Mini review Special Issues

Intracellular/surface moonlighting proteins that aid in the attachment of gut microbiota to the host

  • Received: 03 November 2018 Accepted: 26 February 2019 Published: 11 March 2019
  • The gut microbiota use proteins on their surface to form and maintain interactions with host cells and tissues. In recent years, many of these cell surface proteins have been found to be identical to intracellular enzymes and chaperones. When displayed on the cell surface these moonlighting proteins help the microbe attach to the host by interacting with receptors on the surface of host cells, components of the extracellular matrix, and mucin in the mucosal lining of the digestive tract. Binding of these proteins to the soluble host protein plasminogen promotes the conversion of plasminogen to an active protease, plasmin, which activates other host proteins that aid in infection and virulence. In this mini-review, we discuss intracellular/surface moonlighting proteins of pathogenic and probiotic bacteria and eukaryotic gut microbiota.

    Citation: Constance J. Jeffery. Intracellular/surface moonlighting proteins that aid in the attachment of gut microbiota to the host[J]. AIMS Microbiology, 2019, 5(1): 77-86. doi: 10.3934/microbiol.2019.1.77

    Related Papers:

    [1] Yan Xie, Zhijun Liu, Ke Qi, Dongchen Shangguan, Qinglong Wang . A stochastic mussel-algae model under regime switching. Mathematical Biosciences and Engineering, 2022, 19(5): 4794-4811. doi: 10.3934/mbe.2022224
    [2] Yansong Pei, Bing Liu, Haokun Qi . Extinction and stationary distribution of stochastic predator-prey model with group defense behavior. Mathematical Biosciences and Engineering, 2022, 19(12): 13062-13078. doi: 10.3934/mbe.2022610
    [3] Lin Li, Wencai Zhao . Deterministic and stochastic dynamics of a modified Leslie-Gower prey-predator system with simplified Holling-type Ⅳ scheme. Mathematical Biosciences and Engineering, 2021, 18(3): 2813-2831. doi: 10.3934/mbe.2021143
    [4] Sanling Yuan, Xuehui Ji, Huaiping Zhu . Asymptotic behavior of a delayed stochastic logistic model with impulsive perturbations. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1477-1498. doi: 10.3934/mbe.2017077
    [5] Chun Lu, Bing Li, Limei Zhou, Liwei Zhang . Survival analysis of an impulsive stochastic delay logistic model with Lévy jumps. Mathematical Biosciences and Engineering, 2019, 16(5): 3251-3271. doi: 10.3934/mbe.2019162
    [6] Zhiwei Huang, Gang Huang . Mathematical analysis on deterministic and stochastic lake ecosystem models. Mathematical Biosciences and Engineering, 2019, 16(5): 4723-4740. doi: 10.3934/mbe.2019237
    [7] Yan Zhang, Shujing Gao, Shihua Chen . Modelling and analysis of a stochastic nonautonomous predator-prey model with impulsive effects and nonlinear functional response. Mathematical Biosciences and Engineering, 2021, 18(2): 1485-1512. doi: 10.3934/mbe.2021077
    [8] Xueqing He, Ming Liu, Xiaofeng Xu . Analysis of stochastic disease including predator-prey model with fear factor and Lévy jump. Mathematical Biosciences and Engineering, 2023, 20(2): 1750-1773. doi: 10.3934/mbe.2023080
    [9] Yan Wang, Tingting Zhao, Jun Liu . Viral dynamics of an HIV stochastic model with cell-to-cell infection, CTL immune response and distributed delays. Mathematical Biosciences and Engineering, 2019, 16(6): 7126-7154. doi: 10.3934/mbe.2019358
    [10] H. J. Alsakaji, F. A. Rihan, K. Udhayakumar, F. El Ktaibi . Stochastic tumor-immune interaction model with external treatments and time delays: An optimal control problem. Mathematical Biosciences and Engineering, 2023, 20(11): 19270-19299. doi: 10.3934/mbe.2023852
  • The gut microbiota use proteins on their surface to form and maintain interactions with host cells and tissues. In recent years, many of these cell surface proteins have been found to be identical to intracellular enzymes and chaperones. When displayed on the cell surface these moonlighting proteins help the microbe attach to the host by interacting with receptors on the surface of host cells, components of the extracellular matrix, and mucin in the mucosal lining of the digestive tract. Binding of these proteins to the soluble host protein plasminogen promotes the conversion of plasminogen to an active protease, plasmin, which activates other host proteins that aid in infection and virulence. In this mini-review, we discuss intracellular/surface moonlighting proteins of pathogenic and probiotic bacteria and eukaryotic gut microbiota.


    1. Introduction

    Cell-based in vitro assays [27] are efficient methods to study the effect of industrial chemicals on environment or human health. Our work is based on the cytotoxicity profiling project carried by Alberta Centre for Toxicology in which initially 63 chemicals were investigated using the xCELLigence Real-Time Cell Analysis High Troughput (RTCA HT) Assay [26]. We consider a mathematical model represented by stochastic differential equations to study cytotoxicity, i.e. the effect of toxicants on human cells, such as the killing of cells or cellular pathological changes.

    The cells were seeded into wells of micro-electronic plates (E-Plates), and the test substances with 11 concentrations (1:3 serial dilution from the stock solution) were dissolved in the cell culture medium [20]. The microelectrode electronic impedance value was converted by a software to Cell Index ($n$), which closely reflects not only cell growth and cell death, but also cell morphology. The time-dependent concentration response curves (TCRCs) for each test substance in each cell line were generated [26] and based on these curves the toxicants in the present study were divided in 10 groups [30]. In Fig. 1 we display the TCRCs for the toxicant monastrol.

    Figure 1. TCRCs for monastrol.

    The success of clustering and classification methods depends on providing TCRCs that illustrates the cell population evolution from persistence to extinction. In [1] we consider a model represented by a system of ordinary differential equations to determine an appropriate range for the initial concentration of the toxicant. The model's parameters were estimated based on the data included in the TCRCs [1].

    Let $n(t)$ be the cell index, which closely reflects the cell population, $C_o(t)$ be the concentration of internal toxicants per cell, and $C_e(t)$ be the concentration of toxicants outside the cells at time $t$. We suppose that the toxicants do not exist in the cells before experiments, so $C_o(0 ) = 0$, and that $C_e(0 )$ is equal to the concentration of toxicant used in the experiments. We assume that the death rate of cells is linearly dependent on the concentration $C_o$ of internal toxicants and we consider linear kinetic, so we get the following deterministic model [1]:

    $ dn(t)dt=βn(t)γn2(t)αCo(t)n(t),
    $
    (1)
    $ dCo(t)dt=λ21Ce(t)η21Co(t),
    $
    (2)
    $ dCe(t)dt=λ22Co(t)n(t)η22Ce(t)n(t)
    $
    (3)

    Here $\beta>0$ denotes the cell growth rate, $\gamma = \frac{\beta}{K}$, where $K>0$ is the capacity volume, $\alpha>0$ is the cell death rate, $\lambda_1^2$ represents the uptake rate of the toxicant from environment, $\eta_1^2$ is the toxicant input rate to the environment, $\lambda_2^2$ is the toxicant uptake rate from cells, and $\eta_2^2$ represents the losses rate of toxicants absorbed by cells.

    The deterministic model (1)-(3) is a special case of the class of models proposed in [5], and it is related to the models considered in [7, 11, 15]. However, since we consider an acute dose of toxicant instead of a chronic one, the analysis of the survival/death of the cell population is different from the one done in the previously mentioned papers.

    We have noticed that, for the toxicants considered here, the estimated values of the parameters $\eta_1$, $\eta_2$, $\lambda_1$, and $\lambda_2$ verify $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$ [1]. In this case we have $0<C_e(t)\le C_e(0)$, $0\le C_o(t)\le \frac{\lambda_1^2C_e(0)}{\eta_1^2}$, and $n(t)>0$, for all $t\ge 0.$ (see Lemma 3.1 in [1]). Moreover from Theorem 3.2 in [1] we know that $\lim_{t\rightarrow \infty} C_e(t)$ exists and its value determines the asymptotic behavior of the system:

    1. If $\lim_{t\rightarrow \infty} C_e(t)<\frac{\beta\eta_1^2}{\alpha\lambda_1^2}$ then the population is uniformly persistent:

    $ \lim\limits_{t\rightarrow\infty} n(t) = K, ~~~~\lim\limits_{t\rightarrow\infty} C_o(t) = \lim\limits_{t\rightarrow\infty} C_e(t) = 0. $

    2. If $\lim_{t\rightarrow \infty} C_e(t)>\frac{\beta\eta_1^2}{\alpha\lambda_1^2}$ then $|n|_1 = \int_0^\infty n(t)dt<\infty$ and the population goes to local extinction:

    $ \lim\limits_{t\rightarrow\infty} n(t) = 0, ~~~ \lim\limits_{t\rightarrow\infty} C_o(t) = C_e^*\frac{\lambda_1^2}{\eta_1^2}, ~~~ \lim\limits_{t\rightarrow\infty} C_e(t) = C_e^*>\frac{\beta\eta_1^2}{\alpha\lambda_1^2}, $

    In practice we usually estimate a parameter by an average value plus an error term. To keep the stochastic model as simple as possible, we ignore the relationship between the parameters $\beta$ and $\gamma$, and we replace them by the random variables

    $ \tilde{\beta} = \beta+\text{error}_1~~~~, \tilde{\gamma} = \gamma+\text{error}_2 $ (4)

    By the central limit theorem, the error terms may be approximated by a normal distribution with zero mean. Thus we replace equation (1) by a stochastic differential equation and, together with equations (2) and (3), we get the stochastic model

    $ dn(t)=n(t)(βγn(t)αCo(t))dt+σ1n(t)dB1(t)σ2n2(t)dB2(t),
    $
    (5)
    $ dCo(t)=(λ21Ce(t)η21Co(t))dt,
    $
    (6)
    $ dCe(t)=(λ22Co(t)n(t)η22Ce(t)n(t))dt,
    $
    (7)

    Here $\sigma_i\ge 0$, $i = 1, 2$ are the noise intensities. $(\Omega, \mathcal{F}, \{\mathcal{F}_t\}_{t\ge 0}, \mathbb{P})$ is a complete probability space with an increasing, right continuous filtration $\{\mathcal{F}_t\}_{t\ge 0}$ such that $\mathcal{F}_0$ contains all $\mathbb{P}$-null sets, and $B_i$, $i = 1, 2$ are independent standard Brownian motions defined on the above probability space.

