ^[12]^[13]. This other state corresponds to the disease state or the state of allostatic overload (discussed below), indicating the breakdown of a physiological network. This phenomenon is a consequence of the accumulation of undetectable elements, and once it occurs, it is often too late and non-reversible, like diabetes. Accordingly, methodology through which scholars can identify a sign of this critical transition or the state just before the critical transition, namely “the pre-disease state”, is needed for disease prediction. It is noteworthy that the concept of the pre-disease state was first proposed in the world’s oldest medical textbook, “Yellow Emperor’s Inner Canon”, in China more than 2200 years ago. In this textbook, the pre-disease state is termed “Weibing” in Chinese, which corresponds to “Mebyo”, meaning “not sick yet” in Japanese.

These critical transitions are a sudden and large-scale change in state and occur in many complex systems, including ecosystems, the climate, financial markets, and microorganism populations ^[14]^[15]^[16]^[17]^[18]. The most important indicator of whether a system is getting close to the critical transition is a phenomenon known in dynamical systems theory as “critical slowing down” ^[12]^[19]. Critical slowing down exhibits increases in both fluctuation and autocorrelation, as well as a slow recovery from perturbation just before the critical transition.

In general, gene expression is a fundamentally stochastic process, which stems noise from randomness in mRNA synthesis ^[20]. Stochastic gene expression results in a heterogeneous cell population that has beneficial effects in some contexts. In aging, however, noise in gene expression increases with age, and it may mask the expression changes in the genes that, in particular, lead to the critical transition towards the disease state. Notably, a data analysis using the DNB theory (namely a DNB analysis) can extract the cluster of the genes that shows large fluctuations with the strongest correlations. Thus, DNB analysis is applicable to noisy and nonlinear datasets, including multi-omics data.

2.2. The Applications of DNB Theory

The DNB theory has been applied to various disease models, from cellular to organismal levels (Table 1). These include predictions of viral infection such as influenza A (H3N2) and COVID-19 ^[21]^[22]^[23], hepatocellular carcinoma metastasis ^[24], drug resistance in breast cancer ^[25], photodamage responses in skin ^[26], and adeno-to-squamous transdifferentiation in lung cancer ^[27].

Table 1. The applications of DNB theory to diseases and development.

Models	Cell Types or Species	Datasets	References
Influenza A (H3N2) infection	Human	Microarray of the blood samples	^[21]^[23]
COVID-19 infection	Human	Case reports in five different countries and regions	^[22]
Hepatocellular carcinoma	Xenograft mouse model of HCCLM3 cells	Microarray of the liver samples	^[24]
Breast cancer	Human breast adenocarcinoma MCF-7 cell line	RNA-seq of MCF-7 cells	^[25]
Skin photodamage	The LSE model (3D skin model consisting of normal human keratinocyte and melanocyte)	RNA-seq of the LSE model	^[26]
Lung cancer	Kras^LSL-G12D/+; Lkb1^flox/flox (KL) mice	RNA-seq of the KL lung samples	^[27]
Hematopoietic stem cell differentiation	Mouse hematopoietic stem cells (mHSCs)	scRNA-seq of mHSCs	^[28]
Embryonic stem cell differentiation	Human embryonic stem cells (hESCs)	scRNA-seq of hESCs	^[29]
Immune cell differentiation	T cells from DO11.10 TCR mice	Raman imaging	^[30]
Metabolic syndrome	Metabolic syndrome model mouse (TSOD mice)	Microarray of the adipose tissues	^[31]^[32]
Type 2 diabetes	Diabetes model rat (GK rats)	Microarray of the adipose tissues	^[33]

2.3. Cancer and Cellular Senescence

Cellular senescence is one of the first defense mechanisms against tumor promotion during carcinogenesis. This process is known as oncogene-induced senescence (OIS), which suppress the pro-proliferative effects of oncogenic stimuli by forcing cells to become senescent and prevent the expansion of pre-cancerous cells ^[34]. Senescence is also induced by chemotherapeutic drugs or ionizing radiation during cancer treatment, termed therapy-induced senescence (TIS) ^[35]^[36]. Generally, the mechanisms underlying TIS are connected to the DNA damage response (DDR) that leads to blocking tumor cell proliferation. However, it was found that some senescent tumor cells can escape from cell cycle arrest and acquire stemness properties with a highly aggressive growth potential, which contradicts the dogma of the irreversible arrest of the cell proliferation phenotype in senescent cells ^[35]^[36]^[37]^[38]^[39]. This phenomenon is possibly characterized as “survival at the brink”, which is associated with tumor recovery and cancer relapse ^[39]. During this process, it may display large fluctuations in gene expression with strong correlations, which can be detected using the DNB theory. Notably, Jackson et al. and Huna et al. reported that the dual and heterogeneous up-regulation of two opposing regulators, p21CIP1 for senescence/apoptosis and OCT4A for stemness in the topoisomerase II inhibitor, Etoposide, induced senescent embryonal carcinoma cells ^[40]^[41], suggesting early warning signals of a cell-fate transition towards tumor recovery.

