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Proteomic and transcriptomic analyses of Cutibacterium acnes biofilms and planktonic cultures in presence of epinephrine

  • Transcriptomic and proteomic analysis were performed on 72 h biofilms of the acneic strain Cutibacterium acnes and planktonic cultures in the presence of epinephrine. Epinephrine predominantly downregulated genes associated with various transporter proteins. No correlation was found between proteomic and transcriptomic profiles. In control samples, the expression of 51 proteins differed between planktonic cultures and biofilms. Addition of 5 nM epinephrine reduced this number, and in the presence of 5 µM epinephrine, the difference in proteomic profiles between planktonic cultures and biofilms disappeared. According to the proteomic profiling, epinephrine itself was more effective in the case of C. acnes biofilms and potentially affected the tricarboxylic acid cycle (as well as alpha-ketoglutarate decarboxylase Kgd), biotin synthesis, cell division, and transport of different compounds in C. acnes cells. These findings are consistent with recent research on Micrococcus luteus, suggesting that the effects of epinephrine on actinobacteria may be universal.

    Citation: AV Gannesen, MI Schelkunov, RH Ziganshin, MA Ovcharova, MV Sukhacheva, NE Makarova, SV Mart'yanov, NA Loginova, AM Mosolova, EV Diuvenji, ED Nevolina, VK Plakunov. Proteomic and transcriptomic analyses of Cutibacterium acnes biofilms and planktonic cultures in presence of epinephrine[J]. AIMS Microbiology, 2024, 10(2): 363-390. doi: 10.3934/microbiol.2024019

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  • Transcriptomic and proteomic analysis were performed on 72 h biofilms of the acneic strain Cutibacterium acnes and planktonic cultures in the presence of epinephrine. Epinephrine predominantly downregulated genes associated with various transporter proteins. No correlation was found between proteomic and transcriptomic profiles. In control samples, the expression of 51 proteins differed between planktonic cultures and biofilms. Addition of 5 nM epinephrine reduced this number, and in the presence of 5 µM epinephrine, the difference in proteomic profiles between planktonic cultures and biofilms disappeared. According to the proteomic profiling, epinephrine itself was more effective in the case of C. acnes biofilms and potentially affected the tricarboxylic acid cycle (as well as alpha-ketoglutarate decarboxylase Kgd), biotin synthesis, cell division, and transport of different compounds in C. acnes cells. These findings are consistent with recent research on Micrococcus luteus, suggesting that the effects of epinephrine on actinobacteria may be universal.



    In recent years, there has been growing interest in the study of virus dynamics model with delays [3,4,7,13,16,17,19,28,29]. And the following ordinary differential equations are usually formulated a general virus infection dynamical model with constant delays [7]

    {du1(t)dt=abu1(t)f(u1(t),u3(t))du2(t)dt=rf(u1(th1),u3(th1))pg(u2(t))du3(t)dt=kg(u2(th2))qu3(t), (1)

    where u1(t), u2(t),  and u3(t) represent the population of target cells, infected cells, and free viruses, respectively the positive b, p, and q are the respective death rates. The parameters a, r, and k are the rates at which new target cells, infected cells, and free viruses are generated, respectively. The function f(u1(t),u3(t)) represents the rate for the target cells to be infected by the mature viruses. pg(u2(t)) describes the death rate of the infected cells depending on the population of themselves. Once the virus contacts the target cell, such cell may survive the entire latent period h1. The delay h2 represents the time necessary for the newly produced viruses to be infectious.

    To be better understanding the dynamical behaviour of virus infection one introduces spatial coordinate xΩ and allows the unknowns to depend on it. The target cells, infected cells, and free viruses are assumed to follow the Fickian diffusion with the constant diffusion rate d1,d2, and d3. In earlier results where d1=d2=0  and d3>0 (see e.g., for model without delay [20,21] and [4,13] with constant delay), were investigated by many researchers. For the case di>0, i=1,2,3, a very interesting phenomenon was discovered in paper [1]. Although this phenomenon was first discovered in the vaccinia virus, the researchers showed that the similar phenomenon was found in some other kinds of virus. Recently, there have been some interesting research developments on such virus infection dynamics models, see, e.g., for model without delay [9,22,24] and [14,23,25] with constant delay. On the other hand, it is clear that the constancy of the delay is an extra assumption which essentially simplifies the analysis. Whereas, the state-dependent delay is taken to be a bounded function of the total population (target cells, infected cells, and free viruses), which is more appropriate to describe the real-world processes in biological problem. Moreover, introducing such state-dependent delays in modeling real phenomena results frequently from an attempt to better account for the actual behavior of the population. Therefore, different from the existing results [3,4,7,13,19,28,29], the diffusive virus dynamics model [17] with state-dependent delay is worthy of attention. And such topics have not been fully investigated, which remain a challenging issue.