    Several versions of a stochastic logistic equation similar with (5) were considered in [18], [19], [8], [9], [10] and [21]. The system of stochastic differential equations (5)-(7) is closely related with the stochastic models in a polluted environment considered in [15], [16], and [24]. However, for the models considered in these papers, instead of the equations (6) and (7), $C_o(t)$ and $C_e(t)$ obey two linear equations without any terms involving $n(t)$. Moreover, instead of a combination of linear and quadratic terms as in (5), in [15] only a linear stochastic term is considered, and in [16] two stochastic competitive models are considered including exclusively either linear stochastic terms or quadratic stochastic terms.

    In this paper we extend the methods applied in [15] and [16] to find conditions for extinction, weakly persistence, and weakly stochastically permanence for the model (5)-(7). In addition to this we focus on the ergodic properties when the cell population is strongly persistent. The main contribution of this paper is the proof that $n(t)$ converges weakly to the unique stationary distribution. If only one of the noise variances $\sigma_1^2$, $\sigma_2^2$ is non-zero, we also determine the density of the stationary distribution. For the study of the ergodic properties we apply techniques used for stochastic epidemic models in [4], [28], [29] and [23], and for a stochastic population model with partial pollution tolerance in a polluted environment in [25].

    In the next section we prove that there is a unique non-negative solution of system (5)-(7) for any non-negative initial value. In section 3 we investigate the asymptotic behavior, and in section 4 we study the weak convergence of $n(t)$ to the unique stationary distribution using Lyapunov functions. Numerical simulations that illustrate our results are presented in section 5. The last section of the paper contains a short summary and conclusions.


    2. Existence and uniqueness of a positive solution

    We have to show that system (5)-(7) has a unique global positive solution in order for the stochastic model to be appropriate. Let $\mathbb{R}_+ = \left\{x\in\mathbb{R}:x\ge 0\right\}$, and $\mathbb{R}_+^* = \left\{x\in\mathbb{R}:x> 0\right\}$.

    Since equations (6) and (7) are linear in $C_o$ and $C_e$ we have

    $ Co(t)=Co(0)eη21t+λ21eη21tt0Ce(s)eη21sds
    $
    (8)
    $ Ce(t)=Ce(0)exp(η22t0n(s)ds)+λ22exp(η22t0n(s)ds)t0Co(s)n(s)exp(η22s0n(l)dl)ds,   t0.
    $
    (9)

    Let's define the differential operator $L$ associated with the system (5)-(7) by

    $ L=t+(βnγn2αCon)n+(λ21Ceη21Co)Co+(λ22Conη22Cen)Ce+12((σ21n2+σ22n4)22n)
    $

    For any function $V\in C^{2, 1}\left(\mathbb{R}^3 \times (0, \infty);\mathbb{R}\right)$, by Itô's formula ([17]) we have

    $ dV(x(t), t) = LV(x(t), t)dt+\frac{\partial V(x(t), t)}{\partial n}\left(\sigma_1 n(t)dB_1(t)-\sigma_2 n^2(t) dB_2(t)\right), \label{ito1} $ (10)

    where $x(t) = (n(t), C_o(t), C_e(t))'$, $t\ge 0$.

    Theorem 2.1. Let $D = \mathbb{R}_+^*\times \mathbb{R}_+\times \mathbb{R}_+^*$. For any given initial value $x(0)\in D$ the system (5)-(7) has a unique global positive solution almost sure (a.s.), i.e. $\mathbb{P}\{x(t)\in D, t\ge 0\} = 1$.

    Proof. The proof is similar with the proof of theorem 3.1 in [29]. Since the coefficients are locally Lipschitz continuous functions, there exists a unique solution on $[0, \tau_e)$, where $\tau_e$ is the explosion time ([3]). To prove that the solution is in $D$ and $\tau_e = \infty$ we define the stopping time

    $ τm=inf{t[0,τe):min{n(t),Ce(t)}m1 or max{n(t),Co(t),Ce(t)}m},
    $
    (11)

    where $m>m_0$ and $m_0> 0$ is a positive integer sufficiently large such that $n(0)\in [1/m_0, m_0]$, $0\le C_o(0)\le m_0$, and $C_e(0)\in [1/m_0, m_0]$. Here we set $\inf \emptyset = \infty$. Obviously $\{\tau_m\}$ is increasing and let $\tau_\infty = \lim_{n\rightarrow \infty}\tau_m$, where $0\le \tau_\infty\le \tau_e$ a.s.. From formula (8) it is easy to see that $C_o(t)\ge 0$ for any $t< \tau_\infty$.

    We show that $\tau_\infty = \infty$ a.s., so $\tau_e = \infty$ a.s. and the solution is in $D$ for any $t\ge 0$ a.s. Assume that there exists $T>0$, and $\epsilon>0$ such that $P(\tau_\infty\le T)>\epsilon$. Thus there exists an integer $m_1\ge m_0$ such that $P(\Theta_m)\ge\epsilon$ for any $m\ge m_1$, where $\Theta_m = \{\tau_m\le T\}$.

    We define the $C^3-$ function $V:D\rightarrow R_+^*$ as follows

    $ V(x) = C_o+\frac{\alpha}{4\lambda_2^2}(C_e-\log C_e-1)+\frac{\alpha C_e}{4\lambda_2^2}+\left(\sqrt{n}-\log\sqrt{n}-1\right)+n. $

    We get

    $ LV(x)=(λ21Ceη21Co)+α4λ22(11Ce)(λ22Conη22Cen)+α4λ22(λ22Conη22Cen)+(βnγn2αCon)(12n12n)+12(σ21n2+σ22n4)(14nn+12n2)+(βnγn2αCon)
    $

    Omitting some of the negative terms, for any $x\in D$ we have

    $ LV(x)λ21Ce+αCon4+αCon4+αCo2αCon+f(n),λ21Ce+αCo2+f(n),
    $

    where

    $ f(n)=σ22n2n8+α4λ21η22n+βn2+γn2+σ214+σ22n24+βn
    $

    Since $f$ is continuous on $(0, \infty)$ and $\lim_{n\rightarrow \infty}f(n) = -\infty$ it can easily be shown that $LV(x)\le C V(x)+C$, where the constant $C>0$ and $x\in D$.

    Let's define $\tilde{V}(t, x) = e^{-Ct}(1+V(x))$. We have

    $ L\tilde{V}(x, t) = -Ce^{-Ct}(1+V(x))+e^{-Ct}LV(x)\le 0. $

    Using Itô's formula (10) for $\tilde{V}$ and taking expectation we have for any $m\ge m_1$:

    $ E[˜V(x(tτm),tτm)]=˜V(x(0),0)+E[tτm0L˜V(x(uτm),uτm)du]˜V(x(0),0).
    $

    Notice that for any $\omega\in \Theta_m$, $m\ge m_1$ we have $V(x(\tau_m, \omega))\ge b_m = \min \{ V(y) | y = (y_1, y_2, y_3)'\text{ has the components } y_1 \text{ or } y_3 \text{ equal with } m^{-1}\text{ or } m, $ $\text{ or } y_2 = m\}$. Hence

    $ E\left[V(x(\tau_m, \omega))I_{\Theta_m}(\omega)\right]\ge P(\Theta_m)b_m\ge\epsilon b_m\rightarrow \infty $

    as $m\rightarrow \infty$. But $E\left[V(x(\tau_m, \omega))I_{\Theta_m}(\omega)\right]\le e^{CT}\tilde{V}(x(0), 0))<\infty$, for any $m\ge m_1$. Thus we have proved by contradiction that $\tau_\infty = \infty$.

    Here we focus on the case when $n(0)>0$, we have only an acute dose of toxicant $C_e(0)>0$, $C_o(0) = 0$, and the external concentration of toxicant $C_e(t)$ is never larger than $C_e(0)$. For this we have to impose some conditions on the parameters. Similarly with the deterministic case we obtain the following results (for completion the proofs are included in Appendix A and Appendix B).

    Lemma 2.2. If $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$, $n(0)>0$, $C_e(0)>0$, and $C_o(0) = 0$ then almost surely we have $0<C_e(t)\le C_e(0)$, $0\le C_o(t)\le \frac{\lambda_1^2C_e(0)}{\eta_1^2}$ for all $t\ge 0.$

    Theorem 2.3. If $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$, $n(0)>0$, $C_e(0)>0$, and $C_o(0) = 0$, then almost surely $\lim_{t\rightarrow\infty}C_o(t)$ and $\lim_{t\rightarrow\infty}C_e(t)$ exist and

    $ \lim\limits_{t\rightarrow\infty}C_o(t) = \frac{\lambda_1^2}{\eta_1^2}\lim\limits_{t\rightarrow\infty}C_e(t). $

    3. Survival analysis

    In this section we assume that $n(0)>0, C_o(0) = 0, C_e(0)>0$. We have the following definitions ([16]).

    Definition 3.1. The population $n(t)$ is said to go to extinction a.s. if $\lim_{t\rightarrow\infty}n(t) = 0$ a.s..

    Definition 3.2. The population $n(t)$ is weakly persistent a.s. if $\limsup_{t\rightarrow\infty}n(t)>0$ a.s..

    Definition 3.3. The population $n(t)$ is said to be strongly persistent a.s. if $\liminf_{t\rightarrow\infty}n(t)>0$ a.s..

    Definition 3.4. The population $n(t)$ is said to be stochastically permanent if for any $\epsilon>0$ there exist the positive constants $c_1(\epsilon)$ and $c_2(\epsilon)$ such that $\liminf\limits_{t\rightarrow\infty}P\biggl(n(t)$ $\le c_1(\epsilon)\biggl)\ge 1-\epsilon$ and $\liminf\limits_{t\rightarrow\infty}P\left(n(t)\ge c_2(\epsilon)\right)\ge 1-\epsilon$.

    Theorem 3.5. a. If $\beta -\frac{\sigma_1^2}{2}-\alpha\liminf\limits_{t\rightarrow\infty}\frac{\int_0^t C_o(s)ds}{t}<0$ a.s. then the population $n(t)$ goes exponentially to extinction a.s..

    b. If $\beta -\frac{\sigma_1^2}{2}-\alpha\liminf\limits_{t\rightarrow\infty}\frac{\int_0^t C_o(s)ds}{t}>0$ a.s. then the population $n(t)$ is weakly persistent a.s.