3. Senolytics and Senomorphics

The emerging therapeutic strategies for targeting senescent cells are called senotherapies, which include senolytics and senomorphics. Senolytics is the selective elimination of senescent cells by small molecules, while senomorphics is the inhibition of pathological SASPs to cause senostasis (senescent cells stay there but are less harmful) ^[42]. The majority of the senolytics identified to date promote the apoptosis of senescent cells by targeting the key enzymes involved in cellular pro-survival and anti-apoptotic mechanisms, such as SRC kinases, BCL-2 family proteins, HSP90, PI3K-AKT, p53-FOXO4, GLS1, and others.

Senomorphics, on the other hand, is considered to be a safer alternative to senolytics, as it suppresses the unwanted SASP expressions from senescent cells rather than directly removing them. Senomorphics can directly or indirectly attenuate the SASP of senescent cells by inhibiting mTOR, NF-κB, SIRT1, p38MAPK, JAK-STAT, and other signaling pathways. The safety concern associated with senomorphics is the potential suppression of the growth-promoting functions induced by the SASP, similar to those seen in senolytics.

The side effects of senotherapies are due to the high heterogeneity in gene expression and the diverse origins of senescent cells, as well as their beneficial effects on tissue repair and regeneration ^[43]^[44]. An elimination of all senescent cells or a general inhibition of the SASP might cause the detrimental effects; thus, developing universal senotherapeutic drugs is extremely challenging. Further studies are needed to understand the manifestation of senescent cells for the successful development of senotherapeutic interventions. Notably, an M-DNB analysis identified the tipping points of hESC differentiation and found the master regulator genes, which commit to its cell fate determination ^[29]. Thus, scholars speculate that an M-DNB analysis may help in finding the master regulators of senescent cell development at the “pre-senescence state”, and these genes might have the potential to be novel senotherapeutic targets. If scholars could intervene in “pre-senescence cells”, it may be possible to reverse the pre-senescence to the healthy state, preventing senescent cell burden and chronic diseases, as well as delaying multimorbidity and increasing health span.

4. DNB Analysis in Metabolism

4.1. Identification of DNB Genes

A DNB analysis is also possible to apply to metabolic diseases, including metabolic syndrome and type 2 diabetes ^[31]^[33]. Scholars investigated a mouse model of metabolic syndrome, Tsumura Suzuki Obesity Diabetes (TSOD) mice, which are an inbred strain that spontaneously display metabolic syndrome phenotypes that correspond to phenotypes in humans ^[45]. The TSOD mice sequentially displayed phenotypes including obesity, hyperglycemia, dyslipidemia, hyperinsulinemia, and diabetes starting at around 12 weeks of age. They showed non-alcoholic steatohepatitis (NASH) at around 24 weeks of age and hepatocellular carcinoma at around 48 weeks of age. Scholras performed a microarray analysis using white adipose tissue from the TSOD mice, collected at pre-symptomatic stages from 3 to 7 weeks of age. Analyzing this dataset using a DNB analysis, authors found a group of collectively fluctuated genes with a significant correlation in strength (namely DNB genes) at 5 weeks of age, that scholars refer to as the pre-disease state in this mouse model ^[31]. In the study, the authors obtained 147 DNB genes, which are mostly associated with reproduction such as spermatogenesis and spermatid development. It is noteworthy that testosterone deficiency is associated with metabolic syndrome ^[46]. The results suggested that a DNB analysis can capture fluctuations in gene expression towards the development of disease at ultra-early time point and the genes which are mainly expressed in the testes may be involved in triggering metabolic syndrome in adipose tissue.