    Motivated by the above observations, we modify the model (1) to (2) that also extends the model in [17]. And we mainly consider the asymptotic stability of the system (2) from a dynamical systems point of view.

    {u1(x,t)t=abu1(x,t)f(u1(x,t),u3(x,t))+d1u1(x,t)u2(x,t)t=rf(u1(x,tτ1(ut)),u3(x,tτ1(ut)))pg(u2(x,t))+d2u2(x,t)u3(x,t)t=kg(u2(x,tτ2(ut)))qu3(x,t)+d3u3(x,t), xΩ, t>0, (2)

    where u1(x,t), u2(x,t),  and u3(x,t) defined as earlier, also represent the population of target cells, infected cells, and free viruses, at position x at time t, respectively. Ω  is a fixed, connected bounded domain in Rn  with smooth boundary  Ω. Denote u=(u1,u2,u3) and the history segment ut(x,θ)u(x,t+θ), θ[h,0], hmax{h1,h2}. τ1(ut) and τ2(ut) where are taken to be some general functions of system populations with 0τj(ut)hj, j=1,2, represent the latent period that the cell survives once the virus contacts the target cell and the time that the newly produced viruses are infectious, respectively. di,i=1,2,3 is the constant diffusion rate.

    The rest of the paper is organized as follows. In section 2, we briefly recall some basic known results which will be used in the sequel. In section 3, we first present the positivity and boundedness results for the model (2). Next we mainly study the asymptotic stability of interior equilibria from a dynamical systems point of view. According to constructing a dynamical system on a nonlinear metric space, we apply [5,Theorem 4.1.4] and choose a novel Lyapunov functional to the model (2) and allow, but not require, diffusion terms in each state equation. In section 4, we generalize the method to such model with Logistic growth rate.

    In this section, we present some definitions, lemmas, and notations, which will be useful throughout this paper.

    Denote the linear operator A0=diag(d1, d2, d3) in C(ˉΩ,R3) with D(A0)D(d1)×D(d2)×D(d3). Let D(di){yC2(ˉΩ,R):yn|Ω=0} for di0, i=1,2,3. Omit the space coordinate x, we denote the unknown u(t)=(u1(t),u2(t),u3(t))X[C(ˉΩ,R)]3C(ˉΩ,R3). It is well-known that the closure A (in X) of the operator A0 generates a C0 semigroup eAt on X which is analytic and nonexpansive [12,p.4-p.5]. Further, A is the infinitesimal generator of the analytic compact semigroup eAt on X (see [27,Theorem 1.2.2] for more details). We denote the space of continuous functions by CC([h,0],X) equipped with the sup-norm φCsupθ[h,0]φ(θ)X.

    Now the system (2) is rewritten in the following abstract form

    ddtu(t)+Au(t)=F(ut), (3)

    where the nonlinear continuous mapping F:CX is defined by

    F(ϕ)(x)[abϕ1(x,0)f(ϕ1(x,0),ϕ3(x,0))rf(ϕ1(x,τ1(ϕ)),ϕ3(x,τ1(ϕ)))pg(ϕ2(x,0))kg(ϕ2(x,τ2(ϕ)))qϕ3(x,0)] (4)

    with ϕ(ϕ1,ϕ2,ϕ3)C.

    Set the initial value ψ=(ψ1,ψ2,ψ3)Lip([h,0],X) for the equation (3), where

    ψLip([h,0],X){ψC:supstψ(s)ψ(t)X|st|<, ψ(0)D(A)}. (5)

    In our study we use the standard (c.f. ([15,Def. 2.3,p.106]) and ([15,Def. 2.1,p.105])).

    Definition 2.1. A function u(t)C([h,tf), X) is a mild solution of initial value problem (3)(5) if u0=ϕ  and

    u(t)=eAtϕ(0)+t0eA(ts)F(us)ds, t[0,tf). (6)

    Definition 2.2. A function u(t)C([h,tf), X)C1([0,tf), X) is a classical solution of initial value problem (3)(5) if u(t)D(A) for t[0,tf) and (3)(5) are satisfied.

