Intrinsically disordered proteins and phenotypic switching: Implications in cancer

CHAPTER THREE

Intrinsically disordered proteins and phenotypic switching: Implications in cancer Vivek Kulkarnia, Prakash Kulkarnib,* a

Division of Biology & Biological Engineering, California Institute of Technology, Pasadena, CA, United States b Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, CA, United States *Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Conformational noise hypothesis: The MRK model 3. Evidence supporting the MRK hypothesis 4. Learning and evolution 5. Inheritance of acquired learning 6. Therapeutic implications 7. Conclusions References Further reading

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Abstract It is now well established that intrinsically disordered proteins (IDPs) that constitute a large part of the proteome across the three kingdoms, play critical roles in several biological processes including phenotypic switching. However, dysregulated expression of IDPs that engage in promiscuous interactions can lead to pathological states. In this chapter, using cancer as a paradigm, we discuss how IDP conformational dynamics and the resultant conformational noise can modulate phenotypic switching. Thus, contrary to the prevailing wisdom that phenotypic switching is highly deterministic (has a genetic underpinning) in cancer, emerging evidence suggests that non-genetic mechanisms, at least in part due to the conformational noise, may also be a confounding factor in phenotypic switching.

“Logic will get you from A to B. Imagination will take you everywhere”

Albert Einstein.

Progress in Molecular Biology and Translational Science, Volume 166 ISSN 1877-1173 https://doi.org/10.1016/bs.pmbts.2019.03.013

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2019 Elsevier Inc. All rights reserved.

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1. Introduction A large fraction of the proteome of organisms across all three kingdoms of life is composed of proteins lacking rigid 3D structure.1–4 Instead, these unstructured proteins, referred to as intrinsically disordered proteins or IDPs, exist as conformational ensembles that are highly malleable, which ensure their rapid response to changed environment and facilitates their interactions with multiple partners.5,6 These interactions that are specific to a given cell type are “soft wired” to form protein interaction networks (PINs). Cellular PINs are organized following a “scale-free” architecture7–9 and represent the main conduit of information flow within the cell. Furthermore, the organization and properties of many PINs are evolutionarily conserved, underscoring their functional significance.10,11 Within the PINs, IDPs tend to occupy hub positions defined as nodes with multiple interactions,12–17 and play critical roles in many biological processes, such as transcription, splicing, translation, and signaling18–24. Additionally, IDPs also participate in higher order phenomena, such as regulation of the cell division cycle,25–27 circadian rhythmicity,28–32 and phenotypic plasticity that is the ability to switch phenotypes.3,4,33 Phenotypic switching may be defined as a fundamental process, in which a cell/organism can transition from one phenotype to another or two cells/organisms can display different phenotypes despite identical genotypes. Thus, phenotypic plasticity enables the cell/organism to respond to various intrinsic and external cues and stimuli in a concerted fashion, enabling them to “make” appropriate cellular decisions.34 Therefore, under normal physiological conditions, the IDPs are tightly regulated.35–39 However, if their expression is dysregulated, IDPs have the potential to engage in multiple “promiscuous” interactions resulting in pathological states.40,41 Consistent with these observations, several proteins associated with disease pathology are IDPs.42–45 Yet, a detailed understanding of the molecular mechanisms by which IDPs accomplish their functions, especially when they are dysregulated and hence, engage in promiscuous interactions, are not fully understood except in a few cases. It is generally held that IDPs can undergo transitions from disorder to order upon binding to their cognate targets (coupled folding and binding).46–51 While in some cases an ordered conformation is induced by the interacting partner, a phenomenon referred to as “induced fit,” in other instances, the IDP ensemble samples multiple conformations a priori

