Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks

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Perspective Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks Adam Freund1,* 1Calico Life Sciences, LLC, South San Francisco, CA 94080, USA *Correspondence: [email protected] https://doi.org/10.1016/j.cels.2019.02.005

Research on aging requires the ability to measure aging, and therein lies a challenge: it is impossible to measure every molecular, cellular, and physiological change that develops over time, but it is difficult to prioritize phenotypes for measurement because it is unclear which biological changes should be considered aspects of aging and, further, which species and environments exhibit ‘‘real aging.’’ Here, I propose a strategy to address this challenge: rather than classify phenotypes as ‘‘real aging’’ or not, conceptualize aging as the set of all age-dependent phenotypes and appreciate that this set and its underlying mechanisms may vary by population. Use automated phenotyping technologies to measure as many age-dependent phenotypes as possible within individuals over time, prioritizing organism-level (i.e., physiological) phenotypes in order to enrich for health relevance. Use those high-dimensional phenotypic data to construct dynamic networks that allow aging to be studied with unprecedented sophistication and rigor. Introduction ‘‘Aging’’ is a convenient label for a diverse set of biological changes that develop in a high proportion of individuals within a population over an average lifespan. The concept that aging is worth studying is essentially a hypothesis that these conditions share causal mechanisms and that working to identify and combat those shared mechanisms is a viable strategy to improve the quality and duration of life. However, research in this area is undermined by preconceived notions that aging is a universal, intrinsic process that is distinct from disease and transcends species and environments. This leads to an excess of effort spent attempting to define and measure ‘‘true aging.’’ There is no such thing. Instead, I propose de-emphasizing the classification of phenotypes as aging (or not) and, instead, measuring the multi-dimensional set of all possible age-dependent phenotypes, which can change between populations and environments. By measuring these phenotypes over time, aging can be modeled as a dynamic network. This eliminates the need for arbitrary cutoffs, such as dictating a distinction between normal aging and disease and trying to only measure the former. Many phenotypes progressively change with time, and we cannot measure everything; because the ultimate goal is to understand and ameliorate declines in health and well-being, I propose taking the ‘‘top-down’’ approach of initially focusing on organism-level phenotypes that change with age. The field now has the hardware to measure such phenotypes and the computational infrastructure to analyze them: tools such as metabolic and behavioral monitoring chambers and video monitoring paired with machine vision are making automated, high-dimensional longitudinal phenotyping of model organisms a reality. A network approach has several benefits: (1) it quantifies aging in high-dimensional space, allowing the nuanced assessment and comparison of interventions; (2) network structure may elucidate phenotypic clusters, suggesting shared mechanisms; and (3) comparison of the networks of different populations, e.g., mice and humans, may improve preclinical models of aging

by identifying the preclinical phenotypes that are the most predictive of human phenotypes. Although a high-dimensional network is more complex than single endpoints such as lifespan or a handful of health-span parameters, it is also a more accurate and useful picture of reality. Aging Is Population Dependent What we call ‘‘aging’’ is a set of phenotypes (generally considered deleterious) that develop in a high proportion of individuals within a population over an average lifespan (a population, in this case, meaning any set of individuals under study, which in practice is a species or subspecies in a particular environment). This set depends on context, namely the specific species or subspecies and environment: our description of the aging phenotypes of a human is not identical to our description of the aging phenotypes of a mouse. Further, because aging is considered the ‘‘normal’’ trajectory for individuals, the set of phenotypes we term ‘‘aging’’ depends entirely on what we subjectively consider a ‘‘normal’’ version of each species and a ‘‘normal’’ environment. In short, the phenotypes we term aging are based on a reference point. It follows then that there is no formal requirement that the mechanisms of aging be shared between populations—as the phenotypes of aging vary by population, the mechanisms driving those phenotypes might vary by population as well. Therefore, the hypothesis that the age-dependent decline experienced by another species is causally similar to our own is just that—a hypothesis. The temptation to presuppose this hypothesis, i.e., to assume that the biological mechanisms of aging must be universal and thus span species and environment (Caplan, 2005; Hayflick, 2004, 2007b, 2007a; Strehler, 1977), is an erroneous extension of the observation that, with rare exception (Finch, 1998, 2009), all biological entities deteriorate with time. It is true that all versions of aging are ultimately driven by the passage of time itself, but that is hardly a level of causality we can or want to modulate, and there is no requirement that the aging of different populations share any layer of causality beyond this.

172 Cell Systems 8, March 27, 2019 ª 2019 The Author. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Figure 1. Hypothetical Models of How the Mechanisms of Aging Might Differ between Populations and Environments Circles represent sets of causal mechanisms driving age-dependent decline for specific populations and environments. Overlap between circles represents shared causal mechanisms. (Left) Aging is largely universal. A set of common mechanisms drive a high percentage of aging phenotypes across many populations. Targeting these common mechanisms ameliorates most aspects of aging in both model organisms and human clinical trials. (Middle) Aging is partly conserved, partly population specific. A common set of mechanisms exists but drives only a subset of aging phenotypes or affects phenotypes only partially. Targeting these common mechanisms is beneficial, but only partially; most of the age-dependent decline within a population is driven by mechanisms unique to that population. (Right) Aging is extremely population-specific. Almost all drivers of aging phenotypes change as species or environment changes, with virtually no mechanisms that are common across many species and environments. One should not assume that studying aging in any non-human context will have clinical relevance; models must be critically evaluated. Many human-relevant aging mechanisms are impossible to study in model organisms, and aging mechanisms even vary between human populations living in different environments.