    Next we present the existence and uniqueness of solution for the initial value problem (3)(5).

    Throughout the paper, we assume that functions f:R2R and g:RR satisfy the following conditions:

    (H1) f and g are Lipschitz continuous.

    (H2) |f(u1,u3)|μu1  for u1, u30, and |g(u2)|λu2 for u20.

    We follow the streamline of the proof Lemma 1.2 in [10], (see also [8,Theorem 2]) established by [15,Theorem 3.1 and Corollary 3.3]. Moreover, the linear growth bounds of f  and g imply the global continuation of the classical solution. Then we obtain the result as follows.

    Lemma 2.3. Let the state-dependent delays τj:C[0,hj], (j=1,2) be locally Lipschitz. Suppose that (H1) and (H2) hold. Then initial value problem (3)(5) has a unique global classical solution for t0.

    The following lemma provides a theoretical foundation for the stability of the equilibrium in a dynamical system.

    Lemma 2.4. ([5,Theorem 4.1.4]) Let {S(t), t0} be a dynamical system on Z. Set 0 be an equilibrium point in Z. Assume V is a Lyapunov function on Z satisfying V(0)=0, V(y)α(y) for yZ, y=dist{y,0}, where α() is a continuous strictly increasing function, α(0)=0 and α(r)>0 for r>0. Then 0 is stable. Suppose in addition that V(y)β(y), where β() is continuous, increasing and positive, with β(0)=0. Then 0 is uniformly asymptotically stable.

    In the sequel, we mainly discuss the asymptotic stability of interior equilibria of system (2) on a metric space

    Xf={ϕC1([h,0],X):ϕ(0)D(A), ˙ϕ(0)+Aϕ(0)=F(ϕ)}

    endowed with

    ϕXf=maxθ[τ,0]ϕ(θ)X+maxθ[τ,0]˙ϕ(θ)X+Aϕ(0)X.

    We know that Xf is a complete metric space from [8,10].

    The following proposition offers some properties of a function, which will play an important role in construction of Lyapunov functionals.

    Proposition 2.5. A function v:[0, +)R is defined by v(s)s1ln(s), which is of the following properties.

    (i) v(s)0 for all s[0, +).

    (ii) ˙v(s)=11s, ˙v(s)<0 for s(0, 1) and ˙v(s)>0 for s(1, ).

    (iii) v(s)=0 if and only if s=1.

    (iv) (s1)22(1+ϵ)v(s)(s1)22(1ϵ), ϵ(0, 1) and s(1ϵ, 1+ϵ). It is checked that

    |dds(s1)22(1+ϵ)||˙v(s)||dds(s1)22(1ϵ)|,  ϵ(0,1)and s(1ϵ, 1+ϵ).

    In the subsection, we prove the positivity and boundedness of solutions for system (3).

    To get the results, we need further assumptions:

    (H3) g is increasing, g(0)=0 and g(u2)0 for u20.

    (H4) f(u1,u3)>0 for u1, u3>0 and f(u1,0)=f(0,u3)=0  for u1, u30.

    Set 0=(0,0,0)T  and M=(M1,M2,M3)T=(ab, g1(rμapb), rkμaqpb)T. Denote

    [0,M]X{ϕ=(ϕ1,ϕ2,ϕ3)X:0ϕi(x)Mi, xˉΩ},[0,M]C{ϕ=(ϕ1,ϕ2,ϕ3)Lip([h,0],X):ϕ(θ)[0,M]X, θ[h,0]}.

    Lemma 3.1. Let τj in Lemma 2.3 be valid. Assume that (H1)-(H4) are satisfied. Then [0,M]C is invariant i.e., for each initial value ϕ=(ϕ1,ϕ2,ϕ3)[0,M]C, the unique classical solution of initial value problem (3)(5) satisfies ut[0,M]C for all t0.

