Stochastic gene expression as a molecular switch for viral latency

Stochastic Gene Expression in Proliferating Cells: Differing Noise Intensity in Single-Cell and Population Perspectives

Random fluctuations (noise) in gene expression can be studied from two complementary perspectives: following expression in a single cell over time or comparing expression between cells in a proliferating population at a given time. Here, we systematically investigated scenarios where both perspectives lead to different levels of noise in a given gene product. We first consider a stable protein, whose concentration is diluted by cellular growth, and the protein inhibits growth at high concentrations, establishing a positive feedback loop. For a stochastic model with molecular bursting of gene products, we analytically predict and contrast the steady-state distributions of protein concentration in both frameworks. Although positive feedback amplifies the noise in expression, this amplification is much higher in the population framework compared to following a single cell over time. We also study other processes that lead to different noise levels even in the absence of such dilution-based feedback. When considering randomness in the partitioning of molecules between daughters during mitosis, we find that in the single-cell perspective, the noise in protein concentration is independent of noise in the cell cycle duration. In contrast, partitioning noise is amplified in the population perspective by increasing randomness in cell-cycle time. Overall, our results show that the commonly used single-cell framework that does not account for proliferating cells can, in some cases, underestimate the noise in gene product levels. These results have important implications for studying the inter-cellular variation of different stress-related expression programs across cell types that are known to inhibit cellular growth.

An alternative interpretation for tailing in survival curves for bacteria exposed to germicidal radiation

It has been proposed that transient and reversible phenotypic changes could modify the response of bacteria to germicidal radiation, eventually leading to tailing in the survival curves. If this were the case, changes in susceptibility to radiation would reflect variations in gene expression and should only occur in cells in which gene expression is active. To obtain experimental evidence supporting the involvement of phenotypic changes in the origin of tailing, we studied changes in the susceptibility to radiation of cells able to survive high fluences, using split irradiations. Stationary phase cells of Enterobacter cloacae and Deinococcus radiodurans, in which gene expression is active, and spores of Bacillus subtilis, which are dormant cells without active gene expression, were used as microbial models. While cells of E. cloacae and D. radiodurans became susceptible after surviving exposures to high fluences, tolerant spores exhibited unchanged response to radiation. The results can be interpreted assuming that noise in gene expression modifies bacterial susceptibility to radiation, and tailing is the result of intrinsic phenomena of bacterial physiology rather than a technical artifact. For either theoretical or practical purposes, deviations from simple exponential decay kinetics should be considered in estimations of the effects of germicidal radiation at high fluences.

Phase separation reduces cell-to-cell variability of transcriptional bursting

Gene expression is a stochastic and noisy process often occurring in "bursts". Experiments have shown that the compartmentalization of proteins by liquid-liquid phase separation is conducive to reducing the noise of gene expression. Therefore, an important goal is to explore the role of bursts in phase separation noise reduction processes. We propose a coupled model that includes phase separation and a two-state gene expression process. Using the timescale separation method, we obtain approximate solutions for the expectation, variance, and noise strength of the dilute phase. We find that a higher burst frequency weakens the ability of noise reduction by phase separation, but as the burst size increases, this ability first increases and then decreases. This study provides a deeper understanding of phase separation to reduce noise in the stochastic gene expression with burst kinetics.

Transcriptional Stochasticity as a Key Aspect of HIV-1 Latency

This review summarizes current advances in the role of transcriptional stochasticity in HIV-1 latency, which were possible in a large part due to the development of single-cell approaches. HIV-1 transcription proceeds in bursts of RNA production, which stem from the stochastic switching of the viral promoter between ON and OFF states. This switching is caused by random binding dynamics of transcription factors and nucleosomes to the viral promoter and occurs at several time scales from minutes to hours. Transcriptional bursts are mainly controlled by the core transcription factors TBP, SP1 and NF-κb, the chromatin status of the viral promoter and RNA polymerase II pausing. In particular, spontaneous variability in the promoter chromatin creates heterogeneity in the response to activators such as TNF-α, which is then amplified by the Tat feedback loop to generate high and low viral transcriptional states. This phenomenon is likely at the basis of the partial and stochastic response of latent T cells from HIV-1 patients to latency-reversing agents, which is a barrier for the development of shock-and-kill strategies of viral eradication. A detailed understanding of the transcriptional stochasticity of HIV-1 and the possibility to precisely model this phenomenon will be important assets to develop more effective therapeutic strategies.

Probing transient memory of cellular states using single-cell lineages

The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria-Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.

An HIV stochastic model with cell-to-cell infection, B-cell immune response and distributed delay

In this study, a delayed HIV stochastic model with virus-to-cell infection, cell-to-cell transmission and B-cell immune response is proposed. We first transform the stochastic differential equation with distributed delay into a high-dimensional degenerate stochastic differential equation, and then theoretically analyze the dynamic behaviour of the degenerate model. The unique global solution of the model is given by rigorous analysis. By formulating suitable Lyapunov functions, the existence of the stationary Markov process is obtained if the stochastic B-cell-activated reproduction number is greater than one. We also use the law of large numbers theorem and the spectral radius analysis method to deduce that the virus can be cleared if the stochastic B-cell-inactivated reproduction number is less than one. Through uncertainty and sensitivity analysis, we obtain key parameters that determine the value of the stochastic B-cell-activated reproduction number. Numerically, we examine that low level noise can maintain the number of the virus and B-cell populations at a certain range, while high level noise is helpful for the elimination of the virus. Furthermore, the effect of the cell-to-cell infection on model behaviour, and the influence of the key parameters on the size of the stochastic B-cell-activated reproduction number are also investigated.

Dimerization induces bimodality in protein number distributions

We examined gene expression with DNA switching between two states, active and inactive. Subpopulations emerge from mechanisms that do not arise from trivial transcriptional heterogeneity. Although the RNA demonstrates a unimodal distribution, dimerization intriguingly causes protein bimodality. No control loop or deterministic bistability are present. In such a situation, increasing the degradation rate of the protein does not lead to bimodality. The bimodality is achieved through the interplay between the protein monomer and the formation of protein dimer. We applied Stochastic Simulation Algorithm (SSA) and found that cells spontaneously change states at the protein level. While sweeping parameters, decreasing the rate constant of dimerization severely impairs the bimodality. We also examined the influence of DNA switching. To have bimodality, the system requires a proper ratio of DNA in the active state to the inactive state. In addition to bimodality of the monomer, tetramerization also causes the bimodality of the dimer.

