Genetic selection for context-dependent stochastic phenotypes: Sp1 and TATA mutations increase phenotypic noise in HIV-1 gene expression

The cell biology of HIV-1 latency and rebound

Transcriptionally latent forms of replication-competent proviruses, present primarily in a small subset of memory CD4+ T cells, pose the primary barrier to a cure for HIV-1 infection because they are the source of the viral rebound that almost inevitably follows the interruption of antiretroviral therapy. Over the last 30 years, many of the factors essential for initiating HIV-1 transcription have been identified in studies performed using transformed cell lines, such as the Jurkat T-cell model. However, as highlighted in this review, several poorly understood mechanisms still need to be elucidated, including the molecular basis for promoter-proximal pausing of the transcribing complex and the detailed mechanism of the delivery of P-TEFb from 7SK snRNP. Furthermore, the central paradox of HIV-1 transcription remains unsolved: how are the initial rounds of transcription achieved in the absence of Tat? A critical limitation of the transformed cell models is that they do not recapitulate the transitions between active effector cells and quiescent memory T cells. Therefore, investigation of the molecular mechanisms of HIV-1 latency reversal and LRA efficacy in a proper physiological context requires the utilization of primary cell models. Recent mechanistic studies of HIV-1 transcription using latently infected cells recovered from donors and ex vivo cellular models of viral latency have demonstrated that the primary blocks to HIV-1 transcription in memory CD4+ T cells are restrictive epigenetic features at the proviral promoter, the cytoplasmic sequestration of key transcription initiation factors such as NFAT and NF-κB, and the vanishingly low expression of the cellular transcription elongation factor P-TEFb. One of the foremost schemes to eliminate the residual reservoir is to deliberately reactivate latent HIV-1 proviruses to enable clearance of persisting latently infected cells-the "Shock and Kill" strategy. For "Shock and Kill" to become efficient, effective, non-toxic latency-reversing agents (LRAs) must be discovered. Since multiple restrictions limit viral reactivation in primary cells, understanding the T-cell signaling mechanisms that are essential for stimulating P-TEFb biogenesis, initiation factor activation, and reversing the proviral epigenetic restrictions have become a prerequisite for the development of more effective LRAs.

Dynamic behaviors of a stochastic virus infection model with Beddington-DeAngelis incidence function, eclipse-stage and Ornstein-Uhlenbeck process

In this paper, we present a virus infection model that incorporates eclipse-stage and Beddington-DeAngelis function, along with perturbation in infection rate using logarithmic Ornstein-Uhlenbeck process. Rigorous analysis demonstrates that the stochastic model has a unique global solution. Through construction of appropriate Lyapunov functions and a compact set, combined with the strong law of numbers and Fatou's lemma, we obtain the existence of the stationary distribution under a critical condition, which indicates the long-term persistence of T-cells and virions. Moreover, a precise probability density function is derived around the quasi-equilibrium of the model, and spectral radius analysis is employed to identify critical condition for elimination of the virus. Finally, numerical simulations are presented to validate theoretical results, and the impact of some key parameters such as the speed of reversion, volatility intensity and mean infection rate are investigated.

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.

Enhanced Transcriptional Strength of HIV-1 Subtype C Minimizes Gene Expression Noise and Confers Stability to the Viral Latent State