    Proof. The existence and uniqueness of solution is proven as above Lemma 2.3. Let K=[0,M]X, S(t,s)=eA(ts), B(t,ϕ)=F(ϕ). The proof of the invariance part follows the invariance result of ([12,Corollary 4] or [27,Corollary 8.1.3]) with the almost Lipschitz property of F by the nomenclature of [11]. Next we check the estimates for the subtangential condition. For any ϕ[0,M]C and any ϱ0, we obtain

    ϕ(x,0)+ρF(ϕ)(x)=[ϕ1(x,0)+ρaρbϕ1(x,0)ρf(ϕ1(x,0),ϕ3(x,0))ϕ2(x,0)+ρrf(ϕ1(x,τ1(ϕ)),ϕ3(x,τ1(ϕ)))ρpg(ϕ2(x,0))ϕ3(x,0)+ρkg(ϕ2(x,τ2(ϕ)))ρqϕ3(x,0)].

    Note that (H2). Thus, for any 0ρmin{1b+μ,1pλ,1q}, we have

    ϕ(x,0)+ρF(ϕ)(x)[ϕ1(x,0)ρbϕ1(x,0)ρμϕ1(x,0)ϕ2(x,0)ρpλϕ2(x,0)ϕ3(x,0)ρqϕ3(x,0)]=[[1ρ(b+μ)]ϕ1(x,0)(1ρpλ)ϕ2(x,0)(1ρq)ϕ3(x,0)][000]

    and

    ϕ(x,0)+ρF(ϕ)(x)[ϕ1(x,0)+ρaρbϕ1(x,0)ϕ2(x,0)+ρrμϕ1(x,τ1(ϕ))ρpg(ϕ2(x,0))ϕ3(x,0)+ρkg(ϕ2(x,τ2(ϕ)))ρqϕ3(x,0)][ab+ρaρbabg1(rμapb)+ρrμabρpg(g1(rμapb))rkμaqpb+ρkg(g1(rμapb))ρqrkμaqpb]=[abg1(rμapb)rkμaqpb]=M.

    Then we obtain ϕ(x,0)+ρF(ϕ)(x)[0,M]X. This implies that

    limρ0+1ρdist(ϕ(x,0)+ρF(ϕ)(x),[0,M]X)=0, ϕ[0,M]C.

    We complete the proof.

    Remark 3.2 It should be pointed out that K is a subset of X. However, such fact is often ignored in some works [13,17,29], where K had been chosen a subset of C.

    Let us discuss stationary solutions of (2). By such solutions we mean time independent u which, in general, may depend on x¯Ω. Since stationary solutions of (2) do not depend on the type of delay (state-dependent or constant) we have

    {0=abu1f(u1,u3)0=rf(u1,u3)pg(u2)0=kg(u2)qu3. (7)

    It is easy to see that the trivial stationary solution (ab, 0, 0) always exists if f(u1,0)=0 and g(0)=0. We are interested in nontrivial stationary solutions of (2). Based on (7), we have u2=g1((abu1)rp) and u3=k(abu1)rpq. This gives the condition on the coordinate u1 which should belong to (0, ab]. Denote

    Ef(z)f(z,k(abz)rpq)a+bz. (8)

    Incidentally, we assume that

    (Hf) Ef(z)=0 has at most finite roots on (0, ab].

    Remark 3.3. (i) Since Ω is a connected set, a function wC(ˉΩ) may take either one or continuum values. The assumption (Hf) implies u1(x)=u1, then (u1,u2,u3) is independent of x. Moreover, if (H4) holds, then we know exactly one root of Ef(s)=0, such as the DeAngelis-Bendington functional response [2,6]

    f(u1,u3)=k1u1u31+k2u1+k3u3, k1, k20 and k3>0, (9)

    the saturated functional response [3,26]

    f(u1,u3)=k1u1u31+k2u3, k10 and k2>0, (10)

    the Crowley-Martin functional response [19,30]

    f(u1,u3)=k1u1u3(1+k2u1)(1+k3u3), k10 and k2, k3>0. (11)

    And for more general class of f, under additional conditions, one has exactly one root of Ef(s)=0.

    (ii) It should be pointed out that in study of stability properties of stationary solutions for virus infection model one usually uses conditions on the so-called reproduction numbers. Then one can use such conditions to separate the case of a unique stationary solution. In this work, taking into account the state-dependence of the delay, the conditions on the reproduction numbers do not appear explicitly here, but could be seen as particular sufficient conditions for (Hf). As a consequence, such models admit of multiple equilibria. Then we believe this framework provides a way to model more complicated situations with rich dynamics.

    (iii) Based on (H2) and (Hf), we have

    abu1=f(u1,u3)μu1, for u1(0, ab].

    And then we know that the interior equilibrium (u1,u2,u3) belongs to the invariant set [0,M]C by (Hf) and the monotone property of g.