We are interested in identifying the modifiable, biological mechanisms that affect the rate and nature of physiological decline, and there are no a priori conclusions we can make about the similarity of such mechanisms between species and environments. In short, all things break down; this does not mean all things break down for the same reasons. However, although not a formal requirement, some aging mechanisms may indeed be shared between populations. On one hand, aging may in fact be largely universal, with most aging phenotypes across many species and environments being driven by the same set of common mechanisms (Figure 1, left). In this case, all roads lead to Rome, and we can study aging in whatever population we choose without losing human relevance. Alternatively, there may be some common mechanisms that cross species and environments, but they may only affect a subset of aging phenotypes and/or affect phenotypes only partially (Figure 1, middle). This model is perhaps the most consistent with current data. There are a number of cellular and molecular phenotypes that arise with age in multiple species, e.g., protein aggregate formation, DNA damage accumulation, and reduced ATP generation by mitochondria, suggesting there are common aging processes at work, although it is less clear whether delaying or reversing those cellular and molecular phenotypes leads to therapeutic benefit (Lo´pez-Otı´n et al., 2013). In terms of interventions, inhibition of insulin/insulin-like growth factor 1 (IGF-1) signaling (IIS) extends the lifespan of worms, flies, mice, and perhaps humans as well as improves (or slows the decline of) a number functionally relevant phenotypes in all of those species (Barbieri et al., 2003; Junnila et al., 2013; Morris et al., 2015; Pan and Finkel, 2017). These data, along with similar results for other nutrientsensing pathways such as TOR, are evidence that at least some mechanisms of functional decline are conserved (Barbieri et al., 2003; Kenyon, 2010). Nevertheless, inhibition of IIS is far

from a panacea—not only do animals with reduced IIS signaling still decline over time, a number of age-dependent phenotypes are unaffected, or even worsened, compared to age-matched control animals (Berryman et al., 2008; Liu et al., 2019; Mao et al., 2018; Sonntag et al., 2012). While mutations in some IIS pathway components, notably FOXO3, have been associated with human longevity (Kenyon, 2010), the maximum lifespan extension achieved to date by IIS inhibition drops when moving from invertebrates to mammals (Pan and Finkel, 2017), and the same mutations that dramatically extend lifespan in mice do not cause similar lifespan extension in humans (e.g., growth hormone receptor deletion) (Junnila et al., 2013), suggesting important rewiring. Lastly, there is the possibility that the mechanisms of aging are so specific to species and the environment that virtually no important mechanisms are shared between multiple populations, and model organisms will, at best, be a poor abstraction of human aging (Figure 1, right). The data from nutrient-sensing pathway inhibition suggest that reality is not as unfortunate as this; however, we still know of no mechanism that equally affects age-dependent phenotypes in all species and environments, so it is premature to discount this possibility entirely. Further, it is not difficult to think of species whose version of aging is likely ‘‘population specific,’’ that is, unlikely to share any causal similarity with the aging of humans or standard model organisms, e.g., semelparous species such as Pacific salmon (Austad, 2004). Some might say that salmon do not experience ‘‘true aging,’’ but as discussed above, this is merely an argument from an arbitrary reference point. The more correct statement, and the one that should guide our thinking, is that the aging of salmon is probably not like human aging and thus has little relevance for our purposes. Understanding how aging mechanisms vary between populations is essential for improving models of human aging. The solution is not to preemptively declare that aging is universal or that Cell Systems 8, March 27, 2019 173

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Figure 2. Hypothetical Models of How the Mechanisms of Aging Might Connect to the Phenotypes of Aging (Left) Each aging phenotype has phenotype-specific mechanisms, but there is at least one mechanism that affects all phenotypes. Studying the set of aging phenotypes will eventually reveal this common mechanism, and targeting it will (at least partly) ameliorate all phenotypes of aging. (Middle) Each aging phenotype has phenotype-specific mechanisms and there are a few mechanisms that impinge on multiple phenotypes, but there is no single mechanism that affects all phenotypes. All else being equal, the shared mechanisms represent the most valuable therapeutic targets. (Right) There are no shared mechanisms, so studying the set of aging phenotypes in order to identify such mechanisms will not succeed. From a therapeutic perspective, each phenotype must be treated separately. In all panels, green circles and arrows represent mechanisms that impinge on more than one phenotype.