HIV-1 latency is established preferentially in minimally activated and non-dividing cells during productive infection of primary CD4 T cells

Latently infected CD4 T cells form a stable reservoir of HIV that leads to life-long viral persistence; the mechanisms involved in establishment of this latency are not well understood. Three scenarios have been proposed: 1) an activated, proliferating cell becomes infected and reverts back to a resting state; 2) an activated cell becomes infected during its return to resting; or 3) infection is established directly in a resting cell. The aim of this study was, therefore, to investigate the relationship between T cell activation and proliferation and the establishment of HIV latency. Isolated primary CD4 cells were infected at different time points before or after TCR-induced stimulation. Cell proliferation within acutely infected cultures was tracked using CFSE viable dye over 14 days; and cell subsets that underwent varying degrees of proliferation were isolated at end of culture by flow cytometric sorting. Recovered cell subpopulations were analyzed for the amount of integrated HIV DNA, and the ability to produce virus, upon a second round of cell stimulation. We show that cell cultures exposed to virus, prior to stimulus addition, contained the highest levels of integrated and replication-competent provirus after returning to quiescence; whereas, cells infected during the height of cell proliferation retained the least. Cells that did not divide or exhibited limited division, following virus exposure and stimulation contained greater amounts of integrated and inducible HIV than did cells that had divided many times. Based on these results, co-culture experiments were conducted to demonstrate that latent infection could be established directly in non-dividing cells via cell-to-cell transmission from autologous productively infected cells. Together, the findings from our studies implicate the likely importance of direct infection of sub-optimally activated T cells in establishment of latently infected reservoirs in vivo, especially in CD4 lymphocytes that surround productive viral foci within immune tissue microenvironments.

Establishment, Persistence, and Reactivation of Latent HIV-1 Infection in Renal Epithelial Cells

HIV-1 persistence in different cell types presents the main obstacle to an HIV-1 cure. We have previously shown that the renal epithelium is a site of HIV-1 infection and that the kidney represents a separate viral compartment from blood. Whether renal cells can harbor latent virus that can be reactivated upon treatment with latency reversing agents (LRAs) is unknown. To address this question, we developed an in vitro HIV-1 latency model in renal tubule epithelial (RTE) cells using a dual color HIV-1 reporter virus, R7/E-/GFP/EF1a-mCherry (R7GEmC), and evaluated the effect of LRAs, both as single agents and in combination, on viral reactivation. Our data show that HIV-1 can establish latency in RTE cells early postinfection. While the pool of latently infected cells expanded overtime, the percentage of productively infected cells declined. Following LRA treatment only a small fraction of latently infected cells, both T cells and RTE cells, could be reactivated, and the drug combinations more effective in reactivating HIV transcription in RTE cells differed from those more active in T cells. Our study demonstrates that HIV can establish latency in RTE cells and that current LRAs are only marginally effective in inducing HIV-1 reactivation. This suggests that further study of LRA dynamics in non-T cells may be warranted to assess the suitability of LRAs as a sterilizing cure strategy. IMPORTANCE Anti-retroviral therapy (ART) has dramatically reduced HIV-related morbidity and mortality. Despite this success, a number of challenges remain, including the long-term persistence of multiple, clinically latent viral reservoirs capable of reactivation in the absence of ART. As efforts proceed toward HIV eradication or functional cure, further understanding of the dynamics of HIV-1 replication, establishment of latency and mechanisms of reactivation in reservoirs harboring the virus throughout the body is necessary. HIV-1 can infect renal epithelial cells and the expression of viral genes in those cells contributes to the development of HIV associated nephropathy (HIVAN) in untreated individuals. The significance of our work is in developing the first model of HIV-1 latency in renal epithelial cells. This model enhances our understanding of HIV-1 latency and persistence in the kidney and can be used to screen candidate latency reversing agents.

Activation domains can decouple the mean and noise of gene expression

Regulatory mechanisms set a gene's average level of expression, but a gene's expression constantly fluctuates around that average. These stochastic fluctuations, or expression noise, play a role in cell-fate transitions, bet hedging in microbes, and the development of chemotherapeutic resistance in cancer. An outstanding question is what regulatory mechanisms contribute to noise. Here, we demonstrate that, for a fixed mean level of expression, strong activation domains (ADs) at low abundance produce high expression noise, while weak ADs at high abundance generate lower expression noise. We conclude that differences in noise can be explained by the interplay between a TF's nuclear concentration and the strength of its AD's effect on mean expression, without invoking differences between classes of ADs. These results raise the possibility of engineering gene expression noise independently of mean levels in synthetic biology contexts and provide a potential mechanism for natural selection to tune the noisiness of gene expression.

Droplet Microfluidics for High-Resolution Virology

Microfluidics has enabled a new era of cellular and molecular assays due to the small length scales, parallelization, and the modularity of various analysis and actuation functions. Droplet microfluidics, in particular, has been instrumental in providing new tools for biology with its ability to quickly and reproducibly generate drops that act as individual reactors. A notable beneficiary of this technology has been single-cell RNA sequencing, which has revealed new heterogeneities and interactions for the fundamental unit of life. However, viruses far surpass the diversity of cellular life, affect the dynamics of all ecosystems, and are a chronic source of global health crises. Despite their impact on the world, high-throughput and high-resolution viral profiling has been difficult, with conventional methods being limited to population-level averaging, large sample volumes, and few cultivable hosts. Consequently, most viruses have not been identified and studied. Droplet microfluidics holds the potential to address many of these limitations and offers new levels of sensitivity and throughput for virology. This Feature highlights recent efforts that have applied droplet microfluidics to the detection and study of viruses, including for diagnostics, virus-host interactions, and cell-independent virus assays. In combination with traditional virology methods, droplet microfluidics should prove a potent tool toward achieving a better understanding of the most abundant biological species on Earth.

Noise suppression in stochastic genetic circuits using PID controllers

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.

In the noisy cellular environment, biochemical species such as genes, RNAs and proteins that often occur at low molecular counts, are subject to considerable stochastic fluctuations in copy numbers over time. How cellular biochemical processes function reliably in the face of such randomness is an intriguing fundamental problem. Increasing evidence suggests that random fluctuations (noise) in protein copy numbers play important functional roles, such as driving genetically identical cells to different cell fates. Moreover, many disease states have been attributed to elevated noise levels in specific proteins. Here we systematically investigate design of biochemical systems that function as proportional, integral and derivative-based feedback controllers to suppress protein count fluctuations arising from bursty expression of the protein and external disturbance in protein synthesis. Our results show that different controllers are effective in buffering different noise components, and identify ranges of feedback gain for minimizing deleterious fluctuations in protein levels.

A transient heritable memory regulates HIV reactivation from latency

Reactivation of human immunodeficiency virus 1 (HIV-1) from latently infected T cells is a critical barrier to cure patients. It remains unknown whether reactivation of individual latent cells occurs stochastically in response to latency reversal agents (LRAs) or is a deterministic outcome of an underlying cell state. To characterize these single-cell responses, we leverage the classical Luria-Delbrück fluctuation test where single cells are isolated from a clonal population and exposed to LRAs after colony expansion. Data show considerable colony-to-colony fluctuations with the fraction of reactivating cells following a skewed distribution. Modeling systematic measurements of fluctuations over time uncovers a transient heritable memory that regulates HIV-1 reactivation, where single cells are in an LRA-responsive state for a few weeks before switching back to an irresponsive state. These results have enormous implications for designing therapies to purge the latent reservoir and further utilize fluctuation-based assays to uncover hidden transient cellular states underlying phenotypic heterogeneity.

