A Minimal Fate-Selection Switch

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ScienceDirect

A minimal fate-selection switch

Leor S Weinberger

To preserve ﬁtness in unpredictable, ﬂuctuating environments, allowing chance to govern each seed’s fate to germinate or

a range of biological systems probabilistically generate variant not. For example, if husk thickness of each seed is

phenotypes — a process often referred to as ‘bet-hedging’, allowed to stochastically vary, a fraction of seeds will

after the ﬁnancial practice of diversifying assets to minimize risk randomly remain non-germinated regardless of the envi-

in volatile markets. The molecular mechanisms enabling bet- ronment, leaving a long-lived sub-population to avoid

hedging have remained elusive. Here, we review how HIV extinction during long-term droughts. Borrowing from

makes a bet-hedging decision between active replication and economic theory, Cohen proposed that biological organ-

proviral latency, a long-lived dormant state that is the chief isms could ‘hedge their bets’ in much the same way that

barrier to an HIV cure. The discovery of a virus-encoded bet- ﬁnancial houses diversify their assets — between higher-

hedging circuit in HIV revealed an ancient evolutionary role for risk (higher-yield) stocks and lower-risk (lower-yield)

latency and identiﬁed core regulatory principles, such as securities — in order to minimize risk against market

feedback and stochastic ‘noise’, that enable cell-fate crashes.

decisions. These core principles were later extended to fate

selection in stem cells and cancer, exposed new therapeutic Stochastic gene expression enables ‘bet-

targets for HIV, and led to a potentially broad strategy of using hedging’: from theory to the initial HIV

‘noise modulation’ to redirect cell fate. evidence

Address For many years, the molecular mechanisms enabling

Gladstone Institutes (Virology and Immunology), Department of biological systems to probabilistically hedge their bets

Biochemistry & Biophysics, University of California, San Francisco,

between alternate developmental fates remained unclear.

United States

Viruses proved to be powerful model systems to deﬁne

core regulatory principles underlying bet-hedging deci-

Corresponding author: Weinberger, Leor S ([email protected])

sions. Viruses optimize ﬁtness in variant cellular environ-

ments (i.e., target rich vs. target poor) and do so under

Current Opinion in Cell Biology 2015, 37:111–118 strong genetic constraints, selecting for minimalist regu-

This review comes from a themed issue on Differentiation and latory circuits (RNA virus genomes are typically 10 kB,

disease thereby precluding sophisticated environmental sensing

Edited by Scott A Armstrong and Michael Rape and actuation that require multiple genes and substantial

genomic real estate). In the late 1990s, mathematical

For a complete overview see the Issue and the Editorial

models of the lysis-lysogeny fate decision in the bacterial

Available online 21st November 2015

virus phage l suggested that a basic biophysical phenom-

http://dx.doi.org/10.1016/j.ceb.2015.10.005

enon intrinsic to life at the single-cell level — molecular

0955-0674/ 2015 The Author. Published by Elsevier Ltd. This is an ﬂuctuations driven by Brownian diffusion — could gen-

open access article under the CC BY-NC-ND license (http://creative-

erate the variability required for a developmental bet-

commons.org/licenses/by-nc-nd/4.0/).

hedging decision [7]. These diffusion-driven Brownian

ﬂuctuations in regulatory enzymes, mRNAs, and other

biomolecules are unavoidable and appear sufﬁcient to

Introduction: ‘bet-hedging’ during fate- shift cells between transcriptional on and off states [8].

selection decisions With some cells randomly active and others dormant, the

Biological systems use two general strategies to maintain computational result is a distribution of cell fates across a

ﬁtness in variable, ﬂuctuating environments. One strategy population. That is, the models showed that stochastic

involves the evolution of sophisticated sensor-actuator ﬂuctuations in gene expression enable the phage to

networks that continually assess environmental surround- mediate between its two alternate developmental fates

ings and adapt, generating a tailored response to a given without sensor-actuator circuitry.

surrounding. Sensor-actuator strategies appear through-

out bacterial chemotaxis systems and osmoregulation [1– The ﬁrst experimental evidence for the theory that mo-