some species and environments represent ‘‘true aging’’ and others do not but rather to measure and model the age-dependent set of biological changes experienced by different species and environments and attempt to identify those that are most relevant to human aging. As discussed further below, by measuring and modeling a large number of age-dependent phenotypes in each population of interest, this process can be quantitative (e.g., network alignment) instead of qualitative (e.g., ‘‘an increase in mouse lifespan is probably equivalent to an increase in human lifespan’’) and may improve the clinical translation of aging therapies. Within a Population, There May Be Multiple Aging Mechanisms After asking whether and how mechanisms of aging are shared between populations, a next question might be whether and how the mechanisms of aging are shared between age-dependent phenotypes within a population. There is, of course, no a priori answer: as the mechanisms of aging have no formal requirement to be shared across populations, so too do they have no formal requirement to be shared across phenotypes within a population. The fact that multiple deleterious biological conditions develop with age does not necessitate that those conditions share causal mechanisms (Peto and Doll, 1997). The statement ‘‘age is the largest risk factor for many diseases’’ is sometimes employed to argue that all diseases share a common cause (aging) that can be targeted for therapeutic benefit. This arises from the fallacious notion that chronological age represents a singular biological process rather than serving as a statistical proxy for a multitude of biological changes. Chronological age may be a good predictor of multiple morbidities simply because it is correlated with many biological changes and not 174 Cell Systems 8, March 27, 2019

because those morbidities are necessarily causally linked. Similarly, your family’s income during your childhood is an excellent predictor of your educational attainment, salary, and numerous health conditions because it is correlated with a large number of demographic factors, not because all of those outcomes share the same causal chain (Currie, 2009). Therefore, it is formally possible that each age-dependent phenotype is mechanistically independent (Figure 2, right). This is implausible, given the complex systems nature of biological processes, but if true, it could be argued that studying the biology of aging is frankly a waste of time: if a set of phenotypes has nothing in common, then studying them as a set would yield no greater (and likely less) insight than studying each individually. On the other extreme, all age-dependent phenotypes may share one or more underlying mechanisms (Figure 2, left). Note that there are still phenotype-specific mechanisms in this model— such mechanisms must exist, given that aging phenotypes do not perfectly correlate with one another. However, if pan-phenotype mechanisms do exist, a single therapy might at least partly ameliorate every age-dependent phenotype within a population. This seems overly optimistic, and indeed, such mechanisms have not been discovered to date despite extensive effort. The truth likely lies somewhere in between—age-dependent phenotypes probably have some common causal mechanisms that can be targeted for multi-phenotype benefit but also phenotype-specific mechanisms that are equally important yet less pleiotropic (Figure 2, middle), and we would like to understand that causal structure to inform therapeutic strategies. The set of age-dependent phenotypes we want to ameliorate might collapse to a single, ten, or a thousand causal processes. Whatever the actual number, it can only be found by studying the relationships between the phenotypes.

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Perspective Aging versus Disease: Sound and Fury To study the relationship between items in a set, one must first describe the set, and in this case, the set should include both aging phenotypes and age-related diseases. This is because there is no objective distinction between the two, and attempts to separate them risk obfuscating important mechanisms connections. The idea that aging and disease are fundamentally distinct relies on an arbitrary ‘‘normal’’ reference point in exactly the same manner as attempts to distinguish populations that exhibit ‘‘true aging’’ from those that do not (e.g., wild-type mice versus mutant backgrounds). For example, diseases are often contrasted with aging based on the perception that aging phenotypes, unlike diseases, exhibit complete penetrance within a population (aging happens to everyone) (Blumenthal, 2003; Caplan, 2005; Gems, 2014, 2015; Strehler, 1977). However, which phenotypes appear universal depends entirely on the population under study. A scientist confined to studying a population of people who all harbor presenilin-1 mutations would characterize Alzheimer’s disease before the age of 65 as ‘‘normal aging’’ because it would appear completely penetrant. The discovery of another population without the mutation would shift the scientist’s reference point, and early-onset Alzheimer’s would be relabeled a disease. If the human species as a whole harbors a similarly deleterious stretch (or stretches) of DNA that we have yet to discover, does that mean our perception of normal aging is actually a disease state, albeit one that happens to be fully penetrant within our population? It is a semantic question, and semantics should not dictate the structure of scientific inquiry. Another common argument is that aging, unlike disease, is fundamentally intrinsic, i.e., independent of environment (Blumenthal, 2003; Caplan, 2005; Strehler, 1977). This concept presupposes the existence of some optimal environment, or more correctly, the complete lack of an environment and claims that aging is most accurately assessed in that optimal, environment-less situation, lest it be overshadowed by non-aging pathology. This is nonsensical; life requires interaction with the environment. But even the less extreme version of this concept— attempting to create a less challenging environment in order to better measure aging—is flawed. Because there is no such thing as ‘‘true aging,’’ less challenging environments cannot better expose or more challenging environments mask ‘‘true’’ mechanisms of aging. Rather, the mechanisms that limit healthspan and lifespan simply differ from environment to environment, and the choice of environment, like the choice of genetic background, should be driven by relevance. For example, from a translation perspective, the preferred temperature at which to house mice for aging studies is not necessarily the one that stresses them the least; it is the one that renders their aging process the most similar to humans. This rationale applies to any other criterion used to demarcate aging from disease. ‘‘Aging’’ and ‘‘disease’’ represent cultural and clinical judgments about normality applied to non-discrete realities (Gladyshev and Gladyshev, 2016). Similar concepts exist in other fields: there is no such thing as a ‘‘true planet’’ but simply an agreed-upon set of rules for categorizing celestial bodies. Although those rules might aide certain discussions, the idea that Neptune must obey a different set of physical principles than Pluto because the former is christened a planet and the latter is not is obviously fallacious.