    Proof. The proof is similar with the proof of Theorem 6 in [16]. We start with some preliminary results. By Itô's formula in (5) we have

    $ d \ln n(t) = \left(\beta -\gamma n(t)- \alpha C_o(t)-\frac{\sigma_1^2+\sigma_2^2 n^2(t)}{2}\right)dt+\sigma_1 dB_1(t)-\sigma_2 n(t) dB_2(t).\notag $

    This means that we have

    $ lnn(t)lnn(0)=(βσ212)tγt0n(s)dsαt0Co(s)dsσ222t0n2(s)ds+σ1B1(t)σ2t0n(s)dB2(s),
    \label{eqex} $
    (12)

    Notice that the quadratic variation [17] of $M(t) = -\sigma_2 \int_0^t n(s) dB_2(s)$ is

    $ \langle M(t), M(t)\rangle = \sigma_2^2\int_0^t n^2(s) ds.\notag $

    Now we do the proof for part a. Using the exponential martingale inequality (Theorem 7.4 [17]) and Borel-Cantelli lemma ([22], pp. 102), and proceeding as in the proof of Theorem 6 in [16] we can show that for almost all $\omega$ there exists a random integer $n_0 = n_0(\omega)$ such that for all $n\ge n_0$ we have

    $ \sup\limits_{0\le t\le n}\left(M(t)-\frac{1}{2}\langle M(t), M(t)\rangle \right)\le 2 \ln n.\notag $

    Hence, for all $n\ge n_0$ and all $0\le t\le n$ we have

    $ -\frac{\sigma_2^2 }{2}\int_0^t n^2(s)ds-\sigma_2 \int_0^t n(s) dB_2(s)\le 2 \ln n \text{ a.s.}.\notag $

    Substituting the above inequality in (12) we get

    $ \frac{\ln n(t)-\ln n(0)}{t} \le \beta-\frac{\sigma_1^2}{2} - \alpha \frac{\int _0^t C_o(s)ds}{t}+\sigma_1\frac{B_1(t)}{t}+2\frac{\ln n}{n-1} \text{ a.s.}, \notag $

    for all $n\ge n_0$, and any $0<n-1\le t\le n$. Since $\lim_{t\rightarrow \infty}\frac{B(t)}{t} = 0$ a.s. (see Theorem 3.4 in [17]) we get

    $ \limsup\limits _{t\rightarrow \infty}\frac{\ln n(t)}{t}\le \beta-\frac{\sigma_1^2}{2} - \alpha \liminf\limits_{t\rightarrow \infty}\frac{\int _0^t C_o(s)ds}{t}<0\text{ a.s.}. $

    Next we prove part b. Suppose that $P(\Omega)>0$ where $\Omega = \{\lim\sup _{t\rightarrow \infty} n(t)\le 0\}$. From Theorem 2.1 we know that $n(t)> 0$, $t\ge 0 $ a.s., so $P(\Omega_1)>0$ where $\Omega_1 = \{\lim_{t\rightarrow \infty} n(t) = 0\}$, and $\Omega_1\subseteq \Omega$. Thus, for any $\omega \in \Omega_1$ we have

    $ \limsup\limits _{t\rightarrow \infty} \frac{\ln n(t, \omega)}{t}\le 0\label{contre1} $ (13)

    Moreover, from the law of large numbers for local martingales (Theorem 3.4 in [17]) there exists a set $\Omega_2\subseteq \Omega_1$ with $P(\Omega_2)>0$ such that for any $\omega\in \Omega_2$ we have

    $ \lim\limits_{t\rightarrow \infty}\frac{M(t, \omega)}{t} = \lim\limits_{t\rightarrow \infty}\frac{ B_1(t, \omega)}{t} = 0.\notag $

    From (12) we get:

    $ ln(n(t))t=ln(n(0))t+(βσ212)αt0Co(s)dstt0(γn(s)+σ222n2(s))dst+σ1B1(t)t+M(t,ω)t
    $

    Hence, for any $\omega\in \Omega_2$ we have

    $ \limsup\limits_{t\rightarrow \infty} \frac{\ln n(t, \omega)}{t} = \left(\beta-\frac{\sigma_1^2}{2}\right) - \alpha \liminf \limits_{t\rightarrow \infty}\frac{\int _0^t C_o(s, \omega)ds}{t}\notag $

    Since we know that $\beta -\frac{\sigma_1^2}{2}-\alpha\liminf\limits_{t\rightarrow\infty}\frac{\int_0^t C_o(s, \omega)ds}{t}>0$ a.s., we have a contradiction with (13), so $\limsup_{t\rightarrow\infty}n(t)>0$ a.s.

    We have the following result regarding the expectation of $n(t)$.

    Lemma 3.6. There exists a constant $K_1>0$ such that $\sup_{t\ge 0}E[n(t)]\le K_1$.

    Proof. Using Itô's formula in (5) we get:

    $ d(etn(t))=n(t)et(1+βαCo(t)γn(t))dt+σ1n(t)etdB1(t)σ2n2(t)etdB2(t)n(t)et(1+βγn(t))dt+σ1n(t)etdB1(t)σ2n2(t)etdB2(t)et(1+β)24γdt+σ1n(t)etdB1(t)σ2n2(t)etdB2(t)
    $
    (14)

    Let

    $ \eta_m = \inf\{t\ge 0: n(t)\notin(1/m, m)\}, \label{infi2} $ (15)

    for any $m>m_0$, where $m_0>0$ was defined in the proof of Theorem 2.1. Obviously $\eta_m\ge\tau_m$, $m>m_0$, where $\tau_m$ is given in (11). In Theorem 2.1 we have proved that $\lim_{m\rightarrow\infty}\tau_m = \infty$ a.s., so we also have $\lim_{m\rightarrow\infty}\eta_m = \infty$ a.s.. Taking expectation in (14) we get:

    $ E\left[e^{t\wedge \tau_m} n(t\wedge \tau_m)\right]\le n(0)+E\left[\int_0^{t\wedge \tau_m} e^s\frac{(1+\beta)^2}{4\gamma} ds\right]\le n(0)+\frac{(1+\beta)^2}{4\gamma}(e^t-1).\notag $

    Letting $m\rightarrow \infty$ we get

    $ E\left[n(t)\right]\le \frac{n(0)}{e^t }+\frac{(1+\beta)^2}{4\gamma}(1-e^{-t}). $

    Thus, there exists a constant $K_1>0$ such that $\sup_{t\ge 0}E[n(t)]\le K_1$.

    Corollary 1. For any $\epsilon>0$ there exists $c_1(\epsilon)$ such that $\lim\inf _{t\rightarrow\infty}\mathbb{P}(n(t)\le c_1(\epsilon))\ge 1-\epsilon$.

    Proof. For any $\epsilon>0$, set $c_1(\epsilon) = K_1/\epsilon$, where the constant $K_1>0$ is given in the previous lemma. From the Markov's inequality [22] we obtain

    $ P\left(n(t)>c_1(\epsilon)\right)\le \frac{E[n(t)]}{c_1(\epsilon)}. $

    Hence, from Lemma 3.6 we get

    $ \limsup\limits_{t\rightarrow \infty}P\left(n(t)>c_1(\epsilon)\right)\le \limsup\limits_{t\rightarrow \infty}\frac{E[n(t)]}{c_1(\epsilon)}\le \epsilon. $

    Theorem 3.7. If $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$ and $\beta -\sigma_1^2-\alpha\frac{\lambda_1^2C_e(0)}{\eta_1^2}>0$, then the cell population is stochastically permanent.

    Proof. First we show that $\lim\sup_{t\rightarrow \infty}E[1/n(t)]\le M_2$, where $M_2$ is a positive constant.

    By Itô's formula in (5) we get for any real constant c:

    $ d(ectn(t))=ect(1n(t)(cβ+σ21+αCo(t))+γ+σ22n(t))dtσ1ectn(t)dB1(t)+σ2ectdB2(t)
    $

    Since $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$, from Lemma 2.2 we know that $0\le C_o(t)\le \frac{\lambda_1^2C_e(0)}{\eta_1^2}$ for all $t\ge 0$ a.s.. We choose any $0<c<\beta -\sigma_1^2-\alpha \frac{\lambda_1^2C_e(0)}{\eta_1^2}$, and we get:

    $ d\left(\frac{e^{ct}}{n(t)}\right)\le e^{ct}\biggl(\gamma +\sigma_2^2n(t)\biggl)dt-\frac{\sigma_1e^{ct}}{n(t)}dB_1(t)+\sigma_2 e^{ct}dB_2(t)\label{eqa2} $ (16)

    Taking expectation in (16) and using Lemma 3.6 we get:

    $ E[ec(tηm)n(tηm)]1n(0)+E[tηm0ecs(γ+σ22n(s))ds]1n(0)+(γ+σ22K1)(ect1)c,
    $

    where $\eta_m$ was defined in (15). Letting $m\rightarrow \infty$ we get

    $ E\left[\frac{1}{n(t)}\right]\le \frac{1}{n(0)e^{ct} }+\frac{\left(\gamma+\sigma_2^2 K_1\right)}{c}(1-e^{-ct}), $

    so $\limsup\limits_{t\rightarrow \infty}E[1/n(t)]\le M_2$, where $0<M_2 = (\gamma+\sigma_2^2 K_1)/c$.

    Next we show that for any $\epsilon>0$ there exists $c_2(\epsilon)$ such that $\liminf\limits _{t\rightarrow\infty}\mathbb{P}(n(t)\ge c_2(\epsilon))\ge 1-\epsilon$.

    For any $\epsilon>0$ set $c_2(\epsilon) = \epsilon/M_2$. From Markov's inequality we have

    $ \mathbb{P}(n(t)< c_2(\epsilon)) = \mathbb{P}\left(\frac{1}{n(t)}>\frac{1}{c_2(\epsilon)}\right)\le c_2(\epsilon)E\left[\frac{1}{n(t)}\right] $

    Hence

    $ \limsup\limits_{t\rightarrow\infty}\mathbb{P}\left(n(t)< c_2(\epsilon)\right)\le \epsilon\limsup\limits_{n\rightarrow \infty}E[1/n(t)]/M_2\le\epsilon. $

    Thus $\liminf_{t\rightarrow\infty}\mathbb{P}(n(t)\ge c_2(\epsilon))\ge 1-\epsilon$, and this inequality and Corollary 1 implies that $n(t)$ is stochastically permanent.