Impact of HIV-1 Vpr manipulation of the DNA repair enzyme UNG2 on B lymphocyte class switch recombination

Background

HIV-1 Vpr encodes a 14 kDa protein that has been implicated in viral pathogenesis through modulation of several host cell functions. In addition to pro-apoptotic and cytostatic properties, Vpr can redirect cellular E3 ubiquitin ligases (such as DCAF1-Cul4A E3 ligase complex) to target many host proteins and interfere with their functions. Among them, Vpr binds the uracil DNA glycosylase UNG2, which controls genome uracilation, and induces its specific degradation leading to loss of uracil removal activity in infected cells. Considering the essential role of UNG2 in antibody diversification in B-cells, we evaluated the impact of Vpr on UNG2 fate in B lymphocytes and examined the functional consequences of UNG2 modulations on class switch recombination (CSR).

Methods

The impact of Vpr-induced UNG2 deregulation on CSR proficiency was evaluated by using virus-like particles able to deliver Vpr protein to target cells including the murine model CSR B cell line CH12F3 and mouse primary B-cells. Co-culture experiments were used to re-examine the ability of Vpr to be released by HIV-1 infected cells and to effectively accumulate in bystander B-cells. Vpr-mediated UNG2 modulations were monitored by following UNG2 protein abundance and uracil removal enzymatic activity.

Results

In this study we report the ability of Vpr to reduce immunoglobulin class switch recombination (CSR) in immortalized and primary mouse B-cells through the degradation of UNG2. We also emphasize that Vpr is released by producing cells and penetrates bystander B lymphocytes.

Conclusions

This work therefore opens up new perspectives to study alterations of the B-cell response by using Vpr as a specific CSR blocking tool. Moreover, our results raise the question of whether extracellular HIV-1 Vpr detected in some patients may manipulate the antibody diversification process that engineers an adapted response against pathogenic intruders and thereby contribute to the intrinsic B-cell humoral defect reported in infected patients.

Enhancement of gene expression noise from transcription factor binding to genomic decoy sites

The genome contains several high-affinity non-functional binding sites for transcription factors (TFs) creating a hidden and unexplored layer of gene regulation. We investigate the role of such “decoy sites” in controlling noise (random fluctuations) in the level of a TF that is synthesized in stochastic bursts. Prior studies have assumed that decoy-bound TFs are protected from degradation, and in this case decoys function to buffer noise. Relaxing this assumption to consider arbitrary degradation rates for both bound/unbound TF states, we find rich noise behaviors. For low-affinity decoys, noise in the level of unbound TF always monotonically decreases to the Poisson limit with increasing decoy numbers. In contrast, for high-affinity decoys, noise levels first increase with increasing decoy numbers, before decreasing back to the Poisson limit. Interestingly, while protection of bound TFs from degradation slows the time-scale of fluctuations in the unbound TF levels, the decay of bound TFs leads to faster fluctuations and smaller noise propagation to downstream target proteins. In summary, our analysis reveals stochastic dynamics emerging from nonspecific binding of TFs and highlights the dual role of decoys as attenuators or amplifiers of gene expression noise depending on their binding affinity and stability of the bound TF.

Nonequilibrium Thermodynamics in Cell Biology: Extending Equilibrium Formalism to Cover Living Systems

We discuss new developments in the nonequilibrium dynamics and thermodynamics of living systems, giving a few examples to demonstrate the importance of nonequilibrium thermodynamics for understanding biological dynamics and functions. We study single-molecule enzyme dynamics, in which the nonequilibrium thermodynamic and dynamic driving forces of chemical potential and flux are crucial for the emergence of non-Michaelis-Menten kinetics. We explore single-gene expression dynamics, in which nonequilibrium dissipation can suppress fluctuations. We investigate the cell cycle and identify the nutrition supply as the energy input that sustains the stability, speed, and coherence of cell cycle oscillation, from which the different vital phases of the cell cycle emerge. We examine neural decision-making processes and find the trade-offs among speed, accuracy, and thermodynamic costs that are important for neural function. Lastly, we consider the thermodynamic cost for specificity in cellular signaling and adaptation.

Bacterial Persisters and Infection: Past, Present, and Progressing

Persisters are nongrowing, transiently antibiotic-tolerant bacteria within a clonal population of otherwise susceptible cells. Their formation is triggered by environmental cues and involves the main bacterial stress response pathways that allow persisters to survive many harsh conditions, including antibiotic exposure. During infection, bacterial pathogens are exposed to a vast array of stresses in the host and form nongrowing persisters that survive both antibiotics and host immune responses, thereby most likely contributing to the relapse of many infections. While antibiotic persisters have been extensively studied over the last decade, the bulk of the work has focused on how these bacteria survive exposure to drugs in vitro. The ability of persisters to survive their interaction with a host is important yet underinvestigated. In order to tackle the problem of persistence of infections that contribute to the worldwide antibiotic resistance crisis, efforts should be made by scientific communities to understand and merge these two fields of research: antibiotic persisters and host-pathogen interactions. Here we give an overview of the history of the field of antibiotic persistence, report evidence for the importance of persisters in infection, and highlight studies that bridge the two areas.

Host-virus evolutionary dynamics with specialist and generalist infection strategies: Bifurcations, bistability, and chaos

In this work, we have investigated the evolutionary dynamics of a generalist pathogen, e.g., a virus population, that evolves toward specialization in an environment with multiple host types. We have particularly explored under which conditions generalist viral strains may rise in frequency and coexist with specialist strains or even dominate the population. By means of a nonlinear mathematical model and bifurcation analysis, we have determined the theoretical conditions for stability of nine identified equilibria and provided biological interpretation in terms of the infection rates for the viral specialist and generalist strains. By means of a stability diagram, we identified stable fixed points and stable periodic orbits, as well as regions of bistability. For arbitrary biologically feasible initial population sizes, the probability of evolving toward stable solutions is obtained for each point of the analyzed parameter space. This probability map shows combinations of infection rates of the generalist and specialist strains that might lead to equal chances for each type becoming the dominant strategy. Furthermore, we have identified infection rates for which the model predicts the onset of chaotic dynamics. Several degenerate Bogdanov-Takens and zero-Hopf bifurcations are detected along with generalized Hopf and zero-Hopf bifurcations. This manuscript provides additional insights into the dynamical complexity of host-pathogen evolution toward different infection strategies.

Tat-Based Therapies as an Adjuvant for an HIV-1 Functional Cure

The human immunodeficiency virus type 1 (HIV) establishes a chronic infection that can be well controlled, but not cured, by combined antiretroviral therapy (cART). Interventions have been explored to accomplish a functional cure, meaning that a patient remains infected but HIV is undetectable in the blood, with the aim of allowing patients to live without cART. Tat, the viral transactivator of transcription protein, plays a critical role in controlling HIV transcription, latency, and viral rebound following the interruption of cART treatment. Therefore, a logical approach for controlling HIV would be to block Tat. Tackling Tat with inhibitors has been a difficult task, but some recent discoveries hold promise. Two anti-HIV proteins, Nullbasic (a mutant of Tat) and HT1 (a fusion of HEXIM1 and Tat functional domains) inhibit viral transcription by interfering with the interaction of Tat and cellular factors. Two small molecules, didehydro-cortistatin A (dCA) and triptolide, inhibit Tat by different mechanisms: dCA through direct binding and triptolide through enhanced proteasomal degradation. Finally, two Tat-based vaccines under development elicit Tat-neutralizing antibodies. These vaccines have increased the levels of CD4⁺ cells and reduced viral loads in HIV-infected people, suggesting that the new vaccines are therapeutic. This review summarizes recent developments of anti-Tat agents and how they could contribute to a functional cure for HIV.