    In this subsection, we discuss the stability of the interior equilibrium (u1,u2,u3) from a dynamical systems point of view. Next we work out the stability of (3)(5) with smooth initial value belonging to Xf.

    In the following, we assume that

    (H5) f is increasing for u1, u3>0 and differentiable in a neighborhood of (u1,u3),

    (H6) (u3u3f(u1,u3)f(u1,u3))(f(u1,u3)f(u1,u3)1)>0 for u1, u3>0. The assumption (H6) implies that f(u1,u3)f(u1,u3) lies between u3u3 and 1 (c.f. with the non-strict property [13,p.74] and [17,p.8]). And it is easy to verify that the general class of nonlinear functional responses including (9), (10), and (11) is often appropriate for (H6).

    According to constructing a dynamical system on Xf, we prove the stability of the interior equilibrium of (2) by choosing a novel Lyapunov functional. Then we arrive at the following theorem.

    Theorem 3.4. Let τj, (j=1,2) be locally Lipschitz on C and be continuously differentiable in a neighbourhood of the equilibrium (u1,u2,u3). If (H1)(H6) and (Hf) hold, then the non-trivial steady-state solution (u1,u2,u3) is asymptotically stable (in Xf[0, M]C).

    Remark 3.5. For uC1([h,s),X), we get

    ddtτj(ut)=[(Dτj)(ut)](dutdt),t[0,s),

    where [(Dτj)()], j=1,2, is the Fréchet derivative of τj at point ut. Hence, for a solution in ε neighborhood of the stationary solution ψ, the estimate

    |ddtτj(ut)|(Dτj)(ut)C(C, R)dutdtCε(Dτj)(ut)C(C, R)

    guarantees

    |ddtτj(ut)|Mjε, with Mjε0 as ε0,

    due to Lemma 3.1 and the boundedness of (Dτj)(ψ)C(C, R) as ε0 (here ψψC<ε).

    Proof. Based on D(A)D(A12), we are sufficient to set D(A12)=X in [18]. Thus, according to [18,p.831], we get that (3)(5) is described a dynamical system on Xf. And then we use Lemma 2.4. Define a Lyapunov functional with state-dependent delays along a solution of (2)

    Vsd(t)=ΩVsd_x(x,t)dx,

    where

    Vsd_x(x,t)u1(x,t)u1u1(x,t)u1f(u1,u3)f(s,u3)ds+1r(u2(x,t)u2u2(x,t)u2g(u2)g(s)ds)+prku3v(u3(x,t)u3)+f(u1,u3)ttτ1(ut(x,θ))v(f(u1(x,s),u3(x,s))f(u1,u3))ds+prg(u2)ttτ2(ut(x,θ))v(g(u2(x,s))g(u2))ds, θ[h,0].

    According to Lemma 3.1, it follows that u1, u2,  and u3 are bounded and nonnegative. And then Lyapunov functional is well-define. Next we shall show that dVsd(t)dt is non-positive.

    Let us consider

    ddtVsd(t)=ΩtVsd_x(x,t)dx

    and start with the term tVsd_x(x,t).

    By computations, we have

    tVsd_x(x,t)=RDdiff_x(x,t)+(1f(u1,u3)f(u1(x,t),u3))[abu1(x,t)f(u1(x,t),u3(x,t))]+(1g(u2)g(u2(x,t)))(rf(u1(x,tτ1(ut)),u3(x,tτ1(ut)))pg(u2(x,t)))+(1u3u3(x,t))(kg(u2(x,tτ2(ut)))qu3(x,t))+f(u1,u3)Dsd(x,t)+f(u1,u3)dsd(x,t),

    where

    RDdiff_x(x,t)(1f(u1,u3)f(u1(x,t),u3))d1u1(x,t)+1r(1g(u2)g(u2(x,t)))d2u2(x,t)+prk(1u3u3(x,t))d3u3(x,t)Dsd(x,t)v(f(u1(x,t),u3(x,t))f(u1,u3))v(f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))+v(g(u2(x,t))g(u2))v(g(u2(x,tτ2(ut)))g(u2))dsd(x,t)v(f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))dτ1(ut)dt+v(g(u2(x,tτ2(ut)))g(u2))dτ2(ut)dt.