Stochastic fluctuations in gene expression emanating from the HIV-1 long terminal repeat (LTR), amplified by the Tat positive feedback circuit, determine the choice between viral infection fates: active transcription (ON) or transcriptional silence (OFF). The emergence of several transcription factor binding site (TFBS) variant strains in HIV-1 subtype C (HIV-1C), especially those containing the duplication of the NF-κB motif, mandates the evaluation of the effect of enhanced transcriptional strength on gene expression noise and its influence on viral fate selection switch. Using a panel of subgenomic LTR-variant strains containing different copy numbers of the NF-κB motif (ranging from 0 to 4), we used flow cytometry, mRNA quantification, and pharmacological perturbations to demonstrate an inverse correlation between promoter strength and gene expression noise in Jurkat T cells and primary CD4⁺ T cells. The inverse correlation is consistent in clonal cell populations at constant intracellular concentrations of Tat and when NF-κB levels were regulated pharmacologically. Further, we show that strong LTRs containing at least two copies of the NF-κB motif in the enhancer establish a more stable latent state and demonstrate more rapid latency reversal than weak LTRs containing fewer motifs. We also demonstrate a cooperative binding of NF-κB to the motif cluster in HIV-1C LTRs containing two, three, or four NF-κB motifs (Hill coefficient [H] = 2.61, 3.56, and 3.75, respectively). The present work alludes to a possible evolution of the HIV-1C LTR toward gaining transcriptional strength associated with attenuated gene expression noise with implications for viral latency. IMPORTANCE Over the past two consecutive decades, HIV-1 subtype C (HIV-1C) has been undergoing directional evolution toward augmenting the transcriptional strength of the long terminal repeat (LTR) by adding more copies of the existing transcription factor binding site (TFBS) by sequence duplication. Additionally, the duplicated elements are genetically diverse, suggesting broader-range signal receptivity by variant LTRs. The HIV-1 promoter is inherently noisy, and the stochastic fluctuations in gene expression of variant LTRs may influence the active transcription (ON)/transcriptional silence (OFF) latency decisions. The evolving NF-κB motif variations of HIV-1C offer a powerful opportunity to examine how the transcriptional strength of the LTR might influence gene expression noise. Our work here shows that the augmented transcriptional strength of the HIV-1C LTR leads to concomitantly reduced gene expression noise, consequently leading to stabler latency maintenance and rapid latency reversal. The present work offers a novel lead toward appreciating the molecular mechanisms governing HIV-1 latency.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.

Molecular Characterization of Small Ruminant Lentiviruses in Polish Mixed Flocks Supports Evidence of Cross Species Transmission, Dual Infection, a Recombination Event, and Reveals the Existence of New Subtypes within Group A

Small ruminant lentiviruses (SRLVs) are a group of highly divergent viruses responsible for global infection in sheep and goats. In a previous study we showed that SRLV strains found in mixed flocks in Poland belonged to subtype A13 and A18, but this study was restricted only to the few flocks from Małopolska region. The present work aimed at extending earlier findings with the analysis of SRLVs in mixed flocks including larger numbers of animals and flocks from different part of Poland. On the basis of gag and env sequences, Polish SRLVs were assigned to the subtypes B2, A5, A12, and A17. Furthermore, the existence of a new subtypes, tentatively designed as A23 and A24, were described for the first time. Subtypes A5 and A17 were only found in goats, subtype A24 has been detected only in sheep while subtypes A12, A23, and B2 have been found in both sheep and goats. Co-infection with strains belonging to different subtypes was evidenced in three sheep and two goats originating from two flocks. Furthermore, three putative recombination events were identified within gag and env SRLVs sequences derived from three sheep. Amino acid (aa) sequences of immunodominant epitopes in CA protein were well conserved while Major Homology Region (MHR) had more alteration showing unique mutations in sequences of subtypes A5 and A17. In contrast, aa sequences of surface glycoprotein exhibited higher variability confirming type-specific variation in the SU5 epitope. The number of potential N-linked glycosylation sites (PNGS) ranged from 3 to 6 in respective sequences and were located in different positions. The analysis of LTR sequences revealed that sequences corresponding to the TATA box, AP-4, AML-vis, and polyadenylation signal (poly A) were quite conserved, while considerable alteration was observed in AP-1 sites. Interestingly, our results revealed that all sequences belonging to subtype A17 had unique substitution T to A in the fifth position of TATA box and did not have a 11 nt deletion in the R region which was noted in other sequences from Poland. These data revealed a complex picture of SRLVs population with ovine and caprine strains belonging to group A and B. We present strong and multiple evidence of dually infected sheep and goats in mixed flocks and present evidence that these viruses can recombine in vivo.

Safe CRISPR-Cas9 Inhibition of HIV-1 with High Specificity and Broad-Spectrum Activity by Targeting LTR NF-κB Binding Sites

Viral latency of human immunodeficiency virus type 1 (HIV-1) has become a major hurdle to a cure in the highly effective antiretroviral therapy (ART) era. The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system has successfully been demonstrated to excise or inactivate integrated HIV-1 provirus from infected cells by targeting the long terminal repeat (LTR) region. However, the guide RNAs (gRNAs) have classically avoided transcription factor binding sites (TFBSs) that are readily observed and known to be important in human promoters. Although conventionally thought unfavorable due to potential impact on human promoters, our computational pipeline identified gRNA sequences that were predicted to inactivate HIV-1 transcription by targeting the nuclear factor κB (NF-κB) binding sites (gNFKB0, gNFKB1) with a high safety profile (lack of predicted or observed human edits) and broad-spectrum activity (predicted coverage of known viral sequences). Genome-wide, unbiased identification of double strand breaks (DSBs) enabled by sequencing (GUIDE-seq) showed that the gRNAs targeting NF-κB binding sites had no detectable CRISPR-induced off-target edits in HeLa cells. 5′ LTR-driven HIV-1 transcription was significantly reduced in three HIV-1 reporter cell lines. These results demonstrate a working model to specifically target well-known TFBSs in the HIV-1 LTR that are readily observed in human promoters to reduce HIV-1 transcription with a high-level safety profile and broad-spectrum activity.