    4. Stationary distributions

    The deterministic system (1)-(3) has a maximum capacity equilibrium point $(K, 0, 0)^{'}$, where $K$ is the capacity volume ([1]). For the stochastic system (5)-(7), $(K, 0, 0)^{'}$ is not a fixed point, and, when the cell population is persistent, we no longer have $\lim_{t\rightarrow\infty} n(t) = K$. In this section we study the asymptotic behavior of $n(t)$ when $\lim_{t\rightarrow \infty} C_o(t) = 0$ a.s..

    For stochastic differential equations, invariant and stationary distributions play the same role as fixed points for deterministic differential equations. In general, let $X(t)$ be the temporally homogeneous Markov process in $E\subseteq \mathbb{R}^l$ representing the solution of the stochastic differential equation

    $ dX(t) = b(X(t))dt+\sum\limits_{r = 1}^d \sigma_r(X(t))dB_r(t), \label{eqerg1} $ (17)

    where $B_r(t)$, $r = 1, \ldots, d$ are standard Brownian motions. We define the operator $L$ associated with equation (17):

    $ L = \sum\limits_{i = 1}^l b_i(x)\frac{\partial}{\partial x_i}+\frac{1}{2}\sum\limits_{i, j = 1}^l A_{i, j}(x)\frac{\partial^2}{\partial x_i\partial x_j}, ~~~ A_{i, j}(x) = \sum\limits_{r = 1}^d \sigma_{r, i}(x)\sigma_{r, j}(x).\notag $

    Let $P(t, x, \cdot)$ denote the probability measure induced by $X(t)$ with initial value $X(0) = x\in E$: $P(t, x, A) = P(X(t)\in A|X(0) = x)$, $A\in \mathcal{B}(E)$, where $\mathcal{B}(E)$ is the $\sigma-$algebra of all the Borel sets $A\subseteq E$.

    Definition 4.1. A stationary distribution [6] for $X(t)$ is a probability measure $\mu$ for which we have

    $ \int_E P(t, x, A)\mu(dx) = \mu(A), \text{ for any }t\ge 0, \text{ and any }A\in \mathcal{B}(E).\notag $

    Definition 4.2. The Markov process $X(t)$ is stable in distribution if the transition distribution $P(t, x, \cdot)$ converges weakly to some probability measure $\mu(\cdot)$ for any $x\in E$.

    It is clear that the stability in distribution implies the existence of a unique stationary measure, but the converse is not always true [2]. We have the following result (see lemma 2.2 in [29] and the references therein).

    Lemma 4.3. Suppose that there exists a bounded domain $U\subseteq E$ with regular boundary, and a non-negative $C^2-function$ $V$ such that $A(x) = (A_{i, j}(x))_{1\le i, j\le l}$ is uniformly elliptical in $U$ and for any $x\in E \setminus U$ we have $LV(x)\le -C$, for some $C>0$. Then the Markov process $X(t)$ has a unique stationary distribution $\mu(\cdot)$ with density in $E$ such that for any Borel set $B\subseteq E$

    $ limtP(t,x,B)=μ(B)Px{limT1TT0f(X(t))dt=Ef(x)μ(dx)}=1,
    $

    for all $x\in E$ and $f$ being a function integrable with respect to the probability measure $\mu$.

    We now study the stochastic system (5)-(7) when $\lim_{t\rightarrow \infty}C_o(t) = 0$ a.s.. We introduce two new stochastic process $X(t)$ and $X_{\epsilon}(t)$ which are defined by the initial conditions $X(0) = X_\epsilon (0) = n(0)\in \mathbb{R}_+^*$ and the stochastic differential equations

    $ dX(t)=(βX(t)γX2(t))dt+σ1X(t)dB1(t)σ2X2(t)dB2(t),
    $
    (18)
    $ dXϵ(t)=(βXϵ(t)γX2ϵ(t)αϵXϵ(t))dt+σ1Xϵ(t)dB1(t)σ2X2ϵ(t)dB2(t),
    $
    (19)

    Lemma 4.4. a. For any given initial value $X(0)>0$, the equation (18) has a unique global solution $X(t)$ such that $\mathbb{P}\left\{ X(t)>0, t\ge 0\right\} = 1$.

    b. For any $\epsilon>0$ and any given initial value $X_\epsilon(0)>0$, the equation (19) has a unique global solution $X_\epsilon(t)$ such that $\mathbb{P}\left\{ X_\epsilon(t)>0, t\ge 0\right\} = 1$.

    c. There exists a constant $C_1>0$ such that $\sup_{t\ge 0} E[X(t)]\le C_1$ and, for any $\epsilon>0$, $\sup_{t\ge 0} E[X_\epsilon(t)]\le C_1$.

    Proof. The proofs for a. and b. can be done similarly with the proof of Theorem 2.1, using the $C^2$-function $V:\mathbb{R}_+^*\rightarrow \mathbb{R}_+$, $V(x) = \sqrt{x}-\log\sqrt{x}-1$. The proof of c. is analogous with the proof of Lemma 3.6.

    Let $P_{X}(t, x, \cdot)$ denote the probability measure induced by $X(t)$ with initial value $X(0) = x\in \mathbb{R}_+^*$, $t\ge 0$. In the following theorem, using Lemma 4.3, we show that the Markov process $X(t)$ is stable in distribution.

    Theorem 4.5. If $\sigma_1^2<2\beta$ then the Markov process $X(t)$ has a unique stationary distribution $\mu_1(\cdot)$ with density in $\mathbb{R}_+^*$ such that for any Borel set $B\subseteq \mathbb{R}_+^*$

    $ limtPX(t,x,B)=μ1(B)Px{limT1TT0f(X(t))dt=Ef(x)μ1(dx)}=1,
    $

    for all $x\in \mathbb{R}_+^* $ and $f$ being a function integrable with respect to the probability measure $\mu_1$.

    Proof. We consider the $C^2$-function $V:\mathbb{R}_+^*\rightarrow \mathbb{R}_+$, $V(x) = \sqrt{x}-\log\sqrt{x}-1$. Simple calculations show that

    $ LV(x) = -\frac{\sigma_2^2}{8}x^{5/2}+\frac{\sigma_2^2}{4}x^2-\frac{\gamma}{2}x^{3/2}+\frac{\gamma}{2}x+\left(\frac{\beta}{2}-\frac{\sigma_1^2}{8}\right)x^{1/2} +\left(\frac{\sigma_1^2}{4}-\frac{\beta}{2}\right). $

    Since $LV(\cdot)$ is a continuous function on $\mathbb{R}_+^*$ and $LV(0) = \frac{\sigma_1^2}{4}-\frac{\beta}{2}<0$, there exists a constant $A_1>0$ such that $LV(x)<-C_1$ for any $x\in (0, A_1]$, for some $C_1>0$. We also have $\lim_{x\rightarrow\infty}LV(x) = -\infty$. Thus, there exists a constant $A_2>A_1>0$ such that $LV(x)<-C_2$ for any $x\in [A_2, \infty)$, for some $C_2>0$.

    Let $U = (A_1, A_2)\subset \mathbb{R}_+^* $. Then $U$ is a bounded domain, and $LV(x)<-C$ for any $x\in \mathbb{R}_+^*\setminus U$, where $C>0$ is the minimum between $C_1$ and $C_2$. Notice that $A(x) = \sigma_1^2x^2+\sigma_2^2x^4$ is uniformly elliptical on $U$, so the assumptions of Lemma 4.3 are met. Therefore, the Markov process $X(t)$ has a unique stationary distribution $\mu_1(\cdot)$ and it is ergodic.

    Let define the processes $N(t) = 1/n(t)$ a.s., $Y(t) = 1/X(t)$ a.s., $Y_\epsilon(t) = 1/X_\epsilon(t)$ a.s., $t\ge 0$, with $N(0) = Y(0) = Y_\epsilon(0) = 1/n(0)>0$. Then from Lemma 4.4 and Theorem 2.1 we have $\mathbb{P}\{ N(t)>0, Y(t)>0, Y_\epsilon(t)>0, t\ge 0\} = 1$. Applying Itô's formula in equations (5), (18) and (19) we get

    $ dN(t)=(N(t)(σ21β)+αN(t)Co(t)+γ+σ22N(t))dtσ1N(t)dB1(t)+σ2dB2(t) a.s.,
    $
    (20)
    $ dY(t)=(Y(t)(σ21β)+γ+σ22Y(t))dtσ1Y(t)dB1(t)+σ2dB2(t) a.s.,
    $
    (21)
    $ dYϵ(t)=(Yϵ(t)(σ21β+αϵ)+γ+σ22Yϵ(t))dtσ1Yϵ(t)dB1(t)+σ2dB2(t) a.s..
    $
    (22)

    From the proof of Theorem 3.7 we know that if $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$ and $\beta-\sigma_1^2-\alpha\frac{\lambda_1^2C_e(0)}{\eta_1^2}>0$ then there exist a constant $K_2>0$ such that $\sup_{t\ge 0} E[N(t)]\le K_2$. We have similar results for the processes $Y(t)$ and $Y_\epsilon(t)$.

    Lemma 4.6. If $\sigma_1^2<\beta$ then $\sup_{t\ge 0} E[Y(t)]< \infty$ and $\sup_{t\ge 0} E[Y_\epsilon(t)]< \infty$, for any $0<\epsilon<\frac{\beta-\sigma_1^2}{\alpha}$.

    Proof. The proof is based on the results in Lemma 4.4 and it is similar with the first part of the proof of Theorem 3.7. For completeness we have included it in Appendix C.

    We use the processes $N(t)$, $Y(t)$, $Y_\epsilon(t)$ to prove the main result of this section.

    Theorem 4.7. Let $(n(t), C_o(t), C_e(t))$ be the solution of the system (5)-(7) with any initial value $(n(0), C_o(0), C_e(0))'\in D = \mathbb{R}_+^*\times \mathbb{R}_+\times \mathbb{R}_+^*$. If $\lim_{t\rightarrow \infty} C_o(t) = 0$ a.s. and $\beta -\sigma_1^2>0$ then $n(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \mu_1$, where $\overset{w}{\rightarrow}$ means convergence in distribution (weak convergence [22]) and $\mu_1$ is the probability measure on $\mathbb{R_+^*}$ given in Theorem 4.5.