Differences in Transcriptional Dynamics Between T-cells and Macrophages as Determined by a Three-State Mathematical Model

HIV-1 viral transcription persists in patients despite antiretroviral treatment, potentially due to intermittent HIV-1 LTR activation. While several mathematical models have been explored in the context of LTR-protein interactions, in this work for the first time HIV-1 LTR model featuring repressed, intermediate, and activated LTR states is integrated with generation of long (env) and short (TAR) RNAs and proteins (Tat, Pr55, and p24) in T-cells and macrophages using both cell lines and infected primary cells. This type of extended modeling framework allows us to compare and contrast behavior of these two cell types. We demonstrate that they exhibit unique LTR dynamics, which ultimately results in differences in the magnitude of viral products generated. One of the distinctive features of this work is that it relies on experimental data in reaction rate computations. Two RNA transcription rates from the activated promoter states are fit by comparison of experimental data to model predictions. Fitting to the data also provides estimates for the degradation/exit rates for long and short viral RNA. Our experimentally generated data is in reasonable agreement for the T-cell as well macrophage population and gives strong evidence in support of using the proposed integrated modeling paradigm. Sensitivity analysis performed using Latin hypercube sampling method confirms robustness of the model with respect to small parameter perturbations. Finally, incorporation of a transcription inhibitor (F07#13) into the governing equations demonstrates how the model can be used to assess drug efficacy. Collectively, our model indicates transcriptional differences between latently HIV-1 infected T-cells and macrophages and provides a novel platform to study various transcriptional dynamics leading to latency or activation in numerous cell types and physiological conditions.

Nuclear Transit and HIV LTR Binding of NF-κB Subunits Held by IκB Proteins: Implications for HIV-1 Activation

No effective therapy to eliminate the HIV latently infected cell reservoir has been developed. One approach, “shock and kill”, employs agents that activate HIV, subsequently killing the activated infected cells and/or virus. Shock and kill requires agents that safely and effectively activate HIV. One class of activation agents works through classical NF-κB pathways, but global NF-κB activators are non-specific and toxic. There exist two major IκBs: IκBα, and IκBε, which hold activating NF-κB subunits in the cytoplasm, releasing them for nuclear transit upon cell stimulation. IκBα was considered the main IκB responsible for gene expression regulation, including HIV activation. IκBε is expressed in cells constituting much of the latent HIV reservoir, and IκBε knockout mice have a minimal phenotype, suggesting that IκBε could be a valuable target for HIV activation and reservoir depletion. We previously showed that targeting IκBε yields substantial increases in HIV expression. Here, we show that IκBε holds c-Rel and p65 activating NF-κB subunits in the cytoplasm, and that targeting IκBε with siRNA produces a strong increase in HIV expression associated with enhanced c-Rel and p65 transit to the nucleus and binding to the HIV LTR of the activating NF-κBs, demonstrating a mechanism through which targeting IκBε increases HIV expression. The findings suggest that it may be helpful to develop HIV activation approaches, acting specifically to target IκBε and its interactions with the NF-κBs.

Kinetics of HTLV-1 reactivation from latency quantified by single-molecule RNA FISH and stochastic modelling

The human T cell leukemia virus HTLV-1 establishes a persistent infection in vivo in which the viral sense-strand transcription is usually silent at a given time in each cell. However, cellular stress responses trigger the reactivation of HTLV-1, enabling the virus to transmit to a new host cell. Using single-molecule RNA FISH, we measured the kinetics of the HTLV-1 transcriptional reactivation in peripheral blood mononuclear cells (PBMCs) isolated from HTLV-1+ individuals. The abundance of the HTLV-1 sense and antisense transcripts was quantified hourly during incubation of the HTLV-1-infected PBMCs ex vivo. We found that, in each cell, the sense-strand transcription occurs in two distinct phases: the initial low-rate transcription is followed by a phase of rapid transcription. The onset of transcription peaked between 1 and 3 hours after the start of in vitro incubation. The variance in the transcription intensity was similar in polyclonal HTLV-1+ PBMCs (with tens of thousands of distinct provirus insertion sites), and in samples with a single dominant HTLV-1+ clone. A stochastic simulation model was developed to estimate the parameters of HTLV-1 proviral transcription kinetics. In PBMCs from a leukemic subject with one dominant T-cell clone, the model indicated that the average duration of HTLV-1 sense-strand activation by Tax (i.e. the rapid transcription) was less than one hour. HTLV-1 antisense transcription was stable during reactivation of the sense-strand. The antisense transcript HBZ was produced at an average rate of ~0.1 molecules per hour per HTLV-1+ cell; however, between 20% and 70% of HTLV-1-infected cells were HBZ-negative at a given time, the percentage depending on the individual subject. HTLV-1-infected cells are exposed to a range of stresses when they are drawn from the host, which initiate the viral reactivation. We conclude that whereas antisense-strand transcription is stable throughout the stress response, the HTLV-1 sense-strand reactivation is highly heterogeneous and occurs in short, self-terminating bursts.

Insights into the HIV Latency and the Role of Cytokines

Human immunodeficiency virus-1 (HIV-1) has the ability to infect latently at the level of individual CD4+ cells. Latent HIV-1 proviruses are transcriptionally silent and immunologically inert, but are still capable of reactivating productive lytic infection following cellular activation. These latent viruses are the main obstacle in the eradication of HIV-1, because current HIV-1 treatment regimens are ineffective against them. Normal immunological response against an antigen activates CD4+ naïve T cells. The activated CD4+ naïve T cells undergo cell cycle, resulting in further transformation and profound proliferation to form effector CD4+ T-cells. Notably, in HIV-1 infected individuals, some of the effector CD4+ T cells get infected with HIV-1. Upon fulfillment of their effector functions, almost all activated CD4+ T cells are committed to apoptosis or programmed cell death, but a miniscule fraction revert to quiescence and become resting memory CD4+ T cells to mediate a rapid immunological response against the same antigen in the future. However, due to the quiescent nature of the resting memory T cells, the integrated HIV-1 becomes transcriptionally silent and acquires a latent phenotype. Following re-exposure to the same antigen, memory cells and integrated HIV-1 are stimulated. The reactivated latent HIV provirus subsequently proceeds through its life cycle and eventually leads to the production of new viral progeny. Recently, many strategies against HIV-1 latency have been developed and some of them have even matured to the clinical level, but none can yet effectively eliminate the latent HIV reservoir, which remains a barrier to HIV-1 cure. Therefore, alternative strategies to eradicate latent HIV need to be considered. This review provides vital knowledge on HIV latency and on strategies to supplement highly active anti-retroviral therapy (HAART) with cytokine-mediated therapeutics for dislodging the latent HIV reservoirs in order to open up new avenues for curing HIV.