Not only is it impossible to draw distinct lines between aging and disease, but such an exercise provides little value. Others have argued that maintaining a distinction between aging and disease is important because aging is mechanistically distinct from disease, and thus, they require different approaches for research and intervention (Mann, 1997; Rattan, 2014), but this argument begs the question. We don’t know how age-dependent phenotypes with complete or high population penetrance (i.e., ‘‘aging’’) are mechanistically related to age-dependent phenotypes with lower population penetrance (i.e., ‘‘age-related diseases’’), thus taking fundamentally different approaches to studying and treating them is currently unwarranted. We should seek to understand the causal chains within and between as many age-dependent phenotypes as possible in order to treat them, irrespective of whether we label them aging or disease. One might argue that a distinction between aging and agerelated disease is helpful because the latter should be prioritized for treatment. Such a prioritization may indeed be medically useful, but it is relatively straightforward to prioritize age-dependent phenotypes without reference to any label: the more common and more deleterious a phenotype is (i.e., the more it impacts quality or duration of life), the more important it is to ameliorate. Although hair graying and skin wrinkling are nearly universal, compared to conditions such as cognitive decline, sarcopenia, bone loss, and cardiovascular disease, they have marginal influence on overall health and thus are less important to treat. The more deleterious phenotypes ameliorated and the more people whose quality or duration of life is improved, the more valuable a therapy becomes, regardless of whether it is branded a disease therapy or an aging therapy. Imagine a clinical trial that finds that a drug reduces the incidence of the most common age-related diseases (without deleterious side effects): cardiovascular disease, diabetes, Alzheimer’s and Parkinson’s, chronic kidney disease, sarcopenia, osteoporosis, arthritis, and cancer. Should these data be sufficient to label that pill an ‘‘anti-aging therapy,’’ or is it a disease therapy? It doesn’t really matter—irrespective of what we call it, it would clearly be an incredibly valuable therapy and virtually everyone would want to take it. Age Is Just a Number. Aging Is Just Many, Many Numbers Rather than parse aging and disease into separate lists and seek to measure only the former, we should measure every agedependent phenotype we can in our population of interest, then embrace that complexity by modeling the multi-dimensional distribution of biological states as a network (Figure 3A). These phenotypes can be measured at multiples ages; aging, then, can be modeled as the change in the network over time (Figure 3B). Whether an intervention has altered aging must be assessed quantitatively in multi-dimensional space, based on the overall response of the network, rather than as a simple yes or no. There are many ways to construct and interpret networks (Newman, 2010), and dynamic networks, in particular, are an
, 2014), but in ongoing area of research (Faisal and Milenkovic short, each node of the network represents a measured phenotype, and edges describe relationships between phenotypes. Nodes can be either quantitative (e.g., fasting blood glucose) Cell Systems 8, March 27, 2019 175

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Perspective Figure 3. Hypothetical Network Model of Aging For illustration purposes only; not based on actual data. (A) Visualization of a single-time-point phenotypic network depicting measured phenotypes (nodes) and partial correlations above a certain threshold (edges). Example node labels demonstrate the variety of phenotypes that could be incorporated. Edge thickness represents a relationship between phenotypes, e.g., the partial correlation coefficient. Node aesthetics could represent any of a number of nodal attributes, e.g., node size could represent the population variance of the phenotype. Colors represent visually identifiable clusters in which the high partial correlations between phenotypes might indicate shared causality. (B) Visualization of dynamic phenotypic network depicting single time points as above and temporal correlations above a certain threshold (dashed edges). Node attributes and connectivity can change over time, and nodes having little no or no partial correlation at the same time point may have substantial partial correlation between time points.

or binary phenotypes (e.g., diagnosis of diabetes), although replacing the latter with the former should be a general goal. For longitudinal data, nodes can also be longitudinal phenotypes such as rates of change (derivatives), absolute change from a previous time point (deltas), and cumulative exposure (integrals). Edges can represent a number of possible relationships between phenotypes, but one of the simplest and most powerful relationships, subject to certain assumptions about the data structure, is the correlation between phenotypes within a population, where each individual at a given time point represents a single observation, and more correlated phenotypes are linked by thicker edges. For dynamic networks, edges can also extend between time points (e.g., a diagnosis of cardiovascular disease will likely be correlated to serum cholesterol levels from years earlier). When measuring many phenotypes simultaneously, many correlations will exist due to indirect effects (A and B are both correlated with C, so A and B are correlated with one another). This can impair interpretation, particularly when assessing whether phenotypes likely share common mechanisms. For high-dimensional data, partial correlation is a better approach (de la Fuente et al., 2004; Marrelec et al., 2006). Partial correlation is the remaining correlation after accounting for and removing the effect of all other measured phenotypes. Although it cannot account for unmeasured phenotypes, the partial correlation between two 176 Cell Systems 8, March 27, 2019