    Proof. We follow the same idea as in the proof of Theorem 2.4 in [28]. From theorem 4.5 we know that $X(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \mu_1$, where $\mu_1$ is a probability measure on $\mathbb{R_+^*}$. By the Continuous Mapping Theorem [22], $Y(t) = 1/X(t)$ also converges weakly to a probability measure $\nu_1$ on $\mathbb{R_+^*}$, the reciprocal of $\mu_1$. We will show that $N(t) = 1/n(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \nu_1$.

    Firstly, let's notice that

    $ Y(t)\le N(t) \text{ and } Y(t)\le Y_\epsilon(t) \text{ for any } t\ge 0 \text{ a.s..} \label{fact1} $ (23)

    Indeed, if we denote $\xi(t) = N(t)-Y(t)$, then $\xi(0) = 0$ and from equations (20) and (21) we get

    $ d\xi(t) = \left(\xi(t)\left(\sigma_1^2-\beta-\frac{\sigma_2^2}{N(t)Y(t)}\right)+\alpha N(t) C_o(t)\right)dt-\sigma_1\xi(t)dB_1(t)\text{ a.s.}. $

    The solution of the previous linear equation is given by (see chapter 3, [17])

    $ \xi(t) = \Phi(t)\int_0^t \frac{\alpha N(s) C_o(s)}{\Phi(s)}ds\text{ a.s.}, $

    where

    $ \Phi(t) = \exp\left\{ -t\left(\beta-\frac{\sigma_1^2}{2}\right)-\int_0^t\frac{\sigma_2^2}{N(s)Y(s)}ds-\sigma_1 B_1(t) \right\}>0 $

    Obviously $\xi(t)\ge 0$, $t\ge 0$, a.s., and this means that we have $Y(t)\le N(t)$ for any $t\ge 0$ a.s. Similarly, using equations (21) and (22), we can show that $Y(t)\le Y_\epsilon(t)$ for any $t\ge 0$ a.s..

    Secondly we show that for any $0<\epsilon<\frac{2\beta-\sigma_1^2}{2\alpha}$

    $ {\lim\inf}_{t\rightarrow\infty}(Y_\epsilon(t)-N(t))\ge 0\text{ a.s.}.\label{fact2} $ (24)

    From equations (20) and (22) we get

    $ d(Yϵ(t)N(t))=((Yϵ(t)N(t))(σ21+αϵβσ22N(t)Yϵ(t))+αN(t)(ϵCo(t)))dtσ1(Yϵ(t)N(t))dB1(t) a.s..
    $

    The solution of the linear equation is given by

    $ Y_\epsilon(t)-N(t) = \Phi_1(t)\int_0^t \frac{\alpha N(s) \left(\epsilon-C_o(s)\right)}{\Phi_1(s)}ds\text{ a.s.}, $

    where

    $ 0<Φ1(t)=exp{t(βαϵσ212)t0σ22N(s)Yϵ(s)dsσ1B1(t)}exp{t(βαϵσ212+σ1B1(t)t)}
    $

    Since $\lim_{t\rightarrow \infty} B_1(t)/t = 0$ a.s., for any $0<\epsilon<\frac{2\beta-\sigma_1^2}{2\alpha}$ we get $\lim_{t\rightarrow \infty}\Phi_1(t) = 0 $ a.s.. Moreover, because $\lim_{t\rightarrow \infty}C_o(t) = 0$ a.s., for almost any $\omega$ there exist $0<T = T(\omega)$ such that $\epsilon-C_o(t, \omega)>0$ for any $t>T(\omega)$. Thus for almost any $\omega$ and any $t>T$,

    $ Yϵ(t)N(t)=Φ1(t)(T0αN(s)(ϵCo(s))Φ1(s)ds+tTαN(s)(ϵCo(s))Φ1(s)ds)Φ1(t)T0αN(s)(ϵCo(s))Φ1(s)ds
    $

    Therefore for any $0<\epsilon<\frac{2\beta-\sigma_1^2}{2\alpha}$ we have

    $ \liminf\limits_{t\rightarrow\infty}(Y_\epsilon(t)-N(t))\ge \lim\limits_{t\rightarrow\infty} \Phi_1(t)\int_0^T \frac{\alpha N(s) \left(\epsilon-C_o(s)\right)}{\Phi_1(s)}ds = 0 ~~~a.s.. $

    Thirdly we prove that

    $ \lim\limits_{\epsilon\rightarrow 0}\lim\limits_{t\rightarrow\infty}E[Y_\epsilon(t)-Y(t)] = 0.\label{fact3} $ (25)

    We know from (23) that $Y_\epsilon(t)-Y(t)\ge 0$, $t\ge 0$ a.s. Using equations (21) and (22) we get

    $ d(Yϵ(t)Y(t))=((Yϵ(t)Y(t))(σ21+αϵβσ22Y(t)Yϵ(t))+αϵY(t))dtσ1(Yϵ(t)Y(t))dB1(t)((Yϵ(t)Y(t))(σ21+αϵβ)+αϵY(t))dtσ1(Yϵ(t)Y(t))dB1(t) a.s..
    $

    From Lemma 4.6 we know that $\sup_{t\ge 0} E[Y(t)]<\infty$, so taking expectations in the previous inequality we have

    $ E[Yϵ(t)Y(t)]t0E[Yϵ(s)Y(s)](σ21+αϵβ)+αϵE[Y(s)]dst0E[Yϵ(s)Y(s)](σ21+αϵβ)ds+tαϵsupt0E[Y(t)] a.s..
    $

    For any $0<\epsilon< (\beta-\sigma_1^2)/\alpha $, by the comparison theorem (see theorem 1.4.1 in [14]) we get

    $ 0\le E[Y_\epsilon(t)-Y(t)]\le\frac{\alpha\epsilon \sup\limits_{t\ge 0}E[Y(t)] }{\beta-\sigma_1^2-\alpha\epsilon}\left(1-\exp(-t(\beta-\sigma_1^2-\alpha\epsilon))\right) $

    Taking limits in the previous inequality we get equation (25).

    Finally, using (23), (24), and (25) we obtain that $\lim_{t\rightarrow \infty}(N(t)-Y(t)) = 0$, in probability. But it has been shown that $Y(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \nu_1$, where $\nu_1$ is a probability measure on $\mathbb{R_+^*}$. Thus, from Slutsky's theorem [22], $N(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \nu_1$, and, by the Continuous Mapping Theorem, $n(t) = 1/N(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \mu_1$.

    Corollary 2. Let $(n(t), C_o(t), C_e(t))$ be the solution of the system (5)-(7) with any initial value $(n(0), C_o(0), C_e(0))'\in D$, and such that $\lim_{t\rightarrow \infty} C_o(t) = 0$ a.s..

    a. If $\sigma_1 = 0$ then $n(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \mu_1$ where $\mu_1$ is the probability measure on $\mathbb{R_+^*}$ with density

    $ p(x)=1G1x4exp(βσ22(1xγβ)2),x>0
    $
    (26)
    $ G1=σ22β5/2(Ψ(γ2ββσ2)π(σ22β+2γ2)+γσ2β1/2exp(γ2σ22β))
    $
    (27)

    where $\Psi(x) = \mathbb{P}(Z\le x)$ is the distribution function for the standard normal distribution $Z\sim N(0, 1)$.

    b. If $\sigma_1^2<\beta$ and $\sigma_2 = 0$ then $n(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \mu_1$ where $\mu_1$ is a gamma distribution with shape parameter $\frac{2(\beta-\sigma_1^2)}{\sigma_1^2}+1$ and scale parameter $\frac{\sigma_1^2}{2\gamma}$.

    Proof. We know that $Y(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \nu_1$, where $\nu_1$ is a probability measure on $\mathbb{R_+^*}$. When $\sigma_1 = 0$ or $\sigma_2 = 0$ we can prove the ergodicity of $Y(t)$ directly using Theorem 1.16 in [13].

    a. If $\sigma_1 = 0$ the equation (21) become

    $ dY(t) = \left(-Y(t)\beta+\gamma+\frac{\sigma_2^2}{Y(t)}\right)dt+\sigma_2dB_2(t)\text{ a.s.}, \label{eqi2new} $ (28)

    Let define

    $ q(y)=exp(2σ22y1(βu+σ22u+γ)du)=1y2exp(βσ22(1γβ)2)exp(βσ22(yγβ)2)
    $

    It can be easily shown that

    $ \int_0^1 q(y)dy = \infty, ~~ \int_1^\infty q(y)dy = \infty, ~~\int_0^\infty\frac{1}{\sigma_2^2 q(y)}dy = \frac{G_1}{\sigma_2^2}\exp\left(\frac{\beta}{\sigma_2^2}\left(1-\frac{\gamma}{\beta}\right)^2\right), \notag $

    where $G_1$ is given in (27). So, by Theorem 1.16 in [13], $Y(t)$ is ergodic and with respect to the Lebesgue measure its stationary measure $\nu_1$ has density

    $ p_1(x) = \frac{1}{\sigma_2^2q(x)\int_0^\infty\frac{1}{\sigma_2^2 q(y)}dy} = \frac{x^2\exp\left(-\frac{\beta}{\sigma_2^2}\left(x-\frac{\gamma}{\beta}\right)^2\right)}{G_1} $

    Thus, by Theorem 4.5, $X(t) = 1/Y(t)$ is ergodic and its stationary measure $\mu_1$ is the reciprocal of the measure $\nu_1$, so with respect to the Lebesgue measure has density $p(x) = p_1(1/x)/x^2$ given in equation (26). Notice that we also have

    $ limt1tt0X(u)du=0xp(x)dx=σ22β3/2G1(σ2βexp(γ2σ22β)+2γπΨ(γ2ββσ2)) a.s..
    $

    b. If $\sigma_2 = 0$, then the equation (21) becomes

    $ dY(t) = \left(\gamma-Y(t)(\beta-\sigma_1^2)\right)dt-\sigma_1Y(t)dB_1(t)\text{ a.s.}. $

    Proceeding similarly as for a. we can show that $\nu_1$ is the reciprocal gamma distribution with shape parameter $\frac{2(\beta-\sigma_1^2)}{\sigma_1^2}+1$ and scale parameter $\frac{\sigma_1^2}{2\gamma}$ (see also the proof of Theorem 4.5 in [29]). Thus, by Theorem 4.5, $X(t) = 1/Y(t)$ is ergodic and its stationary measure $\mu_1$ is the gamma distribution with shape parameter $\frac{2(\beta-\sigma_1^2)}{\sigma_1^2}+1$ and scale parameter $\frac{\sigma_1^2}{2\gamma}$. Since the mean for this gamma distribution is $\left(\frac{2(\beta-\sigma_1^2)}{\sigma_1^2}+1\right)\frac{\sigma_1^2}{2\gamma}$, we also have

    $ \lim\limits_{t\rightarrow \infty}\frac{1}{t}\int_0^tX(u)du = \left(\frac{2(\beta-\sigma_1^2)}{\sigma_1^2}+1\right)\frac{\sigma_1^2}{2\gamma}\text{ a.s..} $

    Notice that if $\sigma_1^2>2\beta -2\alpha\lim\inf_{t\rightarrow\infty}\frac{\int_0^t C_o(s)ds}{t}$ a.s. then, according to Theorem 3.5, $\lim_{t\rightarrow \infty}n(t) = 0$, so $n(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \delta_0$, where $\delta_0$ is the Dirac distribution centered in $0$.