    From

    {u1=af(u1,u3)bpg(u2)=rf(u1,u3)u3=kg(u2)q,

    we get

    tVsd_x(x,t)=RDdiff_x(x,t)+bu1(1f(u1,u3)f(u1(x,t),u3))(1u1(x,t)u1)+f(u1,u3)Z(x,t)+f(u1,u3)Dsd(x,t)+f(u1,u3)dsd(x,t),

    where

    Z(x,t)(1f(u1,u3)f(u1(x,t),u3))(1f(u1(x,t),u3(x,t))f(u1,u3))+(1g(u2)g(u2(x,t)))(f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3)g(u2(x,t))g(u2))+(1u3u3(x,t))(g(u2(x,tτ2(ut)))g(u2)u3(x,t)u3). (12)

    After a simple computation, (12) is equivalent to

    Z(x,t)=3f(u1(x,t),u3(x,t))f(u1,u3)f(u1,u3)f(u1(x,t),u3)+f(u1,u3)f(u1(x,t),u3)f(u1(x,t),u3(x,t))f(u1,u3)+f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3)g(u2(x,t))g(u2)g(u2)g(u2(x,t))f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3)+g(u2(x,tτ2(ut)))g(u2)u3(x,t)u3u3u3(x,t)g(u2(x,tτ2(ut)))g(u2). (13)

    By the definition of v(s)=s1ln(s), (13) is rewritten as

    Z(x,t)=v(f(u1(x,t),u3(x,t))f(u1,u3))v(f(u1,u3)f(u1(x,t),u3))+v(f(u1(x,t),u3(x,t))f(u1(x,t),u3))+v(f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))v(g(u2)g(u2(x,t))f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))+v(g(u2(x,tτ2(ut)))g(u2))v(u3(x,t)u3)v(g(u2(x,t))g(u2))v(u3u3(x,t)g(u2(x,tτ2(ut)))g(u2)).

    Then we obtain

    tVsd_x(x,t)=bu1(1f(u1,u3)f(u1(x,t),u3))(1u1(x,t)u1)+f(u1,u3){[v(u3(x,t)u3)v(f(u1(x,t),u3(x,t))f(u1(x,t),u3))]v(g(u2)g(u2(x,t))f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))v(u3u3(x,t)g(u2(x,tτ2(ut)))g(u2))v(f(u1,u3)f(u1(x,t),u3))}+RDdiff_x(x,t)+f(u1,u3)dsd(x,t).

    Denote

    RDdiff(t)ΩRDdiff_x(x,t)dx.

    Based on the Divergence Theorem and the Neumann boundary condition, we have

    RDdiff(t)=ΩRDdiff_x(x,t)dx=d1f(u1,u3)Ω1[f(u1(x,t),u3)]2df(u1(x,t),u3)du1u12dxd2rg(u2)Ω1[g(u2(x,t))]2u22dxd3prku3Ω1(u3(x,t))2u32dx.

    According to df(u1,u3)du10, we obtain RDdiff(t)0.

    Thus we summarize what we have worked out as follows

    ddtVsd(t)=ΩtVsd_x(x,t)dx=RDdiff(t)+bu1Ω(1f(u1,u3)f(u1(x,t),u3))(1u1(x,t)u1)dx+f(u1,u3)Ω{[v(u3(x,t)u3)v(f(u1(x,t),u3(x,t))f(u1(x,t),u3))]v(g(u2)g(u2(x,t))f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))v(u3u3(x,t)g(u2(x,tτ2(ut)))g(u2))v(f(u1,u3)f(u1(x,t),u3))}dx+f(u1,u3)Ωdsd(x,t)dx. (14)

    According to (H5), one gets

    Ω(1f(u1,u3)f(u1(x,t),u3))(1u1(x,t)u1)dx0.

    Based on (H6) and the monotonicity of the function v, we have

    Ω[v(u3(x,t)u3)v(f(u1(x,t),u3(x,t))f(u1(x,t),u3))]ds0.

    Now we prove ddtVsd(t)0 in a small neighbourhood of the stationary solution with the equality only in case of (u1,u2,u3)=(u1,u2,u3). In the particular case of constant delay, one has dsd(x,t)=0 which may lead to the global stability of (u1,u2,u3).