phenotypes in a high-dimensional dataset often represents a reasonable approximation of the causal relationship between those phenotypes. In other words, the more phenotypes measured and accounted for, the more confident a researcher can be that a higher partial correlation coefficient (closer to 1) indicates a closer causal relationship between two phenotypes, though the direction of causality is not inferable unless one temporally precedes the other (Shipley, 2016). For example, if red blood cell count and blood hemoglobin concentration maintain a high partial correlation after accounting for many other measured phenotypes, it is likely they have a close causal relationship (i.e., because red blood cells produce hemoglobin). Conversely, many phenotypes are correlated with body weight and thus are correlated to one another, but their partial correlation once accounting for body weight is negligible, suggesting that they are largely driven by different mechanisms. Like correlation coefficients, partial correlation coefficients can be calculated for every pairwise combination of phenotypes and the values visualized as edge thicknesses. In practice, edges below an arbitrary cutoff value are not drawn in order to avoid a visual hairball. As with all models, networks can be generated based on one set of data and evaluated for accuracy in an independent set. Age-dependent phenotypes accumulate at the level of molecules, cells, organs, and organ systems. Biological reductionism tempts us to focus on cellular and molecular changes, and indeed, multiple network models of genes, proteins, and cellular pathways have been proposed to describe aging in humans and other species (Kirkwood, 2011; de Magalha˜es and Toussaint, 2004; Smita et al., 2016; Tacutu et al., 2012; Zierer et al., 2016). These models, which promote a ‘‘bottom-up’’ view of aging, have been prioritized partly because they may elucidate mechanisms but also because the data can be readily

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Perspective generated: high-throughput sequencing and proteomics have made high-dimensional cell and molecular data relatively easy to attain. However, many phenotypes change with time at the cell and molecular level, and likely, not all are important. Because we ultimately want to understand and ameliorate the age-dependent decline in organism-level health and well-being, I propose taking a ‘‘top-down’’ approach by constructing networks starting with organism-level phenotypes that change with age. This not to say that only these high-level phenotypes should be included in such a network, but their inclusion is essential because they are the most likely phenotypes to directly affect the quality and duration of life and thus can serve as ground truth by which the relevance of other more granular phenotypes, such as cell and molecular -omics data, is validated. In other words, if an age-dependent phenotype is detectable at the level of the whole organism, it’s probably worth measuring and ultimately treating. Further, because organism-level phenotypes are generated by the combined state of the molecules and cells that make up the organism, a relatively small number of such phenotypes may describe overall system state as or more accurately than a very large number of cell and molecular phenotypes. For example, one might develop a computational model to estimate lung function through the single-cell RNA sequencing of every cell in the lung, or one could simply measure the oxygen consumption of the animal. The latter is less mechanistic but is a more straightforward way to describe the relevant phenotype. Of course, we cannot measure every organism-level phenotype that changes with age, but it is fairly straightforward to prioritize measurements using the same criteria as the prioritization of clinical intervention in the previous section: the more a phenotype reduces quality or duration of life and the more people affected, the more important it is to measure that phenotype and ultimately treat it (e.g., cognitive decline is more important to measure than gout). In this network paradigm, we make no objective distinction between aging and disease; we are interested in the change in the entirety of the network over time. When confronted with a large number of observations, it is often useful to aggregate them into summary statistics. In the case of age-dependent phenotypes, a popular summary statistic is ‘‘biological age,’’ which is calculated by combining a number of phenotypes into a single score, meant to represent a person’s biological state more accurately than does their chronological age (Klemera and Doubal, 2006). This approach provides an accessible conceptual framework and has been applied to a number of data modalities, from DNA methylation to physiological parameters (Borkan and Norris, 1980; Horvath, 2013; Levine, 2013; Nakamura and Miyao, 2007), but it reduces the comparison of complex biological states to the comparison of single numbers, which destroys information. Because it assumes that most, or even all, relevant age-dependent differences between individuals can be captured by a single dimension, the concept of a single biological age is itself probably fundamentally flawed (Costa and McCrae, 1988). Fortunately, reducing a network of phenotypes to a single biological age is unnecessary given modern analytics; fully utilizing this type of network model requires departing from reductionism, embracing complexity and the idea of emergent phenomena, and navigating high-dimensional analysis (Cohen, 2016), but the reward is a far more accurate picture of reality.