    On the other hand, if $\sigma_1^2<\beta$, $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$, and $\lim\inf_{t\rightarrow \infty}n(t)>0$ a.s., then according to Theorem 4.7 $n(t)\overset{w}{\underset{t\rightarrow \infty}{\rightarrow}} \mu_1$. Indeed, since $\lim\inf_{t\rightarrow \infty}n(t)>0$ a.s., then $\int_0^\infty n(t)dt = \infty$ a.s., and from the proof of Theorem 2.3 we know that $\lim_{t\rightarrow \infty}C_o(t) = \lim_{t\rightarrow \infty}C_e(t) = 0$, so the assumptions of Theorem 4.7 are satisfied.


    5. Numerical simulations

    First we illustrate numerically the results obtained in section 3 regarding survival analysis.We consider a cell population exposed to the toxicant monastrol as in the experiments described in [1]. The parameters' values for this toxicant are estimated in [1]: $\beta = 0.074$, $K = 18.17$, $\eta_1 = 0.209$, $\lambda_1 = 0.177$, $\lambda_2 = 0.204$, $\eta_2 = 0.5$, and $\alpha = 0.016$. Notice that for this toxicant we have $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$. We solve numerically the system (5)-(7) using an order 2 strong Taylor numerical scheme [12].

    One of the applications of the mathematical model is for finding the threshold value for $C_e(0)$ at which the population becomes extinct. This value depends on the initial value $n(0)$ and for the deterministic model (1)-(3) can be found numerically (see also Fig. 3 in [1]). From Theorem 3.5 we can see that large values of the noise variance $\sigma_1^2$ result in population extinction, so we expect that the presence of noise will lower the values of the threshold.

    Figure 3. Trajectories corresponding to initial values $n(0) = 2.5$, $C_o(0) = 0$, $\sigma_1 = 0$, $\sigma_2 = 0.002$: blue "- -" line deterministic model, $C_e(0) = 380$; red "-" line stochastic model, $C_e(0) = 380$; green "-.-" line stochastic model, $C_e(0) = 379$.

    We illustrate this for the model with initial values $n(0) = 2.5$ and $C_o(0) = 0$. In the deterministic case the threshold value where the population goes extinct can be found numerically, and it is approximately $C_e^{det}(0) = 382.2$. We can see in Fig. 2 (a) that for the stochastic model with $\sigma_1 = 0.01$, $\sigma_2 = 0$ and initial value $C_e(0) = 380$ the population goes to extinction, while in the deterministic case ($\sigma_1 = \sigma_2 = 0$) the population is persistent for these initial values. According to Fig. 2 (a), in the stochastic case the threshold value for this simulation is $C_e^{stoch}(0)\in(375, 380)$. Similar results are obtained for the stochastic model with $\sigma_1 = 0$, $\sigma_2 = 0.001$ and are presented in Fig. 3 (a). For this simulation the threshold value in the stochastic case is $C_e^{stoch}(0)\in(379, 380)$.

    Figure 2. Trajectories corresponding to initial values $n(0) = 2.5$, $C_o(0) = 0$, $\sigma_1 = 0.01$, $\sigma_2 = 0$: blue "- -" line deterministic model, $C_e(0) = 380$; red "-" line stochastic model, $C_e(0) = 380$; green "-.-" line stochastic model, $C_e(0) = 375$.

    Notice also that the results displayed in Figs. 2 and 3 agree with the conclusion of Theorem 2.3. For the stochastic model with $C_e(0) = 380$, for the simulations presented in Figs. 2 and 3 we have $\lim_{t\rightarrow \infty}n(t, \omega) = 0$ (the trajectories plotted with red plain lines). For $\sigma_1 = 0.01$ and $\sigma_2 = 0$ we can see that $\lim_{t\rightarrow \infty}C_o(t, \omega) = 5.3819$ and $\lim_{t\rightarrow \infty}C_e(t, \omega) = 7.494$ (the trajectories plotted with red plain lines in Fig. 2 (b), (c)). For $\sigma_1 = 0$ and $\sigma_2 = 0.002$ from Fig. 3 (b), (c) we can notice that $\lim_{t\rightarrow \infty}C_o(t, \omega) = 5.255$ and $\lim_{t\rightarrow \infty}C_e(t, \omega) = 7.3173$. For both simulation we have $\lim_{t\rightarrow\infty} C_o(t) = \frac{\lambda_1^2}{\eta_1^2}\lim_{t\rightarrow\infty} C_e(t)$, as given in Theorem 2.3. Moreover, for the stochastic model with $C_e(0) = 375$, $\sigma_1 = 0.01$ and $\sigma_2 = 0$ (the green dot -dashed lines in Fig. 2) and the model with $C_e(0) = 379$, $\sigma_1 = 0$ and $\sigma_2 = 0.002$ (the green dot -dashed lines in Fig. 3), we have

    $ \liminf\limits_{t\rightarrow\infty} n(t, \omega)>0, \lim\limits_{t\rightarrow \infty}C_o(t, \omega) = \lim\limits_{t\rightarrow \infty}C_e(t, \omega) = 0 $

    Next we use the same parameters values as stated at the beginning of this section and the initial values $n(0) = 2.5$, $C_o(0) = 0$, $C_e(0) = 1.8$ to illustrate the stability in distribution of the process $n(t)$. For both $\sigma_1 = \sigma_2 = 0.001$ and $\sigma_1 = \sigma_2 = 0.005$ the assumptions of Theorem 4.7 are met. In Figs. 4 (a) and (c) we show the histograms of the result of running 10 000 simulations of the path $n(t)$ for a long run of 5 000 0000 iterations, but storing only the last of these $n(t)$ values. For comparison Figs. 4 (b) and (d) show the histograms of the last 4 000 000 samples from a single run of 5 000 000 iterations. For both sets of values for $\sigma_1$ and $\sigma_2$ the corresponding histograms are similar. Because of this similarity and of the huge number of iterations considered, we may assume that the probability distribution of $n(t)$ has more or less reached the distribution $\mu_1$ given in Theorem 4.7.

    Figure 4. Histograms of the values of n(t) for the last iteration from 10 000 runs (a) and (c) and for the last 4 000 000 samples out of 5 000 000 sample of a single run (b) and (d).

    When $\sigma_2 = 0$ or $\sigma_1 = 0$, the density of the probability distribution $\mu_1$ is given in Corollary 2 (a) and (b), respectively. To illustrate these results we use the same parameter values as stated at the beginning of this section and the initial values $n(0) = 2.5$, $C_o(0) = 0$, $C_e(0) = 180$. For $\sigma_1 = 0$ and several values of $\sigma_2$ we display the histograms for the last 4 000 000 samples of a single run of 5 000 000 iterations (shaded areas in Fig. 5) and the graph of the corresponding density given in Corollary 2 (a). In Fig. 6 we do similar plots for $\sigma_2 = 0$, several values of $\sigma_1$, and the density of the corresponding Gamma distribution in Corollary 2 (b). We can notice that the histograms give very accurate approximations for the densities in Corollary 2. Also, in both Fig. 5 and Fig. 6, when the values of $\sigma_1$ or $\sigma_2$ increase, the histograms become right skewed. Moreover, for large values of $\sigma_1$ or $\sigma_2$ the population becomes extinct and $\mu_1 = \delta_0$ (see also Theorem 3.5).

    Figure 5. Histograms for the last 4 000 000 samples of a single run of 5 000 000 iterations and corresponding density functions a. $\sigma_2 = 0.001$ b. $\sigma_2 = 0.01$ c. $\sigma_2 = 0.1$ d. $\sigma_2 = 0.15$.
    Figure 6. Histograms for the last 4 000 000 samples of a single run of 5 000 000 iterations and corresponding Gamma density functions a. $\sigma_1 = 0.001$ b. $\sigma_1 = 0.01$ c. $\sigma_1 = 0.1$ d. $\sigma_1 = 0.5$.

    6. Conclusions

    We present a stochastic model to study the effect of toxicants on human cells. To account for parameter uncertainties, the model is expressed as a system of coupled ordinary stochastic differential equations. The variables are the cell index $n(t)$, which closely reflects the cell population, the concentration $C_o(t)$ of internal toxicants per cell, and the concentration $C_e(t)$ of toxicants outside the cells at time $t$. There are a few papers that consider similar stochastic models for population dynamics, but they mainly study conditions for extinction and persistence. Here we focus on the ergodic properties when the population is persistent.

    We first prove the positivity of the solutions. Then we investigate the influence of noise on the cell population survival. When the noise variances $\sigma_1^2$ or $\sigma_2^2$ are sufficiently large, the population goes to extinction. Numerical simulations show that, for the stochastic model, the population goes to extinction at threshold values $C_e^{stoch}(0)$ below the deterministic threshold value $C_e^{det}(0)$. Furthermore, increasing the noise variances $\sigma_1^2$ or $\sigma_2^2$ results in a lower value $C_e^{stoch}(0)$ at which the population becomes extinct.

    Moreover, we prove that when the noise variance $\sigma_1^2$ is sufficiently small and the population is strongly persistent, then the cell index converges weakly to the unique stationary probability distribution. Increasing the noise intensity causes a right skewness of the stationary distribution.

    Here we illustrate our results for the toxicant monastrol. We have also considered other toxicants from the experiments described in [1] classified in various clusters [30]. We have noticed that the cluster type does not change the type of stationary distribution, nor has an effect on the behavior of the distributions in response to increased noise variances.