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and the ligand selects the most favored prefolded state from these conformations.52 However, in many cases, some combination of the two extremes has been observed,53,54 suggesting that the intrinsic secondary structure propensities of the IDPs determine their binding mechanisms. Interestingly, some IDPs appear to stochastically switch among distinct conformational states despite the fact that they appeared intrinsically disordered by several measures55,56 suggesting that IDPs can shift the overall conformation of their ensembles while remaining disordered. Therefore, it appears that despite being disordered, many IDPs are only marginally unstable and can be tipped (by changes in their environment or by interaction with specific partners, or by change in the post-translational modification status) to populate preferred conformations to become functionally active. On the other hand, some IDPs57–71 appear to remain largely disordered even while interacting with their biological targets. This structural multiplicity or dynamic disorder retained in protein complexes is termed “fuzziness.”72–76 Indeed, a recent study58 reported that IDPs can interact with the affinities approaching picomolar levels without gaining any ordered structure and retaining long-range flexibility and highly dynamic character. Thus, it follows that, IDPs may utilize a wide spectrum of interaction mechanisms, ranging from induced folding to formation of fuzzy complexes where significant levels of disorder are preserved, to polyvalent stochastic interactions.6 In this chapter, we will discuss how IDP conformational dynamics—thus the dancing protein cloud simile—can cause phenotypic switching and thereby, contribute to disease pathology. As a prelude, we shall discuss the conformational noise or the MRK hypothesis to explain how conformational noise that has not been well appreciated, in conjunction with transcriptional noise that is now well recognized, can lead to phenotypic switching as an adaptive response. We will then examine the experimental evidence that supports this idea and finally, review potential mechanisms by which this information may be inherited by non-genetic mechanisms. Finally, we shall consider how non-genetic mechanisms may contribute to phenotypic heterogeneity in the population and its therapeutic implications.

2. Conformational noise hypothesis: The MRK model It is now well established that gene expression is an intrinsically stochastic process, which often results in substantial “noise” in the system

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that is manifested as cell to cell variability in protein levels in a population.77,78 Biological noise is random variability in quantities arising in biological systems even when they are isogenic. The most frequent quantitative definition of noise is the coefficient of variation: ηx ¼

σx μx

Where ηx is the noise, μx is the mean value of x,and σx is the standard deviation of x. This measure is dimensionless, allowing a relative comparison of the importance of noise without necessitating knowledge of the absolute mean. Therefore, in response to the same stimulus, two cells can display very different phenotypes resulting in genetically identical cells to switch states and behave differently.79,80 In fact, phenotypic switching due to noise is integral to development, stress response, pathological states such as cancer, and evolution.81 However, stochasticity in gene expression is not the only source of biological noise. Accumulating evidence indicates that the information transduced in cellular signaling networks is also significantly affected by noise,82 particularly, noise contributed by the “nonfunctional” interactions of proteins.83 This noise results from the intrinsic promiscuity of proteinprotein interactions that modulate cellular signal transduction.84 It is therefore, not surprising that IDPs, that can engage in “promiscuous” interactions when dysregulated, play a significant role in generating noise due to proteinprotein interactions. In fact, there is a strong correlation between the overexpression of IDPs and altered physiological states.41 Consistently, several oncogenes42,44,85 and other cancer-associated genes43 that are overexpressed in cancer, encode IDPs. However, how IDPs contribute to noise that is distinct from transcriptional noise, and how this may in turn affect cell fate specification, has remained poorly understood. We proposed a model (also referred to as the MRK model named after the main proponents—Mahmoudabadi, Rangarajan, and Kulkarni)81 wherein, the conformational dynamics of IDPs plays a critical role in contributing to noise in cell signaling. We coined the term, conformational noise to differentiate this type of noise from transcriptional noise. Although stochasticity is implied as the underpinning in both types of noise, one should keep in mind that this underlying stochasticity is of very different nature—transcriptional noise is defined by the random variability in transcription levels, and the conformational noise is defined by the random variability in the various confirmations sampled by the IDP ensemble.