The Utility of a Network Model A network model easily absorbs situations that would pose problems for the traditional view of aging as a universal process, distinct from disease. For example, a process that occurs in a relatively small fraction of the population, e.g., gout, is not removed from the network as ‘‘not aging’’; rather, its low probability of occurring as part of any given individual’s aging process is encoded by its largely invariant distribution (mostly zero) over age and its low connectivity to more common phenotypes. Phenotypes driven primarily by physical processes and effectively independent of biology, e.g., the D/L ratio (the ratio of rightand left-handed configurations) of amino acids in teeth, which increases with age via spontaneous racemization but is thought to have no functional consequence (Helfman and Bada, 1976), will have dynamic distributions over age but will poorly correlate with clinically relevant phenotypes (or at least, correlate no better than chronological age), and their relatively unconnected positions in the network will demonstrate this lack of common mechanism. A network model allows us to measure age-dependent change in a multi-faceted way. This will both provide a better understanding of the possible trajectories of decline, i.e., to what extent individuals age differently, and enable nuanced assessment of potential interventions, providing a more rapid and information-rich readout than standard metrics such as lifespan. With enough sensitivity and/or enough data points, we may be able to detect numerous age-dependent changes in relatively young individuals over a relatively short time period (e.g., following 6-month-old mice for 3 months or 40-year-old people for 3 years) and could use this to rapidly assess whether putative aging interventions improve meaningful functional phenotypes. Small variations in time series data (e.g., ‘‘micro-recoveries’’) can often predict much larger changes that occur later (Dakos et al., 2012; Scheffer et al., 2018). This approach could dramatically accelerate preclinical testing and might even be employed in clinical trials. In a network paradigm, potential therapies are not judged as having a binary (yes or no) effect on aging but by examining how many phenotypes are affected, at what ages, by how much, and how those phenotypes are related to each other. For example, a potential therapy might improve baseline kidney function but not the rate of decline, the rate of muscle degeneration but not baseline muscle function, and both the baseline and rate of decline of lung function, while having no effect on cognitive decline. Given the complexity of the finding, the question of whether this therapy ‘‘slows aging’’ would be overly simplistic. Because we do not yet know the underlying causal structure of the network or the extent of pleiotropy (how many phenotypes are affected by any single mechanism), we do not know how many nodes can be affected by any single target, and we therefore cannot confidently predict a best-case scenario. However, another use of the network, elucidating causal hypotheses, may provide insight in this regard. The structure of the network (e.g., the emergence of nodal clusters linked by correlation) could suggest, at least to an order of magnitude, how many mechanistically independent sets of phenotypes likely exist. Processes and phenotypes that are highly correlated are more likely to share common mechanisms; moreover, the earlier in life such correlations can be identified, the closer one comes to Cell Systems 8, March 27, 2019 177

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Perspective identifying the common mechanism. If the network incorporates molecular data, its structure could even help to identify the mechanisms themselves. Using a network approach to identify the links between age-dependent phenotypes is similar to, albeit somewhat broader than, the recently described geroscience concept, which emphasizes the need to systematically investigate the links between aging and age-related diseases (Kennedy et al., 2014; Sierra and Kohanski, 2017). Although I do not subscribe to the distinction between aging and age-related disease, from a geroscience perspective, one could examine the network edges between traditional age-related disease nodes and cell or molecular nodes to propose causal hypotheses. For example, if sarcopenia and chronic kidney disease have a higher partial correlation than one would expect by chance, it might suggest they share a common mechanism. Further, if the incidence of sarcopenia and chronic kidney disease were both highly correlated with the value of a node representing serum TNFa from several years prior to their diagnosis, it might suggest that TNFa level represents an earlier step in the causal chain leading to those conditions. These hypotheses could then be tested. A final use of a network model of aging is to compare networks of different populations or species, thereby allowing identification of analogous phenotypes and mechanisms that might exist. As discussed above, although it is tempting to believe that the age-dependent changes of another species are relevant to humans, there is no reason it must be so; we must identify and optimize human-relevant models and readouts, not assume we already know what they are. Alignment between networks from different species, which will generally consist of different pheno€ssig, 2006; typic nodes, is a challenging problem (Berg and La Faisal et al., 2015), but this type of analysis has the potential to clarify which phenotypes should be measured in model organisms to yield the greatest insight into human aging. For example, by comparing the network of mice to that of humans, we might identify an age-dependent phenotype in mice (e.g., decreased social interaction) that shares many of the same nodal neighbors as a clinically relevant human phenotype that is difficult to directly model in rodents (e.g., loss of higher cognitive functions). This association would suggest that the mouse phenotype of decreased social interaction represents a useful preclinical readout even if it is not health-limiting in mice. Network alignment can most readily be accomplished by identifying common nodes to help anchor the alignment (e.g., body temperature, the serum concentration of conserved proteins), but in the absence of common nodes, it can also be accomplished by measuring the response of both networks to the same perturbation (e.g., rapamycin-treated humans versus rapamycin-treated mice) and aligning nodes that respond similarly. Data from an increasing number of perturbations yield an increasingly accurate alignment, and this approach can be extended by using pre-existing genetic variation as the perturbation; e.g., by comparing the network response to a genetic polymorphism in mice with the network response to polymorphisms in the orthologous gene in humans, we may be able to identify murine surrogates of human phenotypes. This is largely analogous to inferring gene regulatory pathways from transcriptomic data by grouping genes that respond similarly to perturbations (Datlinger et al., 2017; Dixit et al., 2016; Jaitin et al., 2016), except that in this case, many of the phenotypes are organismlevel states instead of mRNA abundances. 178 Cell Systems 8, March 27, 2019