    In the following, we rewrite (14) as

    ddtVsd(t)=f(u1,u3)Ω(Qsd(x,t)+dsd(x,t))dx+RDdiff(t)+bu1Ω(1f(u1,u3)f(u1(x,t),u3))(1u1(x,t)u1)dx,

    where

    Qsd(x,t)[v(u3(x,t)u3)v(f(u1(x,t),u3(x,t))f(u1(x,t),u3))]+v(g(u2)g(u2(x,t))f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))+v(u3u3(x,t)g(u2(x,tτ2(ut)))g(u2))+v(f(u1,u3)f(u1(x,t),u3)).

    Let us consider the zero-set dVsd(t)dt. We start with Ω(1f(u1,u3)f(u1,u3))(1u1u1)dx=0 due to u1=u1. Note that v(s)=0 if and only if s=1. For Qsd(x,t)=0, we obtain u2=u2  and u3=u3. Then one sees f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))=f(u1,u3). Furthermore, RDdiff(t)=0 implies that u1, u2,  and u3 are independent of x. The zero set dsd(x,t)=0 includes g(u2(x,tτ2(ut)))=g(u2), f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))=f(u1,u3) or ddtτj(ut)=0 along a solution. Thus the zero-set consists of just the positive equilibrium (u1,u2,u3) which is also a subset of dsd(x,t)=0. We remind that Qsd(x,t)0, while the sign of dsd(x,t) is undefined. We would show that there is a small neighbourhood of (u1,u2,u3) such that |dsd(x,t)|<Qsd(x,t). In order to prove such result, we need the statement (ⅳ) in Proposition 2.5.

    The following discussions are in part analogous to [16,p.1559]. Based on Proposition 2.5 (ⅳ), let us first consider the following auxiliary functions Q(y) and D(y), defined on R6, where we simplify y=(y1,y2,y3,y4,y5,y6)R6 for y1=u1(x,t), y2=u2(x,t), y3=u3(x,t), y4=u1(x,tτ1), y5=u2(x,tτ2), y6=u3(x,tτ1),

    Q(y)(g(u2)g(y2)f(y4,y6)f(u1,u3)1)2+(u3y3g(y5)g(u2)1)2+(f(u1,u3)f(y1,u3)1)2,

    and

    D(y)α1v(g(y5)g(u2))+α2v(f(y4,y6)f(u1,u3)), α1,α20.

    According to Proposition 2.5 (ⅳ) and Remark 3.5, it is observed that Q(y)Qsd(x,t) and |dsd(x,t)|D(y). And it should be pointed out that Q(y)=0 if and only if y=(u1,u2,u3,u1,u2,u3). Now we change the coordinates in R6 to the spherical ones

    {y1=u1+rcosξ1y2=u2+rsinξ1cosξ2y3=u3+rsinξ1sinξ2cosξ3y4=u1+rsinξ1sinξ2sinξ3cosξ4y5=u2+rsinξ1sinξ2sinξ3sinξ4cosξ5y6=u3+rsinξ1sinξ2sinξ3sinξ4sinξ5

    r0, ξj[π2,π2], j=1, 4, ξ5[0,2π).

    One can check that Q(y)=r2Φ(r,ξ1,ξ5), where Φ(r,ξ1,ξ5) is continuous and Φ(r,ξ1,ξ5)0 for r0. It is proved by way of contradiction. If Φ(r,ξ1,ξ5)=0 for r0, then it is easy to see that this contradicts Proposition 2.5 (ⅳ). Hence the classical extreme value theorem shows that the continuous function on a closed neighborhood of (u1,u2,u3) has a minimum Φmin >0. It follows that Q(y)r2Φmin.

    According to Proposition 2.5 (ⅳ), we have

    D(y)α1(g(y5)g(u2)1)2+α2(f(y4,y6)f(u1,u3)1)2.

    Next we adopt the analogous procedure as in the discussion of Q(y). Remind that Remark 3.5. Then we obtain that D(y)βεr2 where the constant βε=max{α1, α2}0 as ε0. Finally, we choose a small enough ε such that βε<Φmin which proves ddtVsd(t)<0. We complete the proof.

    We now apply the above results to consider the following example.