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Of note, it is important to recognize that although interconversions of conformations of the IDPs are typically fast, once modified by a posttranslational modification such as phosphorylation, the ensemble may preferentially occupy the ensuing conformation until it is dephosphorylated. Consequently, variant conformational ensembles can have significant halflives (in the order of several minutes to hours) that can have considerable physiological effects. For example, conformational switching may increase the propensity of IDPs to engage in promiscuous interactions. Since most if not all transcription factors (TFs) are IDPs, conformational noise can be an integral part of transcriptional noise. Thus, IDPs could help relay, and even amplify, total noise in the system in response to perturbations whether intrinsic or extrinsic. In a nutshell, the MRK hypothesis states that conformational noise arising from the stochasticity of the promiscuous interactions initiated by the IDPs in response to a specific input, allows the system to sample the network interaction space. Consequently, the heuristics leads to rewiring of the network and drives phenotypic switching to generate phenotypic heterogeneity. In other words, IDPs uncover network configurations that are causal in phenotypic switching but are latent under normal conditions.81 Indeed, such stochasticity in phenotypic switching has been linked to cellular differentiation,86 generation of induced pluripotent stem cells (iPS cells),87–92 and the emergence of cancer stem cells from non-stem cancer cells.93,94 Implicit in the MRK model, the PIN configuration contains information that specifies the cell’s phenotype, and each cell has equal probability to undergo a specific phenotypic transition in response to the given input. Furthermore, as depicted in Fig. 1, the model implies that the network is flexible in responding to physiological changes, but robust in response to adverse perturbations. However, in response to stress, the IDPs rewire the network unmasking latent network configurations and causes the cell to transition from one phenotype to the other. More importantly, the model proposes that, in the absence of stress, the PIN can rewire itself to the normal (default) network configuration, thereby reversing the phenotypic switch. It is also important to point out that, the MRK model postulates that information derived from PIN rewiring can operate across diverse timescales. Thus, while some of the information, particularly that which operates over relatively short timescales, maybe retained within the PIN, information that operates over longer periods, such as cellular transformation, development and evolution, is directly transferred to the genome to effect heritable

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Fig. 1 Rewiring of protein networks (PINs) facilitates state-switching by activating latent pathways. A cell with phenotype A which is defined by a protein interaction configuration characteristic of this phenotype is shown on the left. A perturbation such as stress, causes increased IDP expression. Overexpression of the IDP results in promiscuity and the protein network explores the network search space to unmask latent pathways leading to a switch in the phenotype (phenotype B) as shown on the right. But once the perturbation is withdrawn, and IDP expression is dialed down, the network again rewires itself to form the ‘default’ configuration and the cell reverts to phenotype A.

genetic/epigenetic changes, or via a mechanism similar to genetic assimilation of the acquired character proposed by Ref. 95. Together, these observations suggest that contrary to the prevailing wisdom that phenotype specification is highly deterministic, stochasticity may be a confounding factor in specifying cell fate. This thinking may also help in explaining how a given cell can reversibly switch phenotypes as seen in EMT and MET or for that matter, a drug-sensitive cell from developing resistance and switching back to drug sensitivity96–99 or the transformation of a normal cell to a malignant one and its reversal to normalcy.100,101 A theoretical perspective explaining how the latter could be actuated by c-Myc, an illustrative IDP, was provided by Ref. 10,11 lending further credence to some of the tenets enunciated in the MRK hypothesis.

3. Evidence supporting the MRK hypothesis In this section we shall examine the empirical evidence supporting the MRK hypothesis. While we mainly stress on PAGE4, we shall consider yet another example, namely that of the adaptor protein growth factor receptor-bound protein (Grb2). These two proteins used as illustrative examples here are implicated in prostate and ovarian cancer, respectively. PAGE4 is a stress-response protein.102 Thus, in response to stress, particularly inflammatory stress that is thought to be an important