    Appendix A. Proof of Lemma 2.2

    Proof. The proof is similar with the proof of Lemma 3.1 in [1]. We define the stopping time $\tau = \inf \{t\ge 0: C_e(t)> C_e(0)\}$. We show that $\tau = \infty$ a.s.. Assume that there exists $T>0$, and $\epsilon>0$ such that $P(\tau\le T)>\epsilon$ and let $\Omega$ be the set where the solution $(n(t), C_o(t), C_e(t))'$ of the system (5)-(7) is continuous. Hence $P(\Omega) = 1$ ([3]), and $P(\Omega_1)>0$, where $\Omega_1 = \Omega\cap \{\tau\le T\}$.

    From (8) with $C_o(0) = 0$ we get for any $\omega\in \Omega_1$ and any $0<t<\tau(\omega)$

    $ 0Co(t,ω)=λ21eη22tt0Ce(s,ω)eη21sdsλ21eη22tCe(0)t0eη21sds=λ21Ce(0)η21(1eη21t)λ21Ce(0)η21
    $

    Moreover, on $\Omega_1$ we have $C_e(\tau) = C_e(0)$, and then from equation (7) we obtain

    $ dCedt|t=τ=λ22Co(τ)n(τ)η22Ce(τ)n(τ)Ce(0)n(τ)(λ21λ22η21η22)<0
    $

    Thus we have a contradiction with the definition of $\tau$.


    Appendix B. Proof of Theorem 2.3

    Proof. The proof is similar with the proof of Theorem 3.2 in [1]. Let $\Omega$ be the set where the solution $(n(t), C_o(t), C_e(t))'$ of the system (5)-(7) is continuous and $n(t)>0$, $0<C_e(t)\le C_e(0)$, $0\le C_o(t)\le\lambda_1^2C_e(0)/\eta_1^2$ for any $t\ge 0$. From Theorem 2.1 and Lemma 2.2 we know that $P(\Omega) = 1$. Let $\Omega_1 = \{\omega\in \Omega: |n|_1(\omega)<\infty\} $ and $\Omega_2 = \{\omega\in \Omega: |n|_1(\omega) = \infty\} $, where $|n|_1(\omega) = \int_0^\infty n(t, \omega)dt$.

    If $P(\Omega_1)>0$, then for any $\omega\in\Omega_1$ and any $t\ge 0$, we have

    $ \int_0^t C_o(s, \omega) n(s, \omega)\exp{\left(\eta_2 ^2\int_0^s n(l, \omega)dl\right )}ds\le \frac{\lambda_1^2 C_e(0)}{\eta_1^2}\exp{\left(\eta_2 ^2|n|_1(\omega)\right )}|n|_1(\omega). $

    Thus $0\le M(\omega): = \int_0^\infty C_o(s, \omega) n(s, \omega)\exp{\left(\eta_2 ^2\int_0^s n(l, \omega)dl\right )}ds<\infty$, and from (9) we get

    $ \lim\limits_{t\rightarrow\infty} C_e(t, \omega) = C_e(0)\exp(-\eta_2^2|n|_1(\omega))+\lambda_2^2 M(\omega)\exp(-\eta_2^2|n|_1(\omega))<\infty. $

    Consequently, there exists $T_1(\omega)>0$ such that for any $t>T_1(\omega)$ we have $C_e(t, \omega)>C_e(0)\exp(-\eta_2^2|n|_1(\omega))/2$. This implies that $\int_0^\infty C_e(s, \omega) e^{\eta_1^2s}ds = \infty$ because for any $t> T_1(\omega)$ we have

    $ t0Ce(s,ω)eη21sdstT1(ω)Ce(s,ω)eη21sdsCe(0)exp(η22|n|1(ω))/2tT1(ω)eη21sds.
    $

    So we can apply L'Hospital's rule in (8), and we get

    $ \lim\limits_{t\rightarrow\infty} C_o(t, \omega) = \frac{\lambda_1^2}{\eta_1^2}\lim\limits_{t\rightarrow\infty} C_e(t, \omega)>0. $

    Thus, on $\Omega_1$, $\lim_{t\rightarrow\infty} C_e(t)$ and $\lim_{t\rightarrow\infty} C_o(t)$ exist and they are related by the previous equation.

    Next, if $P(\Omega_2)>0$ we consider any $\omega\in \Omega_2$. If $0\le\int_0^\infty C_o(s, \omega) n(s, \omega)$ $\exp \bigl(\eta_2 ^2$ $\int_0^s n(l, \omega)dl\bigl)ds<\infty$, from (9) we get $\lim_{t\rightarrow\infty} C_e(t, \omega) = 0$. On the other hand, if $\int_0^\infty C_o(s, \omega) n(s, \omega)\exp{\left(\eta_2 ^2\int_0^s n(l, \omega)dl\right )}ds = \infty$, from L'Hospital's rule in (9) we have

    $ 0\le \frac{\lambda_2^2}{\eta_2^2}\liminf\limits_{t\rightarrow\infty}C_o(t, \omega)\le \liminf\limits_{t\rightarrow\infty}C_e(t, \omega)\le \limsup\limits_{t\rightarrow\infty}C_e(t, \omega)\le \frac{\lambda_2^2}{\eta_2^2}\limsup\limits_{t\rightarrow\infty}C_o(t, \omega) $

    Similarly, from (8) we either get that $\lim_{t\rightarrow\infty} C_o(t, \omega) = 0$ (if $\int_0^\infty C_e(s, \omega) e^{\eta_1^2s}ds<\infty$), or we have

    $ 0\le \frac{\lambda_1^2}{\eta_1^2}\liminf\limits_{t\rightarrow\infty}C_e(t, \omega)\le \liminf\limits_{t\rightarrow\infty}C_o(t, \omega)\le \limsup\limits_{t\rightarrow\infty}C_o(t, \omega)\le \frac{\lambda_1^2}{\eta_1^2}\limsup\limits_{t\rightarrow\infty}C_e(t, \omega), $

    (if $\int_0^\infty C_e(s, \omega) e^{\eta_1^2s}ds = \infty$). All these possible cases give

    $ \lim\limits_{t\rightarrow\infty}C_o(t, \omega) = \lim\limits_{t\rightarrow\infty}C_e(t, \omega) = 0, $

    because $\eta_1^2\eta_2^2-\lambda_1^2\lambda_2^2>0$. Thus, on $\Omega_2$, $\lim_{t\rightarrow\infty} C_e(t)$ and $\lim_{t\rightarrow\infty} C_o(t)$ exist and they are equal with zero.

    In conclusion, on $\Omega = \Omega_1\cup \Omega_2$ we have shown that $\lim_{t\rightarrow\infty} C_e(t)$ and $\lim_{t\rightarrow\infty} C_o(t)$ exist, and we have $\lim_{t\rightarrow\infty} C_o(t) = \frac{\lambda_1^2}{\eta_1^2}\lim_{t\rightarrow\infty} C_e(t)$.


    Appendix C. Proof of Lemma 4.6

    Proof. We choose any $0<c<\beta-\sigma_1^2$. Using Itô's formula in (21) we get:

    $ d(ectY(t))=ect(Y(t)(c+σ21β)+γ+σ22X(t))dtσ1ectY(t)dB1(t)+σ2ectdB2(t)ect(γ+σ22X(t))dtσ1ectY(t)dB1(t)+σ2ectdB2(t)
    $
    (29)

    Let $\tau_m = \inf\{t\ge 0: Y(t)\notin(1/m, m)\}$, for any $m>m_0$, where $m_0>0$ is sufficiently large such that $n(0)\in(1/m_0, m_0)$. Obviously $\lim_{m\rightarrow\infty}\tau_m = \infty$ a.s.. Taking expectation in (29) and using Lemma 4.4 we get:

    $ E[ec(tτm)Y(tτm)]1n(0)+E[tτm0ecs(γ+σ22X(s))ds]1n(0)+(γ+σ22C1)(ect1)c.
    $

    Letting $m\rightarrow \infty$ we get

    $ E\left[Y(t)\right]\le \frac{1}{n(0)e^{ct} }+\frac{\left(\gamma+\sigma_2^2 C_1\right)}{c}(1-e^{-ct}). $

    Thus, there exists a constant $C_2>0$ such that $\sup_{t\ge 0}E[Y(t)]\le C_2$. The proof that $\sup_{t\ge 0} E[Y_\epsilon(t)]< \infty$, for any $0<\epsilon<\frac{\beta-\sigma_1^2}{\alpha}$, is similar.



    Abbreviation Hsp60: heat shock protein 60; MoonProt: the Moonlighting Proteins Database; : ; GAPDH: glyceraldehyde 3-phosphate dehydrogenase; ECM: extracellular matrix;
    Acknowledgments



    Research on this project in the Jeffery lab is supported by an award from the University of Illinois Cancer Center.