The Glucocorticoid Receptor Is a Critical Regulator of HIV Latency in Human Microglial Cells

We have developed models of HIV latency using microglia derived from adult human patient brain cortex and transformed with the SV40 T large and hTERT antigens. Latent clones infected by HIV reporter viruses display high levels of spontaneous HIV reactivation in culture. BrainPhys, a medium highly representative of the CNS extracellular environment, containing low glucose and 1% FBS, reduced, but did not prevent, HIV reactivation. We hypothesized that spontaneous HIV reactivation in culture was due to the expression of pro-inflammatory genes, such as TNF-α, taking place in the absence of the natural inhibitory signals from astrocytes and neurons. Indeed, expression and secretion of TNF-α is strongly reduced in HIV-latently infected microglia compared to the subset of cells that have undergone spontaneous HIV reactivation. Whereas inhibitors of NF-κB or of macrophage activation only had a short-term silencing effect, addition of dexamethasone (DEXA), a glucocorticoid receptor (GR) agonist and mediator of anti-inflammation, silenced the HIV provirus in a long-term, and shRNA-mediated knock-down of GR activated HIV. DEXA also decreased secretion of a number of cytokines, including TNF-α. Chromatin immunoprecipitation analysis revealed that DEXA strongly increased GR occupancy at the HIV promoter, and reduced histone 3 acetylated levels. Moreover, TNF-α expression inhibitors in combination with DEXA induced further HIV silencing and increased the histone 3 lysine 27 tri-methylated epigenetic mark of repression at the HIV promoter region. We conclude that GR is a critical repressor of HIV transcription in microglia, and a novel potential pharmacological target to restrict HIV expression in the CNS.

The Insight into Protein-Ligand Interactions, a Novel Way of Buffering Protein Noise in Gene Expression

Random fluctuations are often considered detrimental in the context of gene regulation. Studies aimed at discovering the noise-buffering strategies are important. In this study, we demonstrated a novel design of attenuating noise at protein-level. The protein-ligand interaction dramatically reduced noise so that the coefficient of variation (COV) became roughly 1/3. Remarkably, in comparison to the other two noise-buffering methods, the negative feedback control and the incoherent feedforward loop, the COV of the target protein in the case of protein-ligand interaction appeared to be less than 1/2 of that of the other two methods. The high correlation of the target protein and the ligand grants the present method great ability to buffer noise. Further, it buffers noise at the stage after translation so it is also capable of attenuating the noise inherited from the process of translation.

The mobility of packaged phage genome controls ejection dynamics

The cell decision between lytic and lysogenic infection is strongly influenced by dynamics of DNA injection into a cell from a phage population, as phages compete for limited resources and progeny. However, what controls the timing of viral DNA ejection events was not understood. This in vitro study reveals that DNA ejection dynamics for phages can be synchronized (occurring within seconds) or desynchronized (displaying minutes-long delays in initiation) based on mobility of encapsidated DNA, which in turn is regulated by environmental factors, such as temperature and extra-cellular ionic conditions. This mechano-regulation of ejection dynamics is suggested to influence viral replication where the cell’s decision between lytic and latent infection is associated with synchronized or desynchronized delayed ejection events from phage population adsorbed to a cell. Our findings are of significant importance for understanding regulatory mechanisms of latency in phage and Herpesviruses, where encapsidated DNA undergoes a similar mechanical transition.

Viruses are tiny ‘parasites’ that smuggle their genetic material inside a cell and then hijack its resources for their own benefit. A viral infection can either be lytic or latent. In a lytic cycle, viruses make their host produce many copies of themselves, ultimately killing the cell. In contrast, during a latent infection, the viruses go ‘dormant’: for instance, some of them can insert their genetic material into the DNA of their host, which then gets passed on as the cell divides.

Certain viruses are capable of both lytic and latent infections. One example is the lambda phage, which targets Escherichia coli bacteria. In the first stage of infection, the genetic material ‘shoots out’ of the virus and gets injected inside the bacterium. The dynamics of the ejection process determine the type of infection that will follow. If multiple phages release their genomes quickly and within seconds of each other into the same cell, the bacterium tends to incorporate the viral DNA into its own genome, leading to a latent cycle. If the infections take place more slowly and not all at the same time, the cell is more likely to go through a lytic phase. However, the mechanism behind the different injection behaviors is still unknown; in particular, it is unclear which factors control the specificities of the ejection process in the first place.

Here, Alex Evilevitch demonstrates that the mechanical state of the phage DNA just before ejection dictates how the genetic material will then be injected in the bacteria. The experiments measured the stiffness of the DNA and the amount of heat given off during infection. Like fluid toothpaste, if the DNA is more liquid and flexible, it gets ejected quickly and simultaneously from several phages. Then, the genetic information of these viruses can be incorporated in the genome of the bacteria. On the other hand, if the DNA is more solid, it is likely to ‘stick’ and take time before it can be squeezed out: the injections become unsynchronised, which leads to a lytic phase.

Evilevitch then shows that the environment can influence the properties of the phages’ genome. A little more heat, or certain chemicals, can make the DNA more fluid inside the viruses, and change the way it can be injected inside the bacteria.

Many viruses that cause diseases in humans – from cold sores to glandular fever – can switch between the lytic and latent cycles. For the first time, these results show that the mechanical properties of the DNA inside a virus influence the ‘decision’ between the two types of infection. This knowledge could help us prevent infections from becoming lytic and ultimately allow us to control the spread of disease.

Synaptic transmission may provide an evolutionary benefit to HIV through modulation of latency

Transmission of HIV is known to occur by two mechanisms in vivo: the free virus pathway, where viral particles bud off an infected cell before attaching to an uninfected cell, and the cell-cell pathway, where infected cells form virological synapses through close contact with an uninfected cell. It has also been shown that HIV replication includes a positive feedback loop controlled by the viral protein Tat, which may act as a stochastic switch in determining whether an infected cell enters latency. In this paper, we introduce a simple mathematical model of HIV replication containing both the free virus and cell-cell pathways. Using this model, we demonstrate that the high multiplicity of infection in cell-cell transmission results in a suppression of latent infection, and that this modulation of latency through balancing the two transmission mechanisms can provide an evolutionary benefit to the virus. This benefit increases with decreasing overall viral fitness, which may provide a within-host evolutionary pressure toward more cell-cell transmission in late-stage HIV infection.

Fate-Regulating Circuits in Viruses: From Discovery to New Therapy Targets

Current antivirals effectively target diverse viruses at various stages of their life cycles. Nevertheless, curative therapy has remained elusive for important pathogens, such as human immunodeficiency virus type 1 (HIV-1) and herpesviruses, in large part due to viral latency and the evolution of resistance to existing therapies. Here, we review the discovery of viral master circuits: virus-encoded autoregulatory gene networks that autonomously control viral expression programs (i.e., between active, latent, and abortive fates). These circuits offer the opportunity for a new class of antivirals that could lead to intrinsic combination-antiviral therapies within a single molecule-evolutionary escape from such circuit-disrupting antivirals would require simultaneous evolution of both the viral cis regulatory element (e.g., the DNA-binding site) and the trans element (e.g., the transcription factor) in order for the virus to recapitulate a circuit that would not be disrupted. We review the architectures of these fate-regulating master circuits in HIV-1 and the human herpesvirus cytomegalovirus along with potential circuit-disruption strategies that may ultimately enable escape-resistant antiviral therapies.