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confounding factor in the etiology of both benign and malignant diseases of the prostate,103–106 PAGE4 protein levels are upregulated.102 Interestingly, in cells challenged with stress, there is an increased translocation of the PAGE4 protein to the mitochondrion and the production of reactive oxygen species is suppressed. Furthermore, p21 is elevated in a p53-independent manner in PAGE4-overexpressing cells, which results in impeded cell cycle progression, attenuated stress-induced DNA damage, and decreased cell death.102 Mechanistically, PAGE4 upregulation decreases the phosphorylation of MAP2K4, JNK, and c-JUN, while increasing phosphorylation of ERK1/2, suggesting that, under the stress, PAGE4 appears to promote the survival of prostate cancer cells by regulating MAPK pathway.107 Furthermore, PAGE4 is also a transcriptional regulator that potentiates transactivation by c-Jun.108 The stress-response kinase HIPK1 phosphorylates PAGE4 at T51, and to a minor extent, S9.60,109 This leads to higher compaction of the PAGE4 molecule facilitated by the looping of the N terminal region.60,61,88,89 HIPK1-phosphorylated PAGE4 (HIPK1-PAGE4) potentiates c-Jun, that heterodimerizes with c-Fos to form the AP-1 transcription factor complex. AP-1 is a negative regulator of the androgen receptor (AR) activity in prostate cancer cells110,111 and AR, in turn, is a negative regulator of the CDC-like kinase 2 (CLK2).88 Therefore, inhibiting AR de-represses CLK2, and CLK2 hyperphosphorylates PAGE4 (CLK2-PAGE4). Such hyperphosphorylation remodels the PAGE4 ensemble, which now prefers to assume a more random coil-like confirmation. Furthermore, in contrast to HIPK1-PAGE4, CLK2-PAGE4 attenuates c-Jun potentiation.88 Mathematical modeling suggested that these interactions between HIPK1, PAGE4, AP-1, AR, and CLK2 form a negative feedback loop, which may give rise to oscillations in intracellular levels of the different conformational ensembles of PAGE4, as well as to oscillations in AR activity88 (Fig. 2). Interestingly, while HIPK1 is expressed in both androgendependent and androgen-independent prostate cancer cells, CLK2 is only expressed in androgen-dependent cells. Therefore, while in cells that express HIPK1, a HIPK1-PAGE4-AP-1-AR network circuit is operational, in cells that express both HIPK1 and CLK2, the feedback loop can rewire this circuit. Thus, cell-to-cell variability due to differential phosphorylation of PAGE4 and hence, rewiring of the corresponding network circuit, can promote phenotypic heterogeneity potentially due to conformational noise. Consequently, oscillatory dynamics and androgen-dependence of a given cell can be a time-varying function; i.e., a cell can exhibit various extents

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WT PAGE4

AR activity

HIPK1 (Phosphorylation)

HIPK1-PAGE4

CLK2 (Phosphorylation)

Potentiation c-JUN

CLK2 Inhibition

Time

AR activity

Repression AR activity

CLK2-PAGE4 Rapidly degraded

Time

Fig. 2 Cell-to-cell heterogeneity and dynamic variations in Androgen Receptor (AR) activity in an isogenic population. (A) Regulatory circuit formed by interactions among Prostate-associated Gene 4 (PAGE4), Homeodomain-Interacting Protein Kinase 1 (HIPK1), CDC-like Kinase 2 (CLK2), c-Jun, and AR activity—a negative feedback loop. (B) Top panel, in absence of any therapeutic intervention, this negative feedback loop can lead to intracellular oscillations in AR activity. Different colored curves show oscillatory dynamics for two cells in a given isogenic population; these oscillations need not be synchronized, thus, generating cell-to-cell heterogeneity. Bottom panel, upon application of Androgen Deprivation Therapy (ADT) (or periods of AR inhibition during Intermittent Androgen Deprivation Therapy (IAD)), these oscillations can get quenched.111a

of androgen-dependence at different time points. Therefore, cells in a population need not oscillate synchronously, generating heterogeneity in an otherwise clonal population by a non-genetic, IDP conformation-based mechanism. These oscillations can be dampened by depriving the system of androgen [androgen deprivation therapy (ADT) or even intermittent ADT (IAD).88,89 However, the oscillations can be reinstituted if ADT is abrogated or if intermittent ADT (IAD) is used instead, suggesting that prostate cancer (PCa) cells can potentially transition from an androgenindependent to an androgen-dependent phenotype reversibly. It is tempting to conjecture that these observations have significant clinical implications (see Section 6). Consistent with these observations, a recent study107 reported that while forced overexpression of PAGE4 in androgen-dependent (LNCaP) and independent (DU145) cells suppressed production of reactive oxygen species (ROS) in response to stress that was induced by treating cells with hydrogen peroxide, co-expressing PAGE4 and CLK2 in these cells disabled the ability of PAGE4 to suppress ROS, suggesting that hyperphosphorylation inactivates PAGE4. In contrast, in LNCaP, but not in DU145 cells, co-expression of HIPK1 with PAGE4 reduced ROS production after H2O2 treatment. In DU145 cells, co-transfection of HIPK1 and