    Conflicts of interest



    All authors declare no conflicts of interest in this paper

    [1] Jeffery CJ (1999) Moonlighting proteins. Trends Biochem Sci 24: 8–11. doi: 10.1016/S0968-0004(98)01335-8
    [2] Chen C, Zabad S, Liu H, et al. (2018) MoonProt 2.0: an expansion and update of the moonlighting proteins database. Nucleic Acids Research 46: D640–D644.
    [3] Kainulainen V, Korhonen TK (2014) Dancing to another tune-adhesive moonlighting proteins in bacteria. Biology 3: 178–204. doi: 10.3390/biology3010178
    [4] Jeffery CJ (2018) Intracellular proteins moonlighting as bacterial adhesion factors. AIMS Microbiol 4: 362–376.
    [5] Hennequin C, Porcheray F, Waligora-Dupriet A, et al. (2001) GroEL (Hsp60) of Clostridium difficile is involved in cell adherence. Microbiology 147: 87–96. doi: 10.1099/00221287-147-1-87
    [6] Yamaguchi H, Osaki T, Kurihara N, et al. (1997) Heat-shock protein 60 homologue of Helicobacter pylori is associated with adhesion of H. pylori to human gastric epithelial cells. J Med Microbiol 46: 825–831.
    [7] Ensgraber M, Loos M (1992) A 66-kilodalton heat shock protein of Salmonella typhimurium is responsible for binding of the bacterium to intestinal mucus. Infect Immun 60: 3072–3078.
    [8] Hoffman PS, Garduno RA (1999) Surface-Associated heat shock proteins of Legionella pneumophila and Helicobacter pylori: Roles in pathogenesis and immunity. Infect Dis Obstet Gynecol 7: 58–63.
    [9] Wampler JL, Kim KP, Jaradat Z, et al. (2004) Heat shock protein 60 acts as a receptor for the Listeria adhesion protein in Caco-2 cells. Infect Immun 72: 931–936. doi: 10.1128/IAI.72.2.931-936.2004
    [10] Jagadeesan B, Koo OK, Kim KP, et al. (2010) LAP, an alcohol acetaldehyde dehydrogenase enzyme in Listeria, promotes bacterial adhesion to enterocyte-like Caco-2 cells only in pathogenic species. Microbiology 156: 2782–2795. doi: 10.1099/mic.0.036509-0
    [11] Milohanic E, Pron B, Berche P, et al. (2000) Identification of new loci involved in adhesion of Listeria monocytogenes to eukaryotic cells. Microbiology 146: 731–739. doi: 10.1099/00221287-146-3-731
    [12] Castaldo C, Vastano V, Siciliano RA, et al. (2009) Surface displaced alfa-enolase of Lactobacillus plantarum is a fibronectin binding protein. Microb Cell Fact 8: 14. doi: 10.1186/1475-2859-8-14
    [13] Antikainen J, Kuparinen V, Lähteenmäki K, et al. (2007) pH-dependent association of enolase and glyceraldehyde-3-phosphate dehydrogenase of Lactobacillus crispatus with the cell wall and lipoteichoic acids. J Bacteriol 189: 4539–4543. doi: 10.1128/JB.00378-07
    [14] Patel DK, Shah KR, Pappachan A, et al. (2016) Cloning, expression and characterization of a mucin-binding GAPDH from Lactobacillus acidophilus. Int J Biol Macromol 91: 338–346. doi: 10.1016/j.ijbiomac.2016.04.041
    [15] Kainulainen V, Loimaranta V, Pekkala A, et al. (2012) Glutamine synthetase and glucose-6-phosphate isomerase are adhesive moonlighting proteins of Lactobacillus crispatus released by epithelial cathelicidin LL-37. J Bacteriol 194: 2509–2519. doi: 10.1128/JB.06704-11
    [16] Bergonzelli GE, Granato D, Pridmore RD, et al. (2006) GroEL of Lactobacillus johnsonii La1 (NCC 533) is cell surface associated: potential role in interactions with the host and the gastric pathogen Helicobacter pylori. Infect Immun 74: 425–434. doi: 10.1128/IAI.74.1.425-434.2006
    [17] Granato D, Bergonzelli GE, Pridmore RD, et al. (2004) Cell surface-associated elongation factor Tu mediates the attachment of Lactobacillus johnsonii NCC533 (La1) to human intestinal cells and mucins. Infect Immun 72:2160–2169. doi: 10.1128/IAI.72.4.2160-2169.2004
    [18] Kinoshita H, Uchida H, Kawai Y, et al. (2008) Cell surface Lactobacillus plantarum LA 318 glyceraldehyde-3-phosphate dehydrogenase (GAPDH) adheres to human colonic mucin. J Appl Microbiol 104: 1667–1674. doi: 10.1111/j.1365-2672.2007.03679.x
    [19] Candela M, Biagi E, Centanni M, et al. (2009) Bifidobacterial enolase, a cell surface receptor for human plasminogen involved in the interaction with the host. Microbiology 155: 3294–3303. doi: 10.1099/mic.0.028795-0
    [20] Candela M, Bergmann S, Vici M, et al. (2007) Binding of human plasminogen to Bifidobacterium. J Bacteriol 189: 5929–5936. doi: 10.1128/JB.00159-07
    [21] Crowe JD, Sievwright IK, Auld GC, et al. (2003) Candida albicans binds human plasminogen: identification of eight plasminogen-binding proteins. Mol Microbiol 47: 1637–1651. doi: 10.1046/j.1365-2958.2003.03390.x
    [22] Gozalbo D, Gil-Navarro I, Azorin I, et al. (1998) The cell wall-associated glyceraldehyde-3-phosphate dehydrogenase of Candida albicans is also a fibronectin and laminin binding protein. Infect Immun 66: 2052–2059.
    [23] Jong AY, Chen SH, Stins MF, et al. (2003) Binding of Candida albicans enolase to plasmin(ogen) results in enhanced invasion of human brain microvascular endothelial cells. J Med Microbiol 52: 615–622. doi: 10.1099/jmm.0.05060-0
    [24] Luo S, Hoffmann R, Skerka C, et al. (2013) Glycerol-3-phosphate dehydrogenase 2 is a novel factor H-, factor H-like protein 1-,and plasminogen binding surface protein of Candida albicans. J Infect Dis 207: 594–603. doi: 10.1093/infdis/jis718
    [25] Lesiak-Markowicz I, Vogl G, Schwarzmuller T, et al. (2011) Candida albicans Hgt1p, a multifunctional evasion molecule: complement inhibitor, CR3 analogue,and human immunodeficiency virus-binding molecule. J Infect Dis 204: 802–809. doi: 10.1093/infdis/jir455
    [26] Yang W, Li E, Kairong T, et al. (1994) Entamoeba histolytica has an alcohol dehydrogenase homologous to themultifunctional adhE gene product of Escherichia coli. Mol Biochem Parasitol 64: 253–60. doi: 10.1016/0166-6851(93)00020-A
    [27] Jin S, Song YC, Emili A, et al. (2003) JlpA of Campylobacter jejuni interacts with surface-exposed heat shock protein 90-alpha and triggers signaling pathways leading to the activation of NF-kappaB and p38 MAP kinase in epithelial cells. Cell Microbiol 5: 165–74. doi: 10.1046/j.1462-5822.2003.00265.x
    [28] Raymond BB, Djordjevic S (2015) Exploitation of plasmin(ogen) by bacterial pathogens of veterinary significance. Vet Microbiol 178: 1–13. doi: 10.1016/j.vetmic.2015.04.008
    [29] Collen D, Verstraete M (1975) Molecular biology of human plasminogen II Metabolism in physiological and some pathological conditions in man. Thromb Diath Haemorrh 34: 403–408.
    [30] Dano K, Andreasen PA, Grondahl-Hansen J, et al. (1985) Plasminogen activators, tissue degradation, and cancer. Adv Cancer Res 44: 139–266. doi: 10.1016/S0065-230X(08)60028-7
    [31] Wang W, Jeffery CJ (2016) An analysis of surface proteomics results reveals novel candidates for intracellular/surface moonlighting proteins in bacteria. Mol Biosyst 12: 1420–1431. doi: 10.1039/C5MB00550G
    [32] Han MJ, Lee SY, Hong SH (2012) Comparative analysis of envelope proteomes in Escherichia coli B and K-12 strains. J Microbiol Biotechnol 22: 470–478. doi: 10.4014/jmb.1110.10080
    [33] Bøhle LA, Riaz T, Egge-Jacobsen W, et al. (2011) Identification of surface proteins in Enterococcus faecalis V583. BMC Genomics 12: 135. doi: 10.1186/1471-2164-12-135
    [34] Garcia-del Portillo F, Calvo E, D'Orazio V, et al. (2011) Association of ActA to peptidoglycan revealed by cell wall prteomics of intracellular Listeria monocytogenes. J Biol Chem 286: 34675–34689. doi: 10.1074/jbc.M111.230441
    [35] Amblee V, Jeffery CJ (2015) Physical features of intracellular proteins that moonlight on the cell surface. PLoS One 10: e0130575. doi: 10.1371/journal.pone.0130575
    [36] Matsuoka K, Kanai T (2015) The gut microbiota and inflammatory bowel disease. Semin Immunopathol 37: 47–55. doi: 10.1007/s00281-014-0454-4
    [37] Dahlhamer JM, Zammitti EP, Ward BW, et al. (2016) Prevalence of inflammatory bowel disease among adults aged ≥18 years-United States. MMWR 65:1166–1169.
    [38] Kinoshita H, Ohuchi S, Arakawa K, et al. (2016) Isolation of lactic acid bacteria bound to the porcine intestinal mucosa and an analysis of their moonlighting adhesins. Biosci Microbiota Food Health 35:185–196. doi: 10.12938/bmfh.16-012
    [39] Celebioglu HU, Olesen SV, Prehn K, et al. (2017) Mucin-and carbohydrate-stimulated adhesion and subproteome changes of the probiotic bacterium Lactobacillus acidophilus NCFM. J Proteomics. 163: 102–110. doi: 10.1016/j.jprot.2017.05.015
    [40] Zhu D, Sun Y, Liu F, et al. (2016) Identification of surface-associated proteins of Bifidobacterium animalis ssp. Lactis KLDS 2.0603 by enzymatic shaving. J Dairy Sci. 99: 5155–5172.
    [41] Celebioglu HU, Svensson B (2017) Exo-and surface proteomes of the probiotic bacterium Lactobacillus acidophilus NCFM. Proteomics. 17: 11.
    [42] Celebioglu HU, Delsoglio M, Brix S, et al. (2018) Plant polyphenols stimulate adhesion to intestinal mucosa and induce proteome changes in the probiotic Lactobacillus acidophilus NCFM. Mol Nutr Food Res. 62: 1700638. doi: 10.1002/mnfr.201700638
    [43] Celebioglu HU, Ejby M, Majumder A, et al. (2016) Differential proteome and cellular adhesion analyses of the probiotic bacterium Lactobacillus acidophilus NCFM grown on raffinose - an emerging prebiotic. Proteomics. 16: 1361–1375. doi: 10.1002/pmic.201500212
    [44] Pérez Montoro B, Benomar N, Caballero Gómez N, et al. (2018) Proteomic analysis of Lactobacillus pentosus for the identification of potential markers of adhesion and other probiotic features. Food Res Int. 111: 58–66. doi: 10.1016/j.foodres.2018.04.072
  • This article has been cited by:

    1. Chaoqun Xu, Sanling Yuan, Richards Growth Model Driven by Multiplicative and Additive Colored Noises: Steady-State Analysis, 2020, 19, 0219-4775, 2050032, 10.1142/S0219477520500327
    2. Tiantian Ma, Dan Richard, Yongqing Betty Yang, Adam B Kashlak, Cristina Anton, Functional non-parametric mixed effects models for cytotoxicity assessment and clustering, 2023, 13, 2045-2322, 10.1038/s41598-023-31011-1
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6375) PDF downloads(1708) Cited by(31)

Article outline

Figures and Tables

Figures(1)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog