HIV潜伏感染激活剂的研究进展

艾滋病是由人类免疫缺陷病毒(Human immunodeficiency virus,HIV)引起的人类最严重的单一病因疾病,以全身免疫系统严重损害为特征。高效抗逆转录病毒疗法(Highly active anti-retroviral therapy,HAART)的应用已经成功地将艾滋病从一种致死性疾病转变为慢性可控性疾病。但长期接受HAART治疗的艾滋病患者一旦停药,患者体内潜伏的HIV会迅速反弹。艾滋病无法彻底治愈的原因是患者体内HIV病毒潜伏储存库的存在。"Shock and kill"策略是使用HIV潜伏感染激活剂(Latency-reversing agents,LRAs)诱导潜伏HIV前病毒复制及表达,然后联合HAART将病毒一网打尽,同时由于细胞病变效应和/或HIV特征性免疫反应使潜伏细胞的半衰期大大缩短,最终达到功能性治愈的目的。因此,高效、安全且特异性促进潜伏库衰减的LRAs成为现今艾滋病治愈研究的热点。本文聚焦国内外前沿研究,对具有临床发展前景的HIV潜伏感染激活剂做一综述,为未来LRAs药物的研发指明方向。     

HIV感染动力学模型概述

自1981年美国首次发现艾滋病以来,艾滋病在世界范围内广泛传播,引起医学专家、生物学家、数学家和物理学家等的极大关注。近年来,HIV动力学模型成为HIV治疗领域的研究热点。HIV基本动力学模型的研究有助于实现对未来疾病发展状况的描述与预测,HIV感染控制模型的研究有助于改善HIV病毒患者的治疗方案,对控制模型的优化有利于发现对HIV患者的有效治疗策略。本文概述了几种基本的HIV感染动力学模型,分析比较了它们的性能差异和各自存在的优缺点,介绍了HIV控制模型及其优化控制模型的计算机Matlab/simulink模拟。     

艾滋病的基因治疗策略及慢病毒载体

高活性的抗病毒治疗可以显著地降低艾滋病患者血浆中的HIV病毒载量,但对潜伏的病毒库无效.对HIV的基因治疗包括诱导HIV潜伏感染的休止的CD4 T记忆细胞增生,使潜伏的HIV激活进入复制循环,结合药物治疗和激活潜伏的HIV基因表达但并不诱导细胞增生,而是通过载体携带的基因使细胞凋亡,以清除HIV潜伏感染的细胞,利用载体携带目的基因治疗脑中的病毒.     

抗人类免疫缺陷病毒1型广谱中和抗体的研究进展

现行抗反转录病毒治疗药物的联合应用可有效抑制艾滋病进程并显著延长患者寿命,但由于人类免疫缺陷病毒1型(human immunodeficiency virus type 1,HIV-1)潜伏库的存在,艾滋病迄今尚无法治愈。近年发现抗HIV广谱中和抗体能有效降低患者体内病毒载量并延缓疾病进程,为研发艾滋病疫苗和治愈策略带来了曙光,尤其是序贯免疫策略的使用极大推进了广谱中和抗体的开发和应用进程。2018年,美国食品药品管理局(Food and Drug Administration,FDA)批准了第1个临床应用的广谱中性单克隆和抗体,无疑为抗HIV单克隆抗体药物的研发注入了一支强心剂。本文围绕近年来抗HIV广谱中和抗体的研究进展进行综述,探讨未来广谱中和抗体研发面临的挑战。     

微小RNA对人类免疫缺陷病毒感染的影响: 飞向入侵者的子弹?

微小RNA(miRNA)是一类内源性小RNA,通过结合mRNA的3′非翻译区对基因进行转录后的调节,具有广泛的生物学功能.已有研究表明,宿主miRNA能调节人类免疫缺陷病毒(HIV)的基因表达,影响HIV的复制能力、感染性,并可能与HIV的潜伏有关.与此同时,HIV来源的病毒miRNA同样在病毒的生活史以及病毒与宿主的...     