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PAGE4 increased ROS level compared to control cells, suggesting that HIPK1 may impact PAGE4 function in a cell type-dependent manner, although further studies are clearly needed to dissect these differences. While these data are tantalizing and corroborate the opposing effects of phosphorylation by HIPK1 and CLK2, direct evidence for the oscillatory behavior of the PAGE4 conformational variants or the HIPK1/PAGE4/AP-1/AR/ CLK2 regulatory circuit driven by the dynamic ensembles sampled by PAGE4 is currently lacking. Other examples of IDPs that play critical roles in phenotypic switching include several well studied oncogenes and proto-oncogenes. The adaptor protein growth factor receptor-bound protein (Grb2), which is intrinsically disordered,112 is a good example. In the absence of extracellular stimulation, Grb2 and the phospholipase Plcγ1 compete for the same binding site on fibroblast growth factor receptor 2 (FGFR2). Reducing cellular Grb2 results in upregulation of Plcγ1 and depletion of the phosphatidylinositol 4,5bisphosphate, which inhibits PTEN activity, leading to the aberrant activation of the oncoprotein Akt. This results in excessive cell proliferation and tumor progression in a xenograft mouse model.113 Other examples include IDPs that are critical in the creation of induced pluripotent stem cells by reprogramming with proto-oncogenic embryonic transcription factors,114 and c-Myc that can drive the pluripotent capacity of tumors to differentiate into normal cellular lineages and tissue structures, while retaining their latent potential to become cancerous.100 Considered together, these examples suggest that phenotypic switching can also result due to changes in the expression levels of IDPs and PIN rewiring in the absence of any genetic mutations corroborating the MRK hypothesis. While these observations demonstrate an adaptive response of individuals in a population learned via IDP-mediated heuristic, how this information is passed on to subsequent generations remains an important question.

4. Learning and evolution The MRK hypothesis implied that an organism learns from its experiences, while it adapts to the surrounding environment, and that such adaptive learning can contribute to its fitness. Such adaptations are the result of an exploratory search, which samples various iterations of potential outputs in order to discern and select the most appropriate ones. Therefore, it may be argued that learning, which can be described as an elaborate and iterative form of phenotypic modification that allows an organism to adjust

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its response to the same inputs over time based on the outcomes of previous outputs, could guide evolution (often referred to as the “Baldwin effect”).115–123 Therefore, it would be quite wasteful to forego the advantage of the exploration performed by the organism to facilitate the evolutionary search for increased fitness, if information about the acquired (learned) characteristics (new phenotypes) was not transferred to the genotype or at least retained in some (non-genomic) fashion to facilitate transgenerational inheritance. But how is this acquired information (or adaptive learning) is inherited is not fully understood.

5. Inheritance of acquired learning For adaptive learning to be inherited, one would anticipate that changes in the genome, whether genetic or epigenetic, would be necessary, implying a reversal of information flow from phenotype to genotype. In response to dynamic environmental fluctuations, an organism’s PINs constantly process information and organize and reorganize themselves. However, we postulated that in response to “unanticipated” environmental changes, several IDPs are overexpressed and the organism explores numerous iterations of network connections, many of which are due to the promiscuous nature of these interactions.81 This results in a specific output that the organism benefits from, and in resetting the network to a new set-point (threshold). Further, we also postulated that information derived from PIN rewiring can operate across diverse timescales.124 Thus, while some of the information, particularly that operating over relatively short timescales, may be retained within the PINs, information that operates over long periods, such as cellular transformation, development, and evolution, is directly transferred to the genome to affect heritable genetic/epigenetic changes, or via a mechanism similar to genetic assimilation of the acquired character proposed by Ref. 95 and subsequently by Ref. 125. In other words, here, genetic assimilation implies that the acquired features of organisms obtained by means of individual learning can become inherited during many generations of Darwinian evolution. Of note, several proteins that are involved in epigenetic sculpturing of the chromatin are IDPs.126,127 Alternatively, the IDPs themselves could epigenetically transmit information horizontally via a conformation-based mechanism to induce heritable traits.128,129 Although most of the currently known proteins of such type are prions, which have the capacity to adopt at least one