The evolutionary advantage of heritable phenotypic heterogeneity

Phenotypic plasticity is an evolutionary driving force in diverse biological processes, including the adaptive immune system, the development of neoplasms, and the persistence of pathogens despite drug pressure. It is essential, therefore, to understand the evolutionary advantage of an allele that confers on cells the ability to express a range of phenotypes. Here, we study the fate of a new mutation that allows the expression of multiple phenotypic states, introduced into a finite population of individuals that can express only a single phenotype. We show that the advantage of such a mutation depends on the degree of phenotypic heritability between generations, called phenotypic memory. We analyze the fixation probability of the phenotypically plastic allele as a function of phenotypic memory, the variance of expressible phenotypes, the rate of environmental changes, and the population size. We find that the fate of a phenotypically plastic allele depends fundamentally on the environmental regime. In constant environments, plastic alleles are advantageous and their fixation probability increases with the degree of phenotypic memory. In periodically fluctuating environments, by contrast, there is an optimum phenotypic memory that maximizes the probability of the plastic allele's fixation. This same optimum memory also maximizes geometric mean fitness, in steady state. We interpret these results in the context of previous studies in an infinite-population framework. We also discuss the implications of our results for the design of therapies that can overcome persistence and, indirectly, drug resistance.

Inhibitors Alter the Stochasticity of Regulatory Proteins to Force Cells to Switch to the Other State in the Bistable System

The cellular behaviors under the control of genetic circuits are subject to stochastic fluctuations, or noise. The stochasticity in gene regulation, far from a nuisance, has been gradually appreciated for its unusual function in cellular activities. In this work, with Chemical Master Equation (CME), we discovered that the addition of inhibitors altered the stochasticity of regulatory proteins. For a bistable system of a mutually inhibitory network, such a change of noise led to the migration of cells in the bimodal distribution. We proposed that the consumption of regulatory protein caused by the addition of inhibitor is not the only reason for pushing cells to the specific state; the change of the intracellular stochasticity is also the main cause for the redistribution. For the level of the inhibitor capable of driving 99% of cells, if there is no consumption of regulatory protein, 88% of cells were guided to the specific state. It implied that cells were pushed, by the inhibitor, to the specific state due to the change of stochasticity.

Effect of transcription factor resource sharing on gene expression noise

Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it’s TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression.

Genetically identical cells, even when they are exposed to the same environmental conditions, display incredible diversity. Gene expression noise is attributed to be a key source of this phenotypic diversity. Transcriptional dynamics is a dominant source of expression noise. Although scores of theoretical and experimental studies have explored how noise is regulated at the level of transcription, most of them focus on the gene specific, cis regulatory elements, such as the number of transcription factor (TF) binding sites, their binding strength, etc. However, how the global properties of transcription, such as the limited availability of TFs impact noise in gene expression remains rather elusive. Here we build a theoretical model that incorporates the effect of limiting TF pool on gene expression noise. We find that competition between genes for TFs leads to enhanced variability in mRNA copy number across an isogenic population. Moreover, for gene copies sharing TFs with other competitor sites, mRNA variance as a function of the mean shows distinct imprints for one gene copy and multiple gene copies respectively. This stands in sharp contrast to the universal behavior found in mean expression irrespective of the different scenarios of competition. An interesting feature of competition is that introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution, which could lead to phenotypic variability.

Driving Cells to the Desired State in a Bimodal Distribution through Manipulation of Internal Noise with Biologically Practicable Approaches

The stochastic nature of gene regulatory networks described by Chemical Master Equation (CME) leads to the distribution of proteins. A deterministic bistability is usually reflected as a bimodal distribution in stochastic simulations. Within a certain range of the parameter space, a bistable system exhibits two stable steady states, one at the low end and the other at the high end. Consequently, it appears to have a bimodal distribution with one sub-population (mode) around the low end and the other around the high end. In most cases, only one mode is favorable, and guiding cells to the desired state is valuable. Traditionally, the population was redistributed simply by adjusting the concentration of the inducer or the stimulator. However, this method has limitations; for example, the addition of stimulator cannot drive cells to the desired state in a common bistable system studied in this work. In fact, it pushes cells only to the undesired state. In addition, it causes a position shift of the modes, and this shift could be as large as the value of the mode itself. Such a side effect might damage coordination, and this problem can be avoided by applying a new method presented in this work. We illustrated how to manipulate the intensity of internal noise by using biologically practicable methods and utilized it to prompt the population to the desired mode. As we kept the deterministic behavior untouched, the aforementioned drawback was overcome. Remarkably, more than 96% of cells has been driven to the desired state. This method is genetically applicable to biological systems exhibiting a bimodal distribution resulting from bistability. Moreover, the reaction network studied in this work can easily be extended and applied to many other systems.

Effects of cell-cycle-dependent expression on random fluctuations in protein levels

Expression of many genes varies as a cell transitions through different cell-cycle stages. How coupling between stochastic expression and cell cycle impacts cell-to-cell variability (noise) in the level of protein is not well understood. We analyse a model where a stable protein is synthesized in random bursts, and the frequency with which bursts occur varies within the cell cycle. Formulae quantifying the extent of fluctuations in the protein copy number are derived and decomposed into components arising from the cell cycle and stochastic processes. The latter stochastic component represents contributions from bursty expression and errors incurred during partitioning of molecules between daughter cells. These formulae reveal an interesting trade-off: cell-cycle dependencies that amplify the noise contribution from bursty expression also attenuate the contribution from partitioning errors. We investigate the existence of optimum strategies for coupling expression to the cell cycle that minimize the stochastic component. Intriguingly, results show that a zero production rate throughout the cell cycle, with expression only occurring just before cell division, minimizes noise from bursty expression for a fixed mean protein level. By contrast, the optimal strategy in the case of partitioning errors is to make the protein just after cell division. We provide examples of regulatory proteins that are expressed only towards the end of the cell cycle, and argue that such strategies enhance robustness of cell-cycle decisions to the intrinsic stochasticity of gene expression.

Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion

We seek to elucidate the role of macromolecular crowding in transcription and translation. It is well known that stochasticity in gene expression can lead to differential gene expression and heterogeneity in a cell population. Recent experimental observations by Tan et al. have improved our understanding of the functional role of macromolecular crowding. It can be inferred from their observations that macromolecular crowding can lead to robustness in gene expression, resulting in a more homogeneous cell population. We introduce a spatial stochastic model to provide insight into this process. Our results show that macromolecular crowding reduces noise (as measured by the kurtosis of the mRNA distribution) in a cell population by limiting the diffusion of transcription factors (i.e. removing the unstable intermediate states), and that crowding by large molecules reduces noise more efficiently than crowding by small molecules. Finally, our simulation results provide evidence that the local variation in chromatin density as well as the total volume exclusion of the chromatin in the nucleus can induce a homogenous cell population.

The cellular nucleus is packed with macromolecules such as DNAs and proteins, which leaves limited space for other molecules to move around. Recent experimental results by C. Tan et al. have shown that macromolecular crowding can regulate gene expression, resulting in a more homogenous cell population. We introduce a computational model to uncover the mechanism by which macromolecular crowding functions. Our results suggest that macromolecular crowding limits the diffusion of the transcription factors and attenuates the transcriptional bursting, which leads to a more homogenous cell population. Regulation of gene expression noise by macromolecules depends on the size of the crowders, i.e. larger macromolecules can reduce the noise more effectively than smaller macromolecules. We also demonstrate that local variation of chromatin density can affect the noise of gene expression. This shows the importance of the chromatin structure in gene expression regulation.

Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes

Inside individual cells, expression of genes is inherently stochastic and manifests as cell-to-cell variability or noise in protein copy numbers. Since proteins half-lives can be comparable to the cell-cycle length, randomness in cell-division times generates additional intercellular variability in protein levels. Moreover, as many mRNA/protein species are expressed at low-copy numbers, errors incurred in partitioning of molecules between two daughter cells are significant. We derive analytical formulas for the total noise in protein levels when the cell-cycle duration follows a general class of probability distributions. Using a novel hybrid approach the total noise is decomposed into components arising from i) stochastic expression; ii) partitioning errors at the time of cell division and iii) random cell-division events. These formulas reveal that random cell-division times not only generate additional extrinsic noise, but also critically affect the mean protein copy numbers and intrinsic noise components. Counter intuitively, in some parameter regimes, noise in protein levels can decrease as cell-division times become more stochastic. Computations are extended to consider genome duplication, where transcription rate is increased at a random point in the cell cycle. We systematically investigate how the timing of genome duplication influences different protein noise components. Intriguingly, results show that noise contribution from stochastic expression is minimized at an optimal genome-duplication time. Our theoretical results motivate new experimental methods for decomposing protein noise levels from synchronized and asynchronized single-cell expression data. Characterizing the contributions of individual noise mechanisms will lead to precise estimates of gene expression parameters and techniques for altering stochasticity to change phenotype of individual cells.

Inside individual cells, gene products often occur at low molecular counts and are subject to considerable stochastic fluctuations (noise) in copy numbers over time. An important consequence of noisy expression is that the level of a protein can vary considerably even among genetically identical cells exposed to the same environment. Such non-genetic phenotypic heterogeneity is physiologically relevant and critically influences diverse cellular processes. In addition to noise sources inherent in gene product synthesis, recent experimental studies have uncovered additional noise mechanisms that critically effect expression. For example, the time within the cell cycle when a gene duplicates, and the time taken to complete cell cycle are governed by random processes. The key contribution of this work is development of novel mathematical results quantifying how cell cycle-related noise sources combine with stochastic expression to drive intercellular variability in protein molecular counts. Derived formulas lead to many counterintuitive results, such as increasing randomness in the timing of cell division can lower noise in the level of a protein. Finally, these results inform experimental strategies to systematically dissect the contributions of different noise sources in the expression of a gene of interest.

Gene expression variability in clonal populations: Causes and consequences

During the last decade it has been shown that among cell variation in gene expression plays an important role within clonal populations. Here, we provide an overview of the different mechanisms contributing to gene expression variability in clonal populations. These are ranging from inherent variations in the biochemical process of gene expression itself, such as intrinsic noise, extrinsic noise and bistability to individual responses to variations in the local micro-environment, a phenomenon called phenotypic plasticity. Also genotypic variations caused by clonal evolution and phase variation can contribute to gene expression variability. Consequently, gene expression studies need to take these fluctuations in expression into account. However, frequently used techniques for expression quantification, such as microarrays, RNA sequencing, quantitative PCR and gene reporter fusions classically determine the population average of gene expression. Here, we discuss how these techniques can be adapted towards single cell analysis by integration with single cell isolation, RNA amplification and microscopy. Alternatively more qualitative selection-based techniques, such as mutant screenings, in vivo expression technology (IVET) and recombination-based IVET (RIVET) can be applied for detection of genes expressed only within a subpopulation. Finally, differential fluorescence induction (DFI), a protocol specially designed for single cell expression is discussed.

Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences

This article reviews the production of different phenotypes from the same genotype in the same environment by stochastic cellular events, nonlinear mechanisms during patterning and morphogenesis, and probabilistic self-reinforcing circuitries in the adult life. These aspects of phenotypic variation are summarized under the term 'stochastic developmental variation' (SDV) in the following. In the past, SDV has been viewed primarily as a nuisance, impairing laboratory experiments, pharmaceutical testing, and true-to-type breeding. This article also emphasizes the positive biological effects of SDV and discusses implications for genotype-to-phenotype mapping, biological individuation, ecology, evolution, and applied biology. There is strong evidence from experiments with genetically identical organisms performed in narrowly standardized laboratory set-ups that SDV is a source of phenotypic variation in its own right aside from genetic variation and environmental variation. It is obviously mediated by molecular and higher-order epigenetic mechanisms. Comparison of SDV in animals, plants, fungi, protists, bacteria, archaeans, and viruses suggests that it is a ubiquitous and phylogenetically old phenomenon. In animals, it is usually smallest for morphometric traits and highest for life history traits and behaviour. SDV is thought to contribute to phenotypic diversity in all populations but is particularly relevant for asexually reproducing and genetically impoverished populations, where it generates individuality despite genetic uniformity. In each generation, SDV produces a range of phenotypes around a well-adapted target phenotype, which is interpreted as a bet-hedging strategy to cope with the unpredictability of dynamic environments. At least some manifestations of SDV are heritable, adaptable, selectable, and evolvable, and therefore, SDV may be seen as a hitherto overlooked evolution factor. SDV is also relevant for husbandry, agriculture, and medicine because most pathogens are asexuals that exploit this third source of phenotypic variation to modify infectivity and resistance to antibiotics. Since SDV affects all types of organisms and almost all aspects of life, it urgently requires more intense research and a better integration into biological thinking.

Distinct promoter activation mechanisms modulate noise-driven HIV gene expression

Latent human immunodeficiency virus (HIV) infections occur when the virus occupies a transcriptionally silent but reversible state, presenting a major obstacle to cure. There is experimental evidence that random fluctuations in gene expression, when coupled to the strong positive feedback encoded by the HIV genetic circuit, act as a ‘molecular switch’ controlling cell fate, i.e., viral replication versus latency. Here, we implemented a stochastic computational modeling approach to explore how different promoter activation mechanisms in the presence of positive feedback would affect noise-driven activation from latency. We modeled the HIV promoter as existing in one, two, or three states that are representative of increasingly complex mechanisms of promoter repression underlying latency. We demonstrate that two-state and three-state models are associated with greater variability in noisy activation behaviors, and we find that Fano factor (defined as variance over mean) proves to be a useful noise metric to compare variability across model structures and parameter values. Finally, we show how three-state promoter models can be used to qualitatively describe complex reactivation phenotypes in response to therapeutic perturbations that we observe experimentally. Ultimately, our analysis suggests that multi-state models more accurately reflect observed heterogeneous reactivation and may be better suited to evaluate how noise affects viral clearance.

HIV-1 Eradication: Early Trials (and Tribulations)

Antiretroviral therapy (ART) has rendered HIV-1 infection a manageable illness for those with access to treatment. However, ART does not lead to viral eradication owing to the persistence of replication-competent, unexpressed proviruses in long-lived cellular reservoirs. The potential for long-term drug toxicities and the lack of access to ART for most people living with HIV-1 infection have fueled scientific interest in understanding the nature of this latent reservoir. Exploration of HIV-1 persistence at the cellular and molecular level in resting memory CD4(+) T cells, the predominant viral reservoir in patients on ART, has uncovered potential strategies to reverse latency. We review recent advances in pharmacologically based 'shock and kill' HIV-1 eradication strategies, including comparative analysis of early clinical trials.

A minimal fate-selection switch

To preserve fitness in unpredictable, fluctuating environments, a range of biological systems probabilistically generate variant phenotypes--a process often referred to as 'bet-hedging', after the financial practice of diversifying assets to minimize risk in volatile markets. The molecular mechanisms enabling bet-hedging have remained elusive. Here, we review how HIV makes a bet-hedging decision between active replication and proviral latency, a long-lived dormant state that is the chief barrier to an HIV cure. The discovery of a virus-encoded bet-hedging circuit in HIV revealed an ancient evolutionary role for latency and identified core regulatory principles, such as feedback and stochastic 'noise', that enable cell-fate decisions. These core principles were later extended to fate selection in stem cells and cancer, exposed new therapeutic targets for HIV, and led to a potentially broad strategy of using 'noise modulation' to redirect cell fate.

Designing and Interpreting Limiting Dilution Assays: General Principles and Applications to the Latent Reservoir for Human Immunodeficiency Virus-1

Limiting dilution assays are widely used in infectious disease research. These assays are crucial for current HIV-1 cure research in particular. Here we offer new tools to help investigators design and analyze dilution assays based on their specific research needs.

Limiting dilution assays are widely used in infectious disease research. These assays are crucial for current human immunodeficiency virus (HIV)-1 cure research in particular. In this study, we offer new tools to help investigators design and analyze dilution assays based on their specific research needs. Limiting dilution assays are commonly used to measure the extent of infection, and in the context of HIV they represent an essential tool for studying latency and potential curative strategies. Yet standard assay designs may not discern whether an intervention reduces an already miniscule latent infection. This review addresses challenges arising in this setting and in the general use of dilution assays. We illustrate the major statistical method for estimating frequency of infectious units from assay results, and we offer an online tool for computing this estimate. We recommend a procedure for customizing assay design to achieve desired sensitivity and precision goals, subject to experimental constraints. We consider experiments in which no viral outgrowth is observed and explain how using alternatives to viral outgrowth may make measurement of HIV latency more efficient. Finally, we discuss how biological complications, such as probabilistic growth of small infections, alter interpretations of experimental results.

Bistability: requirements on cell-volume, protein diffusion, and thermodynamics

Bistability is considered wide-spread among bacteria and eukaryotic cells, useful e.g. for enzyme induction, bet hedging, and epigenetic switching. However, this phenomenon has mostly been described with deterministic dynamic or well-mixed stochastic models. Here, we map known biological bistable systems onto the well-characterized biochemical Schlögl model, using analytical calculations and stochastic spatiotemporal simulations. In addition to network architecture and strong thermodynamic driving away from equilibrium, we show that bistability requires fine-tuning towards small cell volumes (or compartments) and fast protein diffusion (well mixing). Bistability is thus fragile and hence may be restricted to small bacteria and eukaryotic nuclei, with switching triggered by volume changes during the cell cycle. For large volumes, single cells generally loose their ability for bistable switching and instead undergo a first-order phase transition.

Quantifying gene expression variability arising from randomness in cell division times

The level of a given mRNA or protein exhibits significant variations from cell-to-cell across a homogeneous population of living cells. Much work has focused on understanding the different sources of noise in the gene-expression process that drive this stochastic variability in gene-expression. Recent experiments tracking growth and division of individual cells reveal that cell division times have considerable inter-cellular heterogeneity. Here we investigate how randomness in the cell division times can create variability in population counts. We consider a model by which mRNA/protein levels in a given cell evolve according to a linear differential equation and cell divisions occur at times spaced by independent and identically distributed random intervals. Whenever the cell divides the levels of mRNA and protein are halved. For this model, we provide a method for computing any statistical moment (mean, variance, skewness, etcetera) of the mRNA and protein levels. The key to our approach is to establish that the time evolution of the mRNA and protein statistical moments is described by an upper triangular system of Volterra equations. Computation of the statistical moments for physiologically relevant parameter values shows that randomness in the cell division process can be a major factor in driving difference in protein levels across a population of cells.

Contribution of RNA polymerase concentration variation to protein expression noise

Cell-to-cell variation in gene expression, or noise, is a general phenomenon observed within cell populations. Transcription is known to be the key stage of gene expression where noise is generated, however, how variation in RNA polymerase (RNAP) concentration contributes to gene expression noise is unclear. Here, we quantitatively investigate how variations in absolute amounts of RNAP molecules affect noise in the expression of two fluorescent protein reporters driven by identical promoters. We find that intrinsic noise is independent of variation in RNAP concentrations, whereas extrinsic noise, which is variation in gene expression due to varying cellular environments, scales linearly with variation in RNAP abundance. Specifically, the propagation of RNAP abundance variation to expressed protein noise is inversely proportional to the concentration of RNAP, which suggests that the change in noise that results from RNAP fluctuations is determined by the fraction of promoters that is not occupied by RNAP.

Stochastic cellular fate decision making by multiple infecting lambda phage

Bacteriophage lambda is a classic system for the study of cellular decision making. Both experiments and mathematical models have demonstrated the importance of viral concentration in the lysis-lysogeny decision outcome in lambda phage. However, a recent experimental study using single cell and single phage resolution reported that cells with the same viral concentrations but different numbers of infecting phage (multiplicity of infection) can have markedly different rates of lysogeny. Thus the decision depends on not only viral concentration, but also directly on the number of infecting phage. Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network. Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated. We find that interplay between the level of intrinsic noise and viral protein decision threshold is a major factor that produces dependence on multiplicity of infection. However, simulations suggest spatial segregation of phage particles does not play a significant role. Cellular image processing is used to re-analyse the original time-lapse movies from the recent study and it is found that higher numbers of infecting phage reduce the cell elongation rate. This could also contribute to the observed phenomena as cellular growth rate can affect transcription rates. Our model further predicts that rate of lysogeny is dependent on bacterial growth rate, which can be experimentally tested. Our study provides new insight on the mechanisms of individual phage decision making. More generally, our results are relevant for the understanding of gene-dosage compensation in cellular systems.