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.

NF-κB-Chromatin Interactions Drive Diverse Phenotypes by Modulating Transcriptional Noise

Noisy gene expression generates diverse phenotypes, but little is known about mechanisms that modulate noise. Combining experiments and modeling, we studied how tumor necrosis factor (TNF) initiates noisy expression of latent HIV via the transcription factor nuclear factor κB (NF-κB) and how the HIV genomic integration site modulates noise to generate divergent (low-versus-high) phenotypes of viral activation. We show that TNF-induced transcriptional noise varies more than mean transcript number and that amplification of this noise explains low-versus-high viral activation. For a given integration site, live-cell imaging shows that NF-κB activation correlates with viral activation, but across integration sites, NF-κB activation cannot account for differences in transcriptional noise and phenotypes. Instead, differences in transcriptional noise are associated with differences in chromatin state and RNA polymerase II regulation. We conclude that, whereas NF-κB regulates transcript abundance in each cell, the chromatin environment modulates noise in the population to support diverse HIV activation in response to TNF.

Resistance to the Tat Inhibitor Didehydro-Cortistatin A Is Mediated by Heightened Basal HIV-1 Transcription

Human immunodeficiency virus type 1 (HIV-1) Tat binds the viral RNA structure transactivation-responsive element (TAR) and recruits transcriptional cofactors, amplifying viral mRNA expression. The Tat inhibitor didehydro-cortistatin A (dCA) promotes a state of persistent latency, refractory to viral reactivation. Here we investigated mechanisms of HIV-1 resistance to dCA in vitro Mutations in Tat and TAR were not identified, consistent with the high level of conservation of these elements. Instead, viruses resistant to dCA developed higher Tat-independent basal transcription. We identified a combination of mutations in the HIV-1 promoter that increased basal transcriptional activity and modifications in viral Nef and Vpr proteins that increased NF-κB activity. Importantly, these variants are unlikely to enter latency due to accrued transcriptional fitness and loss of sensitivity to Tat feedback loop regulation. Furthermore, cells infected with these variants become more susceptible to cytopathic effects and immune-mediated clearance. This is the first report of viral escape to a Tat inhibitor resulting in heightened Tat-independent activity, all while maintaining wild-type Tat and TAR.IMPORTANCE HIV-1 Tat enhances viral RNA transcription by binding to TAR and recruiting activating factors. Tat enhances its own transcription via a positive-feedback loop. Didehydro-cortistatin A (dCA) is a potent Tat inhibitor, reducing HIV-1 transcription and preventing viral rebound. dCA activity demonstrates the potential of the "block-and-lock" functional cure approaches. We investigated the viral genetic barrier to dCA resistance in vitro While mutations in Tat and TAR were not identified, mutations in the promoter and in the Nef and Vpr proteins promoted high Tat-independent activity. Promoter mutations increased the basal transcription, while Nef and Vpr mutations increased NF-κB nuclear translocation. This heightened transcriptional activity renders CD4⁺ T cells infected with these viruses more susceptible to cytotoxic T cell-mediated killing and to cell death by cytopathic effects. Results provide insights on drug resistance to a novel class of antiretrovirals and reveal novel aspects of viral transcriptional regulation.

A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study

Background

Mathematical models are used to gain an integrative understanding of biochemical processes and networks. Commonly the models are based on deterministic ordinary differential equations. When molecular counts are low, stochastic formalisms like Monte Carlo simulations are more appropriate and well established. However, compared to the wealth of computational methods used to fit and analyze deterministic models, there is only little available to quantify the exactness of the fit of stochastic models compared to experimental data or to analyze different aspects of the modeling results.

Results

Here, we developed a method to fit stochastic simulations to experimental high-throughput data, meaning data that exhibits distributions. The method uses a comparison of the probability density functions that are computed based on Monte Carlo simulations and the experimental data. Multiple parameter values are iteratively evaluated using optimization routines. The method improves its performance by selecting parameters values after comparing the similitude between the deterministic stability of the system and the modes in the experimental data distribution. As a case study we fitted a model of the IRF7 gene expression circuit to time-course experimental data obtained by flow cytometry. IRF7 shows bimodal dynamics upon IFN stimulation. This dynamics occurs due to the switching between active and basal states of the IRF7 promoter. However, the exact molecular mechanisms responsible for the bimodality of IRF7 is not fully understood.

Conclusions

Our results allow us to conclude that the activation of the IRF7 promoter by the combination of IRF7 and ISGF3 is sufficient to explain the observed bimodal dynamics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0406-4) contains supplementary material, which is available to authorized users.

The variances of Sp1 and NF-κB elements correlate with the greater capacity of Chinese HIV-1 B'-LTR for driving gene expression

The 5' end of HIV-1 long terminal repeat (LTR) serves as a promoter that plays an essential role in driving viral gene transcription. Manipulation of HIV-1 LTR provides a potential therapeutic strategy for suppressing viral gene expression or excising integrated provirus. Subtype-specific genetic diversity in the LTR region has been observed. The minor variance of LTR, particularly in the transcription factor binding sites, can have a profound impact on its activity. However, the LTR profiles from major endemic Chinese subtypes are not well characterized. Here, by characterizing the sequences and functions of LTRs from endemic Chinese HIV-1 subtypes, we showed that nucleotide variances of Sp1 core promoter and NF-κB element are associated with varied LTR capacity for driving viral gene transcription. The greater responsiveness of Chinese HIV-1 B'-LTR for driving viral gene transcription upon stimulation is associated with an increased level of viral reactivation. Moreover, we demonstrated that the introduction of CRISPR/dead Cas9 targeting Sp1 or NF-κB element suppressed viral gene expression. Taken together, our study characterized LTRs from endemic HIV-1 subtypes in China and suggests a potential target for the suppression of viral gene expression and a novel strategy that facilitates the accomplishment of a functional cure.

A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting

Live-cell imaging has revealed unexpected features of gene expression. Here using improved single-molecule RNA microscopy, we show that synthesis of HIV-1 RNA is achieved by groups of closely spaced polymerases, termed convoys, as opposed to single isolated enzymes. Convoys arise by a Mediator-dependent reinitiation mechanism, which generates a transient but rapid succession of polymerases initiating and escaping the promoter. During elongation, polymerases are spaced by few hundred nucleotides, and physical modelling suggests that DNA torsional stress may maintain polymerase spacing. We additionally observe that the HIV-1 promoter displays stochastic fluctuations on two time scales, which we refer to as multi-scale bursting. Each time scale is regulated independently: Mediator controls minute-scale fluctuation (convoys), while TBP-TATA-box interaction controls sub-hour fluctuations (long permissive/non-permissive periods). A cellular promoter also produces polymerase convoys and displays multi-scale bursting. We propose that slow, TBP-dependent fluctuations are important for phenotypic variability of single cells.

HIV-1 viral gene expression stochastically switches between active and inactive states. Here, using improved single molecule RNA microscopy, the authors show that HIV-1 RNA stochastic transcription is achieved by groups of closely spaced polymerases, and is regulated by Mediator and TBP at different time scales.

Toll-interacting protein inhibits HIV-1 infection and regulates viral latency

HIV-1 latency is mainly characterized by a reversible silencing of long-terminal repeat (LTR)-driven transcription of provirus. The existing of repressive factors has been described to contribute to transcription silencing of HIV-1. Toll-interacting protein (Tollip) has been identified as a repressor of Toll like receptors (TLR)-mediated signaling. Our previous study has found that Tollip inhibited NF-κB-dependent HIV-1 promoter LTR-driven transcription, indicating the potential role of Tollip in governing viral latency. In this study, by using HIV-1 latently infected Jurkat T-cell and central memory CD4(+) T-cells, we demonstrate the role of Tollip in regulating HIV-1 latency, as the knock-down of Tollip promoted HIV-1 reactivation from both HIV-1 latently infected Jurkat CD4(+) T cells and primary central memory T cells (TCM). Moreover, we found that the activities of LTRs derived from multiple HIV-1 subtypes could be repressed by Tollip; Knock-down of Tollip promoted HIV-1 transcription and infection in CD4(+) T cells. Our data indicate a key role of Tollip in suppressing HIV-1 infection and regulating viral latency, which provides a potential host target for combating HIV-1 infection and latency.