《核酸研究》：新突变体可选择杀伤HIV感染细胞

艾滋病病毒（HIV）存在潜藏机制可以长期潜伏在细胞中而逃逸宿主免疫系统的攻击,目前已上市的抗HIV药物均不能选择性地杀伤感染细胞而根除病毒。新的研究思路对开发新型抗HIV药物显得非常重要,研究具有选择性地杀伤HIV感染细胞而保护正常细胞不受伤害的抗艾滋病药物是极有前景的方向。     

传播的人群生态动力学模型

研究了HIV传播的动力学模型,描述了流行性传染病区域的人群传播规律,特别是利用摄动理论对艾滋病的传播动力学非线性方程作了定量、定性的讨论。     

HIV模型动力学分析及抗HIV感染治疗仿真

基于基本病毒感染模型,本文引入了一个包含免疫项的艾滋病病毒(HIV)感染模型.该模型有一个病毒清除平衡点和一个持续带毒平衡点.证明如果病毒感染的基本再生数R1,则病毒清除平衡点是全局渐近稳定的.该结果说明若一个HIV感染者其R1,则即使被感染大量的HIV最终仍然能自愈.基于该模型本文提出了一个抗HIV感染治疗模型.本文定理暗指若抗HIV感染治疗时,患者的R1则迟早患者体内的HIV可以清除.反之数值的仿真模拟表明患者R1时,患者体内的HIV不能被彻底清除.患者的依从性是抗HIV感染成功的重要因素之一.     

小鼠脑潜伏巨细胞病毒激活模型的建立

目的:我们建立了小鼠脑潜伏巨细胞病毒激活模型,来实现小鼠脑内潜伏的巨细胞病毒(MCMV)的激活,并对潜伏MCMV激活时程进行分析,确定MCMV即刻早期蛋白基因1(ie1)基因转录,完成对ie1基因转录和活病毒产生量时程动力学的分析,以及为进一步阐明原始MCMV在脑中潜伏的细胞类型提供模型。方法:采用出生后两天的BALB/c幼鼠,经右侧耳和眼连线为底边的正三角形的中心将Smith Strain MCMV 500 PFU/5μL注射进入右侧脑室,培养至16周。之后,将脂多糖(LPS)依15μg/kg体重(接近致死量)分别经腹腔和侧脑室内注射,对照组注射生理盐水。于注射后的1日,2日,5日,7日,14日和21日分别在LPS组和对照组中选取5只小鼠取脑。应用反转录-聚合酶链式反应(RT-PCR)和高敏感性病毒空斑实验(结合病毒空斑实验和RT-PCR)测定MCMV即刻早期蛋白1(IE1)mRNA的表达以及活病毒产生定量分析。结果:LPS组中,可于14日和21日的脑内检测到IE1 mRNA的转录,敏感性病毒空斑实验只在14日和21日出现细胞病毒效应(CPE),病毒量约为4.29×104 PFU/μL和5.20×105PFU/μL,相应对应MEF细胞匀浆物的RT-PCR结果检测到7,14,21日有IE1 mRNA转录。结论:该实验成功建立了小鼠脑潜伏巨细胞病毒激活的模型,并证实和分析了即刻早期蛋白基因ie1在潜伏MCMV激活过程中的表达和时程。该模型的建立将为进一步阐明MCMV在脑中潜伏细胞类型以及MCMV在急性感染、潜伏和重激活过程中对中枢神经细胞的影响提供研究平台,并为人巨细胞病毒(HCMV)的临床研究提供实验依据。     

HIV-1潜伏感染体外实验模型研究进展

潜伏感染的静息记忆CD4+T细胞是清除HIV-1病毒的一个重要障碍。处于潜伏状态的病毒多以原病毒c DNA的形式整合至宿主基因组中,但是病毒基因表达处于沉默状态,因此潜伏感染的细胞难以受到病毒的致细胞病变效应或机体特异性细胞毒性T细胞的杀伤,也不易受到抗反转录病毒治疗药物的作用。如何减少潜伏感染的细胞储存库是艾滋病治疗中亟需解决的一个问题。体内及体外HIV-1潜伏感染模型有助于深入了解HIV-1潜伏感染的建立、维持或打破机制,评价潜伏感染再激活剂的活性。在此侧重于介绍采用永生化细胞系、原代静息CD4~+T细胞或活化的CD4+T细胞建立的HIV-1潜伏感染体外实验模型。     

Optimal control of the chemotherapy of HIV

 Using an existing ordinary differential equation model which describes the interaction of the immune system with the human immunodeficiency virus (HIV), we introduce chemotherapy in an early treatment setting through a dynamic treatment and then solve for an optimal chemotherapy strategy. The control represents the percentage of effect the chemotherapy has on the viral production. Using an objective function based on a combination of maximizing benefit based on T cell counts and minimizing the systemic cost of chemotherapy (based on high drug dose/strength), we solve for the optimal control in the optimality system composed of four ordinary differential equations and four adjoint ordinary differential equations. Received 5 July 1995; received in revised form 3 June 1996     

Stochastic simulations on a model of circadian rhythm generation

Biological phenomena are often modeled by differential equations, where states of a model system are described by continuous real values. When we consider concentrations of molecules as dynamical variables for a set of biochemical reactions, we implicitly assume that numbers of the molecules are large enough so that their changes can be regarded as continuous and they are described deterministically. However, for a system with small numbers of molecules, changes in their numbers are apparently discrete and molecular noises become significant. In such cases, models with deterministic differential equations may be inappropriate, and the reactions must be described by stochastic equations. In this study, we focus a clock gene expression for a circadian rhythm generation, which is known as a system involving small numbers of molecules. Thus it is appropriate for the system to be modeled by stochastic equations and analyzed by methodologies of stochastic simulations. The interlocked feedback model proposed by Ueda et al. as a set of deterministic ordinary differential equations provides a basis of our analyses. We apply two stochastic simulation methods, namely Gillespie's direct method and the stochastic differential equation method also by Gillespie, to the interlocked feedback model. To this end, we first reformulated the original differential equations back to elementary chemical reactions. With those reactions, we simulate and analyze the dynamics of the model using two methods in order to compare them with the dynamics obtained from the original deterministic model and to characterize dynamics how they depend on the simulation methodologies.     

脉冲-扩散周期竞争系统解的渐近行为

研究了在周期变化环境中具有扩散及种群密度可能发生突变的两竞争种群动力系统的数学模型．模型由反应扩散方程组以及初边值及脉冲条件组成．文章建立了研究模型的上下解方法,获得了一些比较原理．利用脉冲常微分方程的比较定理以及利用相应的脉冲常微分方程的解控制和估计所讨论模型的解,研究了系统模型的解的渐近性质．     

Unbuffered and buffered supply chains in human metabolism

The investigation of very complex dynamical systems like the human metabolism requires the comprehension of important subsystems. The present paper deals with energy supply chains as subsystems of the metabolism on the molecular, cellular, and individual levels. We form a mathematical model of ordinary differential equations and we show fundamental properties by Fourier techniques. The results are supported by a transition from a system of ordinary differential equations to a partial differential equation, namely, a transport equation. In particular, the behavior of supply chains with dominant pull components is discussed. A special focus lies on the role of buffer compartments.     

Optimal HIV treatment by maximising immune response

We present an optimal control model of drug treatment of the human immunodeficiency virus (HIV). Our model is based upon ordinary differential equations that describe the interaction between HIV and the specific immune response as measured by levels of natural killer cells. We establish stability results for the model. We approach the treatment problem by posing it as an optimal control problem in which we maximise the benefit based on levels of healthy CD4+ T cells and immune response cells, less the systemic cost of chemotherapy. We completely characterise the optimal control and compute a numerical solution of the optimality system via analytic continuation.Research supported by the Natural Science and Engineering Research Council (NSERC) and the Mathematics of Information Technology and Complex Systems (MITACS) of Canada     

Stochastic models for virus and immune system dynamics

New stochastic models are developed for the dynamics of a viral infection and an immune response during the early stages of infection. The stochastic models are derived based on the dynamics of deterministic models. The simplest deterministic model is a well-known system of ordinary differential equations which consists of three populations: uninfected cells, actively infected cells, and virus particles. This basic model is extended to include some factors of the immune response related to Human Immunodeficiency Virus-1 (HIV-1) infection. For the deterministic models, the basic reproduction number, R₀, is calculated and it is shown that if R₀<1, the disease-free equilibrium is locally asymptotically stable and is globally asymptotically stable in some special cases. The new stochastic models are systems of stochastic differential equations (SDEs) and continuous-time Markov chain (CTMC) models that account for the variability in cellular reproduction and death, the infection process, the immune system activation, and viral reproduction. Two viral release strategies are considered: budding and bursting. The CTMC model is used to estimate the probability of virus extinction during the early stages of infection. Numerical simulations are carried out using parameter values applicable to HIV-1 dynamics. The stochastic models provide new insights, distinct from the basic deterministic models. For the case R₀>1, the deterministic models predict the viral infection persists in the host. But for the stochastic models, there is a positive probability of viral extinction. It is shown that the probability of a successful invasion depends on the initial viral dose, whether the immune system is activated, and whether the release strategy is bursting or budding.     