We Need Tools to Measure Many Organismal Phenotypes Simultaneously and Repeatedly The difficulty in modeling age-dependent change as a dynamic network is that it requires a multi-faceted experimental readout: to determine the response of a network, it is necessary to measure the nodes of the network at multiple times. Traditionally, however, such longitudinal observation is not performed; lifespan, the most common endpoint used as a proxy for aging, is defined by a single event in each individual. Although unambiguously measurable and palpably relevant, lifespan has numerous limitations, most notably that it (1) provides no insight into the biological state at any point prior to death, (2) takes a long time to read out, and (3) can be altered by changes to single causes of death (e.g., cancer in mice), which may be of limited interest. Research on aging is gaining momentum outside of academia; biotechnology companies are turning to aging as a potential clinical indication, making mammalian models essential (de Magalha˜es et al., 2017). Unfortunately, in this regard, lifespan studies in even the shortest-lived mammals are painfully long, particularly considering the failure rate inherent to drug development (Hay et al., 2014). Alternate endpoints are necessary; being able to precisely and rapidly measure the functionally relevant phenotypes modulated by a putative aging therapeutic is a sine qua non for translational research in geroscience. Accordingly, we must develop methods to acquire the relevant phenotypes. This is not a new idea: a number of multi-phenotype healthspan assessments and frailty indices have been developed for both humans and rodent models (Clegg et al., 2016; Huffman et al., 2016; Ladiges et al., 2017; Liu et al., 2014; Richardson et al., 2016; Searle et al., 2008; Whitehead et al., 2014). Standardized age-related phenotyping pipelines for rodent models are absolutely a valuable step forward, but they tend to involve challenge-based readouts (e.g., wheel running, rotarod ability, maze navigation, fine motor control, and startle response) that are low dimensional, in that each assay returns only one or a handful of phenotypes, and labor intensive to perform. Even frailty indices, which can often be performed through passive visual inspection, require the time of trained technicians. This limits the number of animals that can be assessed, the number of time points that can be measured, and the number of phenotypes than can be quantified. This, in turn, hinders reproducibility—behavioral assays are notoriously variable between labs and even between experiments within the same lab (Brown et al., 2018; Fischer et al., 2016). Ideal phenotyping pipelines would be high dimensional, temporally dense, longitudinal, automated, and scalable. Technology in this area is rapidly advancing. For rodents, metabolic and behavioral monitoring cages can extract dozens if not hundreds of physiological parameters simultaneously, with exquisite time resolution (from 1-s to 5-min intervals) (Bonasera et al., 2016; Fischer et al., 2016; Van Klinken et al., 2012; Westbrook et al., 2009). This level of temporal density allows for innovative analyses that are inaccessible to other phenotyping pipelines, such as features based on the natural fluctuations within individuals (e.g., temporal autocorrelation). These fluctuations arise stochastically rather than from specific challenge-based tasks and can predict much larger changes that occur later (e.g., mortality) (Scheffer et al., 2018). Video monitoring is the next frontier; already, machine vision

Cell Systems

Perspective algorithms can extract activity, respiration, gait, and behavioral tendencies and predict the identity and dose of psychoactive drugs given to mice (Brown et al., 2018; Wiltschko et al., 2015). It is plausible that virtually all parameters currently acquired by technician-based frailty assessment will soon be quantifiable automatically and continuously. As multi-animal tracking improves, multiple aspects of social interaction are also becoming measurable (Hong et al., 2015). In the future, implantable chips may be able to continuously acquire non-visible parameters such as body temperature, blood pressure, and blood glucose (Chen et al., 2014). For humans, activity data generated by wearables and image and video analyses and behavioral data assessed by online activity can provide a rich description of functional state. When extracting so many parameters, many will be highly correlated (e.g., VO2 and VCO2), but as discussed above, tools such as partial correlation can identify whether new features are largely redundant with existing features and therefore not worth the effort of computing. As the network takes shape, its interconnected structure may reveal that a relatively small number of high-level nodes can reasonably summarize the overall state (much like a small set of protein markers identifying a cell type), obviating the necessity to profile every phenotype in every experiment; of course, like the putative shared causality of age-dependent phenotypes, this is a hypothesis that must be tested, not a conclusion that should be presumed. Conclusion The concept that aging is worth studying is essentially a hypothesis that there are shared mechanisms that affect multiple agedependent phenotypes and that working to identify those mechanisms and ways to combat them is a promising path to therapy. If we are to seriously test that hypothesis, we must first be able to describe and measure the set of phenotypes we are interested in, i.e., we must be able to measure aging. This is a difficult task because aging is a subjective label. Rather than drawing arbitrary cutoffs based on population penetrance or obscuring the problem by pre-defining aging as a universal process, I propose building empirically determined dynamic networks to statistically describe the aging space in different populations of interest. With such models, we can quantitatively address questions of a common mechanism, connectivity, causation, and intervention. A goal (perhaps the goal) of aging research is to increase the quality and duration of life for as many people as possible. Regardless of how we define aging, the more penetrant and deleterious a phenotype is, the more significant it becomes to that goal. Similarly, the more a therapy ameliorates multiple conditions in multiple people, the closer it approaches that goal. Rather than asking the binary question ‘‘Does this intervention slow aging?’’, we should turn to more sophisticated questions of relevance and penetrance, which can only be answered in multi-dimensional space: which conditions does this intervention ameliorate? In what percentage of the population? For how long? By taking a network approach to aging, we can begin to reduce the dimensionality of the problem with the clear-eyed realization that we may only go so far as the biology is actually reducible. This may be one process, or ten, or a thousand, but aging is not infinitely complex, and neither is its solution.