Constructing an integrated genetic and epigenetic cellular network for whole cellular mechanism using high-throughput next-generation sequencing data

Background

Epigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data.

Results

We investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein–protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone pathways; dysregulation of MIR139-5p, MIRLET7i, and MIR10a contributes to the HIV-1 integration/replication stage; dysregulation of MIR101, MIR141, and MIR152 contributes to the HIV-1 virus assembly and budding mechanisms; dysregulation of MIR302a contributes to not only microvesicle-mediated transfer of miRNAs but also dysfunction of NF-κB signaling pathway in hepatocarcinogenesis.

Conclusion

The coupling dynamic systems of the whole IGECN can allow us to investigate genetic and epigenetic cellular mechanisms via omics data and big database mining, and are useful for further experiments in the field of systems and synthetic biology.

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.

Comparative Analysis of Tat-Dependent and Tat-Deficient Natural Lentiviruses

The emergence of human immunodeficiency virus (HIV) causing acquired immunodeficiency syndrome (AIDS) in infected humans has resulted in a global pandemic that has killed millions. HIV-1 and HIV-2 belong to the lentivirus genus of the Retroviridae family. This genus also includes viruses that infect other vertebrate animals, among them caprine arthritis-encephalitis virus (CAEV) and Maedi-Visna virus (MVV), the prototypes of a heterogeneous group of viruses known as small ruminant lentiviruses (SRLVs), affecting both goat and sheep worldwide. Despite their long host-SRLV natural history, SRLVs were never found to be responsible for immunodeficiency in contrast to primate lentiviruses. SRLVs only replicate productively in monocytes/macrophages in infected animals but not in CD4+ T cells. The focus of this review is to examine and compare the biological and pathological properties of SRLVs as prototypic Tat-independent lentiviruses with HIV-1 as prototypic Tat-dependent lentiviruses. Results from this analysis will help to improve the understanding of why and how these two prototypic lentiviruses evolved in opposite directions in term of virulence and pathogenicity. Results may also help develop new strategies based on the attenuation of SRLVs to control the highly pathogenic HIV-1 in humans.

Structure of silent transcription intervals and noise characteristics of mammalian genes

Mammalian transcription occurs stochastically in short bursts interspersed by silent intervals showing a refractory period. However, the underlying processes and consequences on fluctuations in gene products are poorly understood. Here, we use single allele time-lapse recordings in mouse cells to identify minimal models of promoter cycles, which inform on the number and durations of rate-limiting steps responsible for refractory periods. The structure of promoter cycles is gene specific and independent of genomic location. Typically, five rate-limiting steps underlie the silent periods of endogenous promoters, while minimal synthetic promoters exhibit only one. Strikingly, endogenous or synthetic promoters with TATA boxes show simplified two-state promoter cycles. Since transcriptional bursting constrains intrinsic noise depending on the number of promoter steps, this explains why TATA box genes display increased intrinsic noise genome-wide in mammals, as revealed by single-cell RNA-seq. These findings have implications for basic transcription biology and shed light on interpreting single-cell RNA-counting experiments.

A passive-flow microfluidic device for imaging latent HIV activation dynamics in single T cells

Quantifying cell-to-cell variability in drug response dynamics is important when evaluating therapeutic efficacy. For example, optimizing latency reversing agents (LRAs) for use in a clinical "activate-and-kill" strategy to purge the latent HIV reservoir in patients requires minimizing heterogeneous viral activation dynamics. To evaluate how heterogeneity in latent HIV activation varies across a range of LRAs, we tracked drug-induced response dynamics in single cells via live-cell imaging using a latent HIV-GFP reporter virus in a clonal Jurkat T cell line. To enable these studies in suspension cells, we designed a simple method to capture an array of single Jurkat T cells using a passive-flow microfluidic device. Our device, which does not require external pumps or tubing, can trap hundreds of cells within minutes with a high retention rate over 12 hours of imaging. Using this device, we quantified heterogeneity in viral activation stimulated by transcription factor (TF) activators and histone deacetylase (HDAC) inhibitors. Generally, TF activators resulted in both faster onset of viral activation and faster rates of production, while HDAC inhibitors resulted in more uniform onset times, but more heterogeneous rates of production. Finally, we demonstrated that while onset time of viral gene expression and rate of viral production together predict total HIV activation, rate and onset time were not correlated within the same individual cell, suggesting that these features are regulated independently. Overall, our results reveal drug-specific patterns of noisy HIV activation dynamics not previously identified in static single-cell assays, which may require consideration for the most effective activate-and-kill regime.