4.2. Verification of DNB Genes Using a Drosophila Model

In a previous study, authors identified 147 DNB genes from the white adipose tissue of the TSOD mice and determined the pre-disease state in this mouse model ^[31]. Given that the pre-disease state in each disease can be determined using the DNB theory, it is necessary to determine the means by which authors can reverse the physiological state from the pre-disease to healthy state, in order to make this approach clinically available in the near future. Therefore, authors are developing a method called DNB intervention using the control theory, especially the sample covariance matrix, which provides a list of the genes to be targeted, based on the simulation results of gene manipulations (either knockdown or activation). Consequently, authors specified 18 genes using a DNB intervention approach from the original microarray dataset, which did not have known functions relating to metabolism. To evaluate whether these genes played a role in metabolism, scholars took advantage of the fruit fly model system due to its time- and cost-effectiveness compared to mouse models. The authors performed RNAi screening using a fat body-specific (equivalent to the adipose tissue and liver in mammals) knockdown of fly orthologs of the candidate genes to observe their resistance to starvation and found several hit genes (Akagi et al., in preparation) (Figure 1). Moreover, the authors confirmed that the expressions of the mouse and human genes corresponding to the fly orthologs were slightly changed in response to high-fat diet feeding in mice or human subjects with a high body mass index, from a database analysis ^[47]. These slight changes in expression are often disregarded or impossible to detect by using the current average-based detection of the static molecular biomarkers. Using the DNB theory, however, authors can capture those “ignored genes” with a significant biological meaning. These approaches will be the model case for exploiting DNB analyses in research on aging and age-related diseases.

Figure 1. 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.

5. Homeostasis and Allostasis in Aging

Homeostasis is defined as the process that maintains a biological system in a steady state (set-point) against multiple stressors ^[48]. Allostasis, on the other hand, is the ability to achieve stability through a dynamic change in response to various stresses ^[11]. In the concept of allostasis, there is no fixed set-point parameter; rather, it is variable, the value of which results from iterative chronic stressors during aging. This kind of long-term effect of the physiologic response to stress is termed as “allostatic load” ^[49]. Hence, the allostatic load concept gives us an early phenotype step toward the development of diagnosable disease.

As aging is constructed by complex and dynamic changes, Kemoun et al. proposed the concept of “homeo-allostatic aging” ^[50]. When an individual is robust and homeostasis is sustained (i.e., physiologically young), the individual can respond adequately to challenges (multiple stress) and the set-point (a physiological parameter) rapidly returns to its initial value. This is because the individual has enough intrinsic capacities and resilience, which is defined as the ability of the organism to respond to stress ^[2]. It has been shown that age-related loss of resilience is associated with the fragility of a body system. For example, the brain exhibits impaired adaptive neuroplasticity and resilience during aging, but feeding and exercise regimens result in intermittent metabolic switching from liver-derived glucose to fat-derived ketones, which facilitates this switch ^[51]^[52]. During aging, as soon as the set-point not only decreases slowly, but also does not return to its initial value after being challenged, one falls into allostasis. This is a consequence of decreases in intrinsic capacities and resilience ^[50]. Therefore, understanding the nature of resilience mechanisms and accumulated damage, as well as finding methods for assessing them, is critical to uncovering the underlying biological mechanisms of aging and longitudinal changes in aging trajectories ^[53]. In a mouse model, Chen et al. recently developed a comprehensive platform for scoring physiological aging and resilience using a multi-dimensional longitudinal phenotyping approach ^[54]. This platform is capable of longitudinally evaluating aging in hundreds of mice across an age range from 3 months to 3.4 years. Using the multi-dimensional data they obtained, they developed a new method for determining biological age called the Combined Age and Survival Prediction of Aging Rate (CASPAR) model, which is trained to simultaneously predict both chronological age and survival time ^[54]. This approach will provide novel insights into disentangling biological aging from chronological aging. Importantly, concepts arising from a dynamical systems perspective have been applied to quantify or assess the resilience in physiological systems ^[55]^[56]. Thus, scholars believe that the DNB theory will help to understand the mechanisms of the resilience of a body system.

6. Conclusions

The spatial mapping of senescent cells provides critical information about their communication networks with neighboring cells as a driver of aging, but there are still some technical limitations due to the size of senescent cells, the long duration of imaging acquisition, and sample preparation and preservation ^[10]. In contrast, the accumulation of senescent cells in tissues may be estimated by the specific patterns of circulating proteins in the blood ^[57]. Notably, all of the hallmarks of aging directly or indirectly cause an inflammatory state, also known as “inflammaging”, suggesting that the pro-inflammatory state observed in many older persons may reflect the burden of biological aging ^[6]^[53]^[58]^[59]. Consistent with this notion, inflammation measured by the circulating levels of IL-6 is the only known cross-sectional and longitudinal predictor of multimorbidity and one of the strongest predictors of frailty and disability in daily life. Hence, the expression of IL-6 is proposed to serve as an early warning sign for the burden of multimorbidity ^[60]. As the level of IL-6 expression is included in ALI ^[61], analyzing ALI using the DNB theory will be fascinating to test as a novel methodology for the prediction of age-related loss of resilience, as well as the progression of cellular senescence in humans. Numerous studies have suggested that aging occurs through complex and dynamic changes ^[50]^[53]^[62]^[63]^[64]. This conceptual shift by “dynamical aging” opens up new and previously unexplored opportunities for the research on aging and disease prediction; thus, the DNB theory should be applied to multiple aging models with multimodal applications.

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