ACKNOWLEDGMENTS I am grateful to Emily Freund, Anne Sapiro, Carmela Sidrauski, Scott McIsaac, Calvin Jan, Chris Patil, Anastasia Baryshnikova, Cynthia Kenyon, Daphne Koller, and Eugene Melamud for useful discussions and comments on earlier drafts of this essay. DECLARATION OF INTERESTS Adam Freund is an employee of Calico Life Sciences, LLC, a research and development company focused on aging. The author declares no other competing financial interests. REFERENCES Austad, S.N. (2004). Is aging programmed? Aging Cell 3, 249–251. Barbieri, M., Bonafe`, M., Franceschi, C., and Paolisso, G. (2003). Insulin/IGF-Isignaling pathway: an evolutionarily conserved mechanism of longevity from yeast to humans. Am. J. Physiol. Endocrinol. Metab. 285, E1064–E1071. €ssig, M. (2006). Cross-species analysis of biological networks Berg, J., and La by Bayesian alignment. Proc. Natl. Acad. Sci. USA 103, 10967–10972. Berryman, D.E., Christiansen, J.S., Johannsson, G., Thorner, M.O., and Kopchick, J.J. (2008). Role of the GH/IGF-1 axis in lifespan and healthspan: lessons from animal models. Growth Horm. IGF Res. 18, 455–471. Blumenthal, H.T. (2003). The aging–disease dichotomy: true or false? J. Gerontol. A Biol. Sci. Med. Sci. 58, M138–M145. Bonasera, S.J., Arikkath, J., Boska, M.D., Chaudoin, T.R., DeKorver, N.W., Goulding, E.H., Hoke, T.A., Mojtahedzedah, V., Reyelts, C.D., Sajja, B., et al. (2016). Age-related changes in cerebellar and hypothalamic function accompany non-microglial immune gene expression, altered synapse organization, and excitatory amino acid neurotransmission deficits. Aging 8, 2153–2181. Borkan, G.A., and Norris, A.H. (1980). Assessment of biological age using a profile of physical parameters. J. Gerontol. 35, 177–184. Brown, S.D.M., Holmes, C.C., Mallon, A.M., Meehan, T.F., Smedley, D., and Wells, S. (2018). High-throughput mouse phenomics for characterizing mammalian gene function. Nat. Rev. Genet. 19, 357–370. Caplan, A.L. (2005). Death as an unnatural process: why is it wrong to seek a cure for aging? EMBO Rep. 6, S72–S75. Chen, L.Y., Tee, B.C.-K., Chortos, A.L., Schwartz, G., Tse, V., Lipomi, D.J., Wong, H.S., McConnell, M.V., and Bao, Z. (2014). Continuous wireless pressure monitoring and mapping with ultra-small passive sensors for health monitoring and critical care. Nat. Commun. 5, 5028. Clegg, A., Bates, C., Young, J., Ryan, R., Nichols, L., Ann Teale, E., Mohammed, M.A., Parry, J., and Marshall, T. (2016). Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing 45, 353–360. Cohen, A.A. (2016). Complex systems dynamics in aging: new evidence, continuing questions. Biogerontology 17, 205–220. Costa, P.T., and McCrae, R.R. (1988). Measures and markers of biological aging: ‘a great clamoring . of fleeting significance’. Arch. Gerontol. Geriatr. 7, 211–214. Currie, J. (2009). Healthy, wealthy, and wise: socioeconomic status, poor health in childhood, and human capital development. J. Econ. Lit. 47, 87–122. Dakos, V., Carpenter, S.R., Brock, W.A., Ellison, A.M., Guttal, V., Ives, A.R., Ke´fi, S., Livina, V., Seekell, D.A., van Nes, E.H., et al. (2012). Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS One 7, e41010. Datlinger, P., Rendeiro, A.F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L.C., Kuchler, A., Alpar, D., and Bock, C. (2017). Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301. Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C.P., Jerby-Arnon, L., Marjanovic, N.D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016). Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17.

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