Time-dependent propagators for stochastic models of gene expression: an analytical method

The inherent stochasticity of gene expression in the context of regulatory networks profoundly influences the dynamics of the involved species. Mathematically speaking, the propagators which describe the evolution of such networks in time are typically defined as solutions of the corresponding chemical master equation (CME). However, it is not possible in general to obtain exact solutions to the CME in closed form, which is due largely to its high dimensionality. In the present article, we propose an analytical method for the efficient approximation of these propagators. We illustrate our method on the basis of two categories of stochastic models for gene expression that have been discussed in the literature. The requisite procedure consists of three steps: a probability-generating function is introduced which transforms the CME into (a system of) partial differential equations (PDEs); application of the method of characteristics then yields (a system of) ordinary differential equations (ODEs) which can be solved using dynamical systems techniques, giving closed-form expressions for the generating function; finally, propagator probabilities can be reconstructed numerically from these expressions via the Cauchy integral formula. The resulting ‘library’ of propagators lends itself naturally to implementation in a Bayesian parameter inference scheme, and can be generalised systematically to related categories of stochastic models beyond the ones considered here.

     

HIV Latency Is Established Directly and Early in Both Resting and Activated Primary CD4 T Cells

Highly active antiretroviral therapy (HAART) suppresses human immunodeficiency virus (HIV) replication to undetectable levels but cannot fully eradicate the virus because a small reservoir of CD4⁺ T cells remains latently infected. Since HIV efficiently infects only activated CD4⁺ T cells and since latent HIV primarily resides in resting CD4⁺ T cells, it is generally assumed that latency is established when a productively infected cell recycles to a resting state, trapping the virus in a latent state. In this study, we use a dual reporter virus—HIV Duo-Fluo I, which identifies latently infected cells immediately after infection—to investigate how T cell activation affects the estab-lishment of HIV latency. We show that HIV latency can arise from the direct infection of both resting and activated CD4⁺ T cells. Importantly, returning productively infected cells to a resting state is not associated with a significant silencing of the integrated HIV. We further show that resting CD4⁺ T cells from human lymphoid tissue (tonsil, spleen) show increased latency after infection when compared to peripheral blood. Our findings raise significant questions regarding the most commonly accepted model for the establishment of latent HIV and suggest that infection of both resting and activated primary CD4⁺ T cells produce latency.     

Modeling epithelial cell homeostasis: Assessing recovery and control mechanisms

Critical to epithelial cell viability is prompt and direct recovery, following a perturbation of cellular conditions. Although a number of transporters are known to be activated by changes in cell volume, cell pH, or cell membrane potential, their importance to cellular homeostasis has been difficult to establish. Moreover, the coordination among such regulated transporters to enhance recovery has received no attention in mathematical models of cellular function. In this paper, a previously developed model of proximal tubule (Weinstein, 1992, Am. J. Physiol. 263, F784–F798), has been approximated by its linearization about a reference condition. This yields a system of differential equations and auxiliary linear equations, which estimate cell volume and composition and transcellular fluxes in response to changes in bath conditions or membrane transport coefficients. Using the singular value decomposition, this system is reduced to a linear dynamical system, which is stable and reproduces the full model behavior in a useful neighborhood of the reference. Cost functions on trajectories formulated in the model variables (e.g., time for cell volume recovery) are translated into cost functions for the dynamical system. When the model is extended by the inclusion of linear dependence of membrane transport coefficients on model variables, the impact of each such controller on the recovery cost can be estimated with the solution of a Lyapunov matrix equation. Alternatively, solution of an algebraic Riccati equation provides the ensemble of controllers that constitute optimal state feedback for the dynamical system. When translated back into the physiological variables, the optimal controller contains some expected components, as well as unanticipated controllers of uncertain significance. This approach provides a means of relating cellular homeostasis to optimization of a dynamical system.     

Solving the chemical master equation using sliding windows

Background  

The chemical master equation (CME) is a system of ordinary differential equations that describes the evolution of a network of chemical reactions as a stochastic process. Its solution yields the probability density vector of the system at each point in time. Solving the CME numerically is in many cases computationally expensive or even infeasible as the number of reachable states can be very large or infinite. We introduce the sliding window method, which computes an approximate solution of the CME by performing a sequence of local analysis steps. In each step, only a manageable subset of states is considered, representing a "window" into the state space. In subsequent steps, the window follows the direction in which the probability mass moves, until the time period of interest has elapsed. We construct the window based on a deterministic approximation of the future behavior of the system by estimating upper and lower bounds on the populations of the chemical species.