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conformation that self-templates over long biological timescales, recent evidence suggests that IDPs with non-prion characteristics that are widely conserved across evolution, may also act as conduits for protein-based inheritance.130 Remarkably, despite the overwhelming dogma that DNA (and in some cases, RNA) but not protein is the carrier of genetic information, protein-based epigenetic inheritance has now been uncovered in all domains of life.129,131 Insofar as genetic changes are concerned, emerging evidence suggests that a nexus between transcription factors and chromatin remodelers132,133 and between transcription factors and DNA repair proteins134–137 that are part of large PINs, may facilitate such changes. With regard to genetic assimilation, Waddington proposed that it is the process, in which an environmental stimulus that affects the phenotype has been superseded by an internal genetic factor during the course of evolution.95,138–140 However, how exactly the environmentally-induced phenotypic change in the first generation becomes genetically fixed during genetic assimilation is not fully understood. The “cooperative model” advanced by Ref. 139 suggests that epigenetically-induced phenotypic changes are incrementally and statistically replaced with multiple minor genetic mutations through natural selection. In this scenario, epigenetic and genetic changes may be considered as mutually independent but equivalent in terms of their effects on phenotypic changes. Although, the authors argue that transgenerational epigenetic inheritance is not required for evolution by genetic assimilation,139 we believe that the epigenetic changes can be transferred transgenerationally. Indeed, there is now good evidence that epigenetic inheritance is ubiquitous and is involved in adaptive evolution and macroevolution.141 Notwithstanding the molecular mechanisms however, an equally important question that needs to be considered here is the evolutionary timescale. A key point in Darwinian evolution is that it works very slowly, over millions of years of geological time, through the gradual, incremental acquisition of small differences. Then how can a cancer cell evolve in such a short time? One possibility is that142 under certain conditions, evolution could occur more rapidly than previously envisioned. For example, in the extreme case, in a population of just a few individuals, all sorts of unusual mutations could become fixed simply because the number of individuals was so small and each mutation has a much higher likelihood of survival because competition among mutant forms is lower. Through this process, a new species can arise in a few generations. However, in either case, mutations that hold the key arise by chance and without foresight for the potential advantage

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or disadvantage of the mutation. Furthermore, the underlying implication would be a unidirectional flow of information from genotype to phenotype. On the other hand, in the scenario resulting from the MRK model, wherein learning can guide evolution, changes to the genome arise due to necessity, after trial and error and not just by chance, and in a few generations, are fixed. Episodes of rapid change—network rewiring to uncover latent pathway interactions in response to environmental perturbations— could lead to genotypic changes in a relatively short order. In other words, a species need not originate in a series of gradual steps, each resulting from a mutation with a small effect, slowly changing ancestor into descendant. Rather, the genetic changes that lead to the formation of new species have large effects and happen over relatively few generations. Thus, the MRK model implies that informational flow would be bidirectional and has parallels to ideas enunciated by Ref. 143, although additional empirical evidence for informational flow from phenotype to genotype is needed. In fact, the inheritance of characteristics induced by the environment has often been opposed to the theory of evolution by natural selection. However, it is important to note that the emergence of non-conventional modes and the diversity of mechanisms for generating and transmitting variations such as the transmission of small interfering RNAs, the transmission of conformational states of IDPs, such as prions, or, at the cellular level, the transmission of self-sustaining states of gene regulation could provide additional support for the MRK theory. Indeed, a mathematical model proposed by Ref. 144 that compared the adaptive value of different schemes of inheritance may lend credence to the MRK model. The authors considered three biological phenomena that are often considered to be either irrelevant to evolution, namely, (i) role of phenotypic plasticity and developmental canalization, (ii) reverse flow of information from phenotype to genotype (or its absence), and (iii) conditions under which direct integration of information into the transmitted genotype is logically excluded as a consequence of natural selection. Their model allows for variations to be inherited, randomly produced, or environmentally induced, and, irrespectively, to be either transmitted or not during reproduction. The adaptation of the different schemes for processing variations is quantified for a range of fluctuating environments, following an approach that links quantitative genetics with stochastic control theory. When the authors conducted a gedankenexperiment that allowed them to compare the Darwinian and Lamarckian “modalities” and test the conjecture that each of them is tuned to a different type of selective pressure,

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they found that the main controlling parameter in the model appears to be the correlation a of the environmental fluctuations, with the Lamarckian modality systematically becoming more favorable, when this correlation is large, in line with the intuition that transmitting acquired information is beneficial, when the selective pressure experienced by the offspring is sufficiently similar to that experienced by the parents.

6. Therapeutic implications In the section that dealt with PAGE4, we have summarized results, which underscore the therapeutic potential of this IDP. Since the wild type (WT) PAGE4 conformational ensemble can be activated to potentiate c-Jun when phosphorylated by HIPK1, inhibiting PAGE4 using small molecule inhibitors may be a novel therapeutic strategy for low-risk, androgensensitive PCa, where it is highly upregulated. On the other hand, reinstituting the sustained expression of PAGE4 in metastatic PCa, where it is downregulated, is likely to subvert or attenuate the emergence of androgenresistant prostate cancer. Consequently, IAD may present an unprecedented opportunity to tackle high-risk PCa (Fig. 3). While the advantage of IAD over ADT remains equivocal, it is reasonable to argue that IAD can abrogate some of the undesirable side effects and costs associated with continuous ADT while improving the quality of life for PCa patients.145

Tumor volume

A

B IAD/reinstituting PAGE4 expression

ADT

(PAGE4 ) OC AD

(PAGE4 ) Met AI

Met

OC AD

Fig. 3 Schematic diagram indicating a new type of precision medicine for prostate diseases. (A) In symptomatic BPH and in organ-confined prostate cancer where PAGE4 is upregulated, inhibiting PAGE4 using small molecule inhibitors may be a novel therapeutic strategy to treat these diseases. (B) Reinstituting PAGE4’s sustained expression either by intermittent ADT (IAD) or protein expression in metastatic prostate cancer, where it is downregulated, is likely to subvert or attenuate the emergence of CRPC. OC, organ-confined; Met, metastatic; AD, androgen-dependent; AI, androgenindependent prostate cancer.111a

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In further support of reinstituting PAGE4 expression in advanced disease, two lines of tantalizing evidence are noteworthy. First, in a preclinical study, compared to vector only control tumors, the growth of PCa xenograft tumors overexpressing PAGE4 was attenuated, when the host was castrated.146 These data tend to suggest that the presence of PAGE4 attenuated PCa growth in an androgen-depleted background that may, at least conceptually, mimic castrate-resistant prostate cancer (CRPC). Second, clinical studies have revealed that higher levels of PAGE4 are an indicator of better PCa prognosis. Therefore, in hormone-naive PCa, the median survival of patients with high PAGE4 expression was 8.2 years compared with 3.1 years for the PAGE4 negative patients or the patients with low PAGE4 expression.146 Conversely, loss of PAGE4 correlated with poor overall survival in hormone-naive PCa patients. Consistently, a lower level of PAGE4 mRNA correlated with reduced incidence of biochemically recurrent PCa, although it was not an independent predictor of biochemical recurrence.147

7. Conclusions In this perspective, we have shed new light on potential non-genetic mechanisms underlying phenotypic switching. Given that IDPs play critical roles in human health, including development, as well as in disease, especially cancer, the MRK hypothesis offers a plausible explanation for the pathological effects following the dysregulated expression of IDPs. While evidence that supports several aspects of the hypothesis is steadily accumulating, additional studies are still required to firmly establish its tenability. Finally, in cancer where it has been suggested that genetic and non-genetic mechanisms may merely represent the duality of cancer113,148,148a the weltanschauung that cancer is a genetic disease primarily driven by mutations149,150,151,152 is too pervasive to overcome. However, as additional evidence accumulates, the tide is likely to change, and the IDPs are likely to emerge as important targets in disease pathology.

References 1. Peng Z, Yan J, Fan X, et al. Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in all domains of life. Cell Mol Life Sci. 2015;72(1): 137–151. 2. Ward JJ, Sodhi JS, McGuffin LJ, Buxton BF, Jones DT. Prediction and functional analysis of native disorder in proteins from the three kingdoms of life. J Mol Biol. 2004;337(3):635–645.

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