Adaptability of non-genetic diversity in bacterial chemotaxis

Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could serve as an alternative, or even evolutionary stepping-stone, to switching between multiple systems. We hypothesized that mutations in gene regulation could lead to heritable control of chemotactic diversity. By simulating foraging and colonization of E. coli using a single-cell chemotaxis model, we found that different environments selected for different behaviors. The resulting trade-offs show that populations facing diverse environments would ideally diversify behaviors when time for navigation is limited. We show that advantageous diversity can arise from changes in the distribution of protein levels among individuals, which could occur through mutations in gene regulation. We propose experiments to test our prediction that chemotactic diversity in a clonal population could be a selectable trait that enables adaptation to environmental variability.

DOI:http://dx.doi.org/10.7554/eLife.03526.001

Bacterial colonies are generally made up of genetically identical cells. Despite this, a closer look at the members of a bacterial colony shows that these cells can have very different behaviors. For example, some cells may grow more quickly than others, or be more resistant to antibiotics. The mechanisms driving this diversity are only beginning to be identified and understood.

Escherichia coli bacteria can move towards, or away from, certain chemicals in their surrounding environment to help them navigate toward favorable conditions. This behavior is known as chemotaxis. The signals from all of these chemicals are processed in E. coli by just one set of proteins, which control the different behaviors that are needed for the bacteria to follow them. Different numbers of these proteins are found in different—but genetically identical—bacteria, and the number of proteins is linked to how the bacteria perform these behaviors.

It has been suggested that diversity can be beneficial to the overall bacterial population, as it helps the population survive environmental changes. This suggests that the level of diversity in the population should adapt to the level of diversity in the environment. However, it remains unknown how this adaptation occurs.

Frankel et al. developed and combined several models and simulations to investigate whether differences in chemotaxis protein production help an E. coli colony to survive. The models show that in different environments, it can be beneficial for the population as a whole if different cells have different responses to the chemicals present. For example, if a lot of a useful chemical is present, bacteria are more likely to survive by heading straight to the source. If not much chemical is detected, the bacteria may need to move in a more exploratory manner.

Frankel et al. find that different amounts of chemotaxis proteins produce these different behaviors. To survive in a changing environment, it is therefore best for the E. coli colony to contain cells that have different amounts of these proteins. Frankel et al. propose that the variability of chemotaxis protein levels between genetically identical cells can change through mutations in the genes that control how many of the proteins are produced, and predict that such mutations allow populations to adapt to environmental changes.

The environments simulated in the model were much simpler than would be found in the real world, and Frankel et al. describe experiments that are now being performed to confirm and expand on their results. The model could be used in the future to shed light on the behavior of other cells that are genetically identical but exhibit diverse behaviors, from other bacterial species to more complex cancer cells.

DOI:http://dx.doi.org/10.7554/eLife.03526.002

Quantitative evaluation and optimization of co-drugging to improve anti-HIV latency therapy

Human immunodeficiency virus 1 (HIV) latency remains a significant obstacle to curing infected patients. One promising therapeutic strategy is to purge the latent cellular reservoir by activating latent HIV with latency-reversing agents (LRAs). In some cases, co-drugging with multiple LRAs is necessary to activate latent infections, but few studies have established quantitative criteria for determining when co-drugging is required. Here we systematically quantified drug interactions between histone deacetylase inhibitors and transcriptional activators of HIV and found that the need for co-drugging is determined by the proximity of latent infections to the chromatin-regulated viral gene activation threshold at the viral promoter. Our results suggest two classes of latent viral integrations: those far from the activation threshold that benefit from co-drugging, and those close to the threshold that are efficiently activated by a single drug. Using a primary T cell model of latency, we further demonstrated that the requirement for co-drugging was donor dependent, suggesting that the host may set the level of repression of latent infections. Finally, we showed that single drug or co-drugging doses could be optimized, via repeat stimulations, to minimize unwanted side effects while maintaining robust viral activation. Our results motivate further study of patient-specific latency-reversing strategies.