5]. An alternate strategy, proposed 50 years ago by the lecular ﬂuctuations could drive a developmental bet-

theoretical ecologist Dan Cohen [6], involves probabilis- hedging decision was found in another virus: the human

tically generating a range of different phenotypes in any immunodeﬁciency virus type 1 (HIV), which

given environment. While studying desert annual plants, probabilistically chooses between active transcription

where reproductive success is governed by unpredictable and a latent state [9]. HIV has become a useful model

weather patterns, Cohen noted that ﬁtness could be to experimentally elucidate core principles underlying

enhanced if seeds varied in their germination potential, stochastic bet-hedging, and the principles discovered in

www.sciencedirect.com Current Opinion in Cell Biology 2015, 37:111–118

112 Differentiation and disease

HIV have been generalized to fate-selection decisions virus-encoded latency circuit suggested an unknown

occurring in bacteria [10], stem-cell reprogramming selective advantage that counterbalances latency’s puta-

[11,12], and cancer drug tolerance [13]. Below, these core tive ﬁtness cost (i.e., reduced long-term viral loads).

principles of fate-selection are reviewed in the context of

the HIV latency decision. To address this discrepancy, Rouzine et al. [26 ] exam-

ined whether latency could provide a ﬁtness advantage

HIV latency: a bet-hedging strategy to during initial infection, when the virus must pass through

optimize viral transmission the target-cell-poor mucosa, where >90% of HIV infec-

Upon infecting CD4 T lymphocytes, HIV either actively tions start [27]; the evolutionary precursor to HIV in

replicates to rapidly produce progeny virions or enters a humans — SIV in non-human primates — also spreads

long-lived quiescent state (proviral latency), from which it through mucosal transmission [28]. In target-poor envir-

subsequently reactivates [14]. Latently infected cells onments (e.g., the early mucosa) high virulence quickly

form a viral reservoir, enabling life-long viral persistence depletes available targets, leaving no infectable cells at

and necessitating lifelong antiretroviral therapy (ART) for the site. Consequently, if a virus is highly virulent, the

HIV-infected individuals. The evolutionary conundrum mucosal infection is extinguished before the virus can

was how latency had been maintained over the centuries spread to the target-rich environments to generate self-

of natural lentiviral infections in non-human primates sustaining systemic infection (Figure 1). In fact, direct

before the current ART era, given the rapid evolution observations in macaques show that mucosal infections

of the virus. precipitously decay toward local extinction in the ﬁrst few

days after inoculation [27,29].

Historically, the prevailing dogma in retrovirology had

been that HIV latency played no role during HIV’s Rouzine et al. [26 ] built off patient-validated models

natural history of infection [14,15]. In the absence of [30] to calculate what the optimum bet-hedging frequen-

ART, latency was thought to be a deleterious phenotype, cy would be, given the inherent cost–beneﬁt tradeoff

since latently infected cells produce no virus and decrease latency generates between systemic and mucosal infec-

viral loads. Given latency’s reduction of lentiviral repli- tion. Surprisingly, they found the optimal bet-hedging

cative ﬁtness, latency seemed to be an evolutionary frequency was 50% latency, the same latency frequency

accident — an ‘epiphenomenon’ reﬂecting serendipitous typically found in infections of cultured T lymphocytes.

lentiviral infection of some CD4 T cells precisely during Incorporating immune responses into the model enabled

their transition from an activated state to a quiescent- the authors to ﬁt all available patient data, including a far

memory state [16–18]. Latency was therefore viewed as lower latency frequency [26 ]. A recent clinical study in

an infrequent bystander effect that occurs only after a patients [31 ] further supports the model that latency is a

virus-driven adaptive immune response begins and CD4 bet-hedging adaptation to optimize transmission through

T lymphocytes start to form memory subsets. the target-poor mucosa.

However, several ﬁndings were inconsistent with this Molecular architecture of HIV’s bet-hedging

dogma. First, in Rhesus monkeys, viral latency is rapidly circuit

established long before the adaptive immune response For many years, the absence of a viable evolutionary

begins [19 ]. Second, in patient cells, reactivation of ﬁtness argument bolstered the notion that HIV proviral

quiescent-memory cells infected with latent HIV did latency was a bystander effect (i.e., epiphenomenon)

not necessarily reactivate the latent virus [20 ,21]. Third, ineludibly resulting from transcriptional silencing during

latency occurs with high frequency in cell culture (50% relaxation of activated lymphocytes to a quiescent-mem-

of infected cells establish latency), despite the absence of ory state (Figure 2a). However, despite the vast literature

immune responses or any cellular relaxation to memory of associative evidence linking latent HIV integration

[22 ,23]. Fourth, remarkably, HIV’s Tat feedback circuit sites to silenced chromatin and correlating latency with

is sufﬁcient to generate a probabilistic bet-hedging deci- cellular silencing [32,33], there was also evidence for an

sion in the absence of cellular relaxation, and the molec- alternate model where latency was controlled by viral

ular architecture of the circuit appears optimized to gene-regulatory circuitry [9,20,34] without strict depen-

generate a bet-hedging decision [9,24,25] (described in dence on cellular state (Figure 2a). Critically, the hypoth-

more detail below). esis that latency establishment was driven by cellular

state had never been directly tested (i.e., lymphocytes

If latency were a non-beneﬁcial viral trait or an epiphe- had never been infected and tracked in real time as they

nomenon, the latency phenotype should have been lost underwent relaxation to memory).

due to natural selection or genetic drift — given the rapid

evolution of lentiviruses — and the viral gene-regulatory The ﬁrst direct test between these models was only

circuitry should have evolved away from being an recently carried out. Razooky and Pai et al. [35 ]

intrinsic latency generator. The persistence of this isolated primary CD4 T lymphocytes from human

Current Opinion in Cell Biology 2015, 37:111–118 www.sciencedirect.com

The HIV latency circuit Weinberger 113

Figure 1 Figure 2

(a) Active (a) Alternate HIV Latency Models Replication Cell-state Autonomous Dependent Viral Program Viral Activity Viral Activity

Cellular State Cellular State

(b) Viral Infection Latency Viral

(b) Expression Initial Systemic Activated Infection Infection Cell- Latency- (Target-poor) (Target-rich) State Resting

free

Virus 0 4 7 10 13 Day

Latency- capable Virus

Cell-state Normalized Mean Intensity

0 413 7 10 Days post infection Viral Current Opinion in Cell Biology Latency

Current Opinion in Cell Biology

HIV latency establishment is largely autonomous to cellular relaxation. (a)

Two models of HIV latency regulation. The ‘epiphenomenon’ hypothesis

HIV latency: a bet-hedging strategy to optimize viral transmission. (a) of HIV latency regulation (left) predicted a strong correlation between

+ +

Upon infecting CD4 T lymphocytes, HIV can either actively replicate and cellular activation state and viral activity: as CD4 T cells relaxed from an

rapidly destroy the cell or enter a long-lived quiescent state (proviral activated state (permissive to infection) to a resting-memory state, they

latency), from which it can subsequently reactivate. The reservoir of latent would silence HIV gene expression to drive latency establishment. The

cells precludes curative therapy. (b) The evolutionary role of latency; alternate hypothesis (right) held that Tat positive-feedback circuitry is

schematic of two competing HIV strains during early infection — a sufficient to enable probabilistic bet hedging and that viral gene

hypothetical latency-incapable strain (upper) and the extant latency- expression is robust to changes in the cell state. This autonomous circuit

capable strain (lower). Infection begins with viral inoculation in the mucosa hypothesis predicts that cellular relaxation does not necessarily drive

and progresses — in some cases — to systemic infection in the lymphoid establishment of latency and that both latent and active viral expression

tissue, where >98% of CD4 T cells reside [48]. An HIV strain incapable can occur irrespective of cellular state. (b) Schematic of experiment to

of entering latency would generate increased viral loads during systemic directly test cellular dependence of latency versus autonomous viral

infection, transferring more virions to new hosts. However, the latency- programming of latency. CD4 T cells, derived from human donors, were

incapable virus would rapidly destroy the small CD4 T cell population activated to enable HIV infection, infected with a single-round HIV (env-

initially present in the mucosa of the new host — reducing the probability deleted and expressing a short-lived GFP), and cells were then allowed to

of systemic infection. In contrast, an HIV strain capable of probabilistically relax to resting memory (by removal of activation stimuli). HIV expression

entering latency (i.e., bet hedging) would generate lower viral loads during and cellular activation levels were tracked for two weeks by flow

target-rich systemic infection, transferring fewer virions to new hosts, yet, cytometry. (c) Transitioning of primary T lymphocytes from activated to

the relatively few transferred virions would not destroy all mucosal target resting did not silence HIV expression as assayed by flow cytometry

cells. By entering long-lived latency in some mucosal cells, the latency- analysis of CD25/CD69 cell-activation markers and viral GFP expression.

capable strain increases its probability of surviving initial infection to Although cells fully relax from activated to resting, viral gene expression

establish systemic infection. Calculations of the optimal bet-hedging does not silence (even despite extension of infected-cell lifetime due env

frequency for latency to constitute an evolutionary stable strategy match deletion). Data shown from duplicate infections performed on cells from

observed frequencies of latency in cell culture [26 ]. two donors (adapted from [35 ]).

www.sciencedirect.com Current Opinion in Cell Biology 2015, 37:111–118

114 Differentiation and disease

donors, activated the cells to enable HIV infection, and stimuli), and HIV expression and cellular activation levels

infected them with a single-round HIV (env-deleted) were tracked by ﬂow cytometry. Strikingly, transitioning

expressing a short-lived green ﬂuorescent protein of primary T lymphocytes from activated to resting did

(GFP) (Figure 2b). The activated cells were then allowed not silence HIV expression (Figure 2c), demonstrating

to relax to resting memory (by removing activation that HIV expression during establishment of latency was

Figure 3

(a) Tat

5′ LTR Tat ↑↓

5′ LTR Tat

(b) (c)

Tat Molecules/Cell 10–1 100 Clones OFF 101 Tat (a.u102 103 Time .)

(d)

Tat Decay 1 + Shield-1 100 No Shield-1 Ta t 80 No FKBP 60 Shield-1 + Shield-1 +Shield-1 No Shield-1 40 Fold DeclineFold in 20 ExpressionTat

Tat FKBP 4

5′ LTR % of Max (# cells) 0

0 102 103 104 105 1 4 Fold Decline in Tat (a.u.) Cell-state

Current Opinion in Cell Biology

The architecture of HIV’s minimal bet-hedging circuit. (a) Schematic of the HIV fate-selection circuit. The HIV LTR promoter fluctuates between a

transcriptional elongation-competent (ON) state and a non-elongation-competent (OFF) state [36,38]. In the ON state, Tat protein, can

transactivate the LTR to enhance transcription (by promoting elongation of RNA polymerases stalled on the LTR) comprising a positive-feedback

circuit that amplifies expression 100 fold, for review see [40]. (b) Stochastic simulations of this minimal circuit show that molecular fluctuations

can generate a phenotypic bifurcation in cellular Tat levels where Tat levels will fluctuate to zero in some cells (i.e., trajectories) but not in other

(genetically identical) cells [35 ]. (c) Experimental evidence (flow cytometry) that a minimal LTR-Tat-GFP feedback circuit is sufficient to generate

a phenotypic bifurcation in genetically identical clones [9]. Abrogating Tat positive feedback eliminates the phenotypic bifurcation both

computationally and experimentally [9,49]. (d) A synthetic Tat feedback circuit (left) validates model predictions that the minimal Tat positive-

feedback circuit is sufficient to account for the circuit turning on and off (middle) and for HIV resilience to cellular silencing (right). The FKBP

proteolysis domain causes Tat to rapidly degrade (abrogating positive feedback), but the small molecule Shield-1 inhibits FKBP-mediated

degradation and allows Tat to assume its native half-life and complete the positive feedback loop. Cellular relaxation experiment performed as in

Figure 2b but cells were transduced with the minimal-synthetic circuit and feedback was chemically modulated using Shield-1 [35 ].

Current Opinion in Cell Biology 2015, 37:111–118 www.sciencedirect.com

The HIV latency circuit Weinberger 115

resilient to cellular state and pointing to an intrinsic viral [9,24,40]. In contrast, Tat transactivation increases

program controlling the establishment of latency. LTR expression by >50-fold. This strong positive feed-

back from Tat transactivation effectively insulates the

To determine the mechanisms that might comprise the circuit from the relatively weak effects of cell-state

viral program, mathematical modeling of the HIV circuit changes and other external stimuli. The only mechanisms

was used to define the minimal core principles that could that can significantly influence the activated circuit are

enable a fate-selection decision and allow the fate deci- those that disrupt feedback, such as the large stochastic

sion to be resilient to cellular relaxation. Previous single- bursts that are intrinsic to LTR expression and that are

cell studies had determined that HIV’s long-terminal ampliﬁed by Tat feedback. The resulting Tat ﬂuctua-

repeat (LTR) promoter, the virus’s only promoter, is tions can be so extreme that Tat molecules are complete-

exceptionally noisy [36]; the dominant noise source is ly depleted, which is sufﬁcient to drive probabilistic

large, infrequent ‘bursts’ of transcription [37,38] such that switching to an off state [24,40]. Essentially, during a

the promoter can be modeled using a two-state ‘random large ﬂuctuation the system can ﬂuctuate into zero Tat

telegraph’ model [38]. It was also known that LTR noise molecules, which constitutes a ‘molecular extinction’,

is ampliﬁed by positive feedback from HIV’s Tat protein, from which there is no recovery; this is analogous to a

which transactivates the LTR [39]. Thus, the viral circuit self-replicating system (e.g. bacterial division) where a

can be modeled as a two-state promoter with positive ﬂuctuation to a population size of zero results in extinc-

feedback (Figure 3a). Stochastic simulations of this mini- tion. For the HIV circuit, transcriptional elongation from

mal Tat-feedback circuit recapitulated a phenotypic bi- the LTR promoter is so weak in many integration sites

furcation between a transcriptionally active and latent that the circuit approximates a self-replicating system:

state (Figure 3b), which matched the original experimen- Tat can only be produced when Tat is present to trans-

tal observation [9] that a minimal Tat positive-feedback activate the LTR. Overall, positive feedback is the core

circuit is sufﬁcient to generate a phenotypic bifurcation in mechanism that enables the circuit to be ﬂipped by

genetically identical cells (Figure 3c). Importantly, the stochastic transcriptional ﬂuctuations even when deter-

simulations showed that the bifurcation can occur largely ministic processes are unable to ﬂip the switch [35 ].

independent of cell state [35 ].

Two functional aspects of the circuit are worth highlight-

To experimentally test the mathematical model that Tat ing from an evolutionary perspective. First, comparable

circuitry by itself can establish HIV latency independent positive-feedback architecture had been proposed on

of cellular state, a synthetic biology approach was used theoretical grounds to be an unreliable environmental

[35 ]. A minimal synthetic Tat-feedback circuit was sensor in ﬂuctuating environments [43]. Thus, HIV’s

constructed that allowed tuning of feedback strength circuit architecture is precisely the opposite of what

through chemically modulated proteolysis (Figure 3d). would be expected for an environmental sensor that

Then, the primary-cell cellular-relaxation experiment would respond reliably to cellular changes. Second,

was repeated to determine if the minimal Tat circuit high-magnitude noise, as exhibited by the LTR, is gen-

was resilient to cellular silencing, as was the full-length erally deleterious, being selected against and ﬁltered out

virus. As predicted, when Tat feedback was abrogated — of regulatory circuits [41,42]. So, the high level of noise in

by rapid proteolysis of Tat — HIV transcription was HIV circuitry is extraordinary given the virus’s rapid

silenced as the cells became quiescent. But, when Tat evolutionary rate, supporting the concept that expression

proteolysis was chemically blocked by a small molecule noise is selectively beneﬁcial for HIV’s fate decision.

(Shield-1), Tat feedback alone was sufﬁcient to overcome Hence, viral evolution appears to have selected for cir-

the cellular silencing during relaxation to quiescence in cuitry that both maintains strong autonomy from envi-

the primary T cells (Figure 3d). The results demonstrate ronmental cues and simultaneously drives probabilistic

that Tat feedback is necessary and sufﬁcient to establish on-off decisions.

HIV latency independent of cellular silencing.

Conclusion: bet-hedging circuits as targets

How does HIV achieve this paradoxical functioning? for therapy

Speciﬁcally, how is HIV simultaneously resistant to the Stochastic noise is now considered a fundamental process

global effects of cellular silencing while sensitive enough driving diverse cell-fate decisions [11–13] and is recog-

to molecular ﬂuctuations to allow these to drive transcrip- nized as a primary clinical barrier to reversing latency in

tional switching? patients (i.e., curing HIV) [21,44,45]. Decoding of the core

fate-selection circuit in HIV recently led to development

The regulatory circuit architecture provides a mecha- of several new therapeutic avenues.

nism. Although the LTR responds to external stimuli,

the response is relatively small — strong transcriptional On the one hand, newly designed Tat-feedback inhibi-

activators (e.g., TNF, which induces changes in NFkB) tors block latent reactivation in patient samples [46 ],

generate only 2-fold changes in LTR expression demonstrating the critical role of the Tat circuit in laten-

www.sciencedirect.com Current Opinion in Cell Biology 2015, 37:111–118

116 Differentiation and disease

Figure 4

(a) Chemical Potential Epigenetic Landscape

Catalyst

Bunsen Burner ActivatorActiivvvaattooor Noise Enhancer Latent Active

(b) 2.5 Untreated d2GFP TNF, PMA LTR Noise 2.0 Enhancers Noise (85) Activator Enhancer 1.5 1.0 Noise

Δ 0.5

# Cells 0

GFP GFP –0.5

–2 –1 0 1 2

Log2(GFP Treated/GFPUntreated)

60 % Reactivation 10

0 V4 V6 V1 V5 V2 V3 V9 V7 V11 V14 V12 V13 V10

PMA only Noise Enhancers

Current Opinion in Cell Biology

Controlling bet-hedging circuitry through noise modulation. (a) Conceptual basis for noise modulation. Left: chemical-reaction efficiency is

enhanced not only by catalysts but also by increasing the thermal energy (i.e., kT in the Arrhenius equation). Although catalysts deterministically

lower activation-energy barriers on potential-energy landscapes, amplifying thermal fluctuations (e.g., via a Bunsen burner) provides an added

perturbation for crossing activation-energy barriers. Right: On a Waddington epigenetic landscape, small-molecule enhancement of stochastic

gene-expression fluctuations is analogous to increasing thermal fluctuations in chemical systems. (b) Identification of small molecules that

enhance noise in HIV gene expression. Left: LTR-GFP construct and schematic histograms of cells exposed to either a transcriptional activator or

a noise enhancer. Right: Each point represents flow cytometry analysis of 50 000 LTR-GFP-expressing cells exposed to a compound;

85 transcriptional noise enhancers identified from among 1600 FDA-approved drugs (red) are shown as in [47 ]; the effect of TNF or PMA

(activators) is shown in blue. (c) Noise-enhancer molecules (labeled V12, V13, among others) synergize with conventional activators such as PMA

(blue) and significantly modulate HIV’s fate decision by enhancing (purple) reactivation of latent HIV in donor-derived human primary T cells [47 ].

Current Opinion in Cell Biology 2015, 37:111–118 www.sciencedirect.com

The HIV latency circuit Weinberger 117

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Acknowledgements prevent HIV-1 eradication. Immunity 2012, 37:377-388.

I thank Anand Pai, Elizabeth Tanner, Noam Vardi, Brandon Razooky, Ariel 19. Whitney JB, Hill A, Sanisetty S, Penaloza-MacMaster P, Liu J,

Weinberger, and members of the UCSF and Gladstone community for Shetty M, Parenteau L, Cabral C, Shields J, Blackmore S et al.:

helpful discussion and reviews of this manuscript. This work was supported Rapid seeding of the viral reservoir prior to SIV viraemia in

rhesus monkeys. Nature 2014, 512:74-77.

by NIH awards OD017181, OD006677, AI109593 and AI109611, and by the

A study in rhesus macaques (the gold-standard model) showing that

Pew Scholars in Biomedical Sciences and the Alfred P. Sloan Foundation.

latency is established within three days of infection — long before sub-

stantial formation of resting-memory cells. During these ﬁrst few days,

latent infection of cells is a high probability event. These ﬁndings are

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