    Example 3.6. Consider the system (2) with

    f(u1,u3)=k1u1u31+k2u1+k3u3, k1, k20and k3>0,g(u2)=k4u2, k4>0,τj(φ)=0hjwj(φ(s))ds, φCandhj>0, (j=1,2)  (15)

    where wj, j=1,2 is locally Lipschitz on X. We know that f and g are continuously differentiable on R2 and R, respectively. Moreover, f is increasing and nonnegative on R2+. And g is increasing on R. Then it is easy to see that (H1) and (H3)-(H5) are satisfied. By choosing μ=max{k1, b} and λ=k4+1, we get that for ui0, i=1,2,3

    |f(u1,u3)|=f(u1,u3)k1u31+k3u3u1k1u1μu1and|g(u2)|=g(u2)=k4u2λu2,

    which implies that (H2) holds. If 0<pq+ak3krbk3kr+k1krk2pqab, we know that the equation

    Ef(z)=f(z,k(abz)rpq)a+bz=(abz)[k1zkr(pq+k2pqz+k3k(abz)r)]pq+k2pqz+k3k(abz)r=0

    has two roots z=ab and z=pq+ak3krbk3kr+k1krk2pq. It follows that (Hf) is satisfied. Then we obtain the non-trivial steady-state solution

    (u1,u2,u3)=(pq+ak3krbk3kr+k1krk2pq, r(ak1krak2pqbpq)k4p(bk3kr+k1krk2pq), kr(ak1krak2pqbpq)pq(bk3kr+k1krk2pq))

    with 0<pq+ak3krbk3kr+k1krk2pqab and ak1krak2pqbpq>0. And we have

    f(u1,u3)f(u1,u3)=1+k2u1+k3u31+k2u1+k3u3u3u3

    lies between u3u3 and 1 for u1, u3>0, which means that (H6) holds. For τj, j=1,2, we get

    ddtτj(ut)=ddt0hjwj(ut(s))ds=ddttthjwj(u(θ))dθ=wj(u(t))wj(u(thj)).

    Thus, in the ε neighborhood of (u1,u2,u3), we have

    |ddtτj(ut)||wj(u(t))wj(u(thj))|2Lwjε=Mjε0 as ε0,

    where Lwj, j=1,2, is Lipschitz constant of wj.

    Based on the above analysis and Lemma 3.1, we have the invariant set

    [0,M]C={ϕ=(ϕ1,ϕ2,ϕ3)Lip([h,0],X):ϕ(θ)[0,M]X, θ[h,0]}

    where

    [0,M]X={ϕ=(ϕ1,ϕ2,ϕ3)X:0ϕi(x)Mi, xˉΩ}

    with M=(M1,M2,M3)T=(ab, rμak4pb, rkμaqpb)T. It is now evident to see that (u1,u2,u3) is asymptotically stable (in Xf[0, M]C) from Theorem 3.4.

    Next we perform numerical simulations of system (15) with the parameters a=0.8, b=0.2, r=0.1, p=0.1, k=0.3, q=0.2, d1=0.2, d2=0.1, d3=0.7, k1=0.3, k2=0.01, k3=0.5, and k4=0.8. By substituting the parameters, it follows that the non-trivial steady-state solution (u1,u2,u3)=(2.712, 0.322, 0.386) exists. According to choosing

    τ1(ut)=0.2033i=1(ui)t(s)dsandτ2(ut)=0.08023i=1(ui)t(s)ds,

    we have that τ1() and τ2() are locally Lipschitz in C and are continuously differentiable in a neighbourhood of the equilibrium (u1, u2, u3). Now one can check that (H1)-(H6) and (Hf) are satisfied. Consequently, based on Theorem 3.4, we infer that (u1,u2,u3)=(2.712, 0.322, 0.386) is asymptotically stable (in Xf[0, M]C) which is illustrated in Figure 1, where

    Xf={ϕC1([3,0],[C([0,π],R)]3):ϕ(0)D(A), ˙ϕ(0)+Aϕ(0)=F(ϕ)},[0,M]C={ϕ=(ϕ1,ϕ2,ϕ3)Lip([3,0],[C([0,π],R)]3):ϕ(θ)[0,M][C([0,π],R)]3, θ[3,0]}
    Figure 1.  Numerical simulations of system (15) with the initial value (ψ1, ψ2, ψ3)=(3cos2x+0.027, 0.5cos2x+0.032, 0.5cos2x+0.038). (a)-(c) The non-trivial steady-state solution (u1, u2, u3)=(2.712, 0.322, 0.386) is asymptotically stable. (d) Phase portrait of the asymptotically stable equilibrium (u1, u2, u3).

    with

    [0,M][C([0,π],R)]3={ϕ=(ϕ1,ϕ2,ϕ3)[C([0,π],R)]3:0ϕ1(x)4,0ϕ2(x)1.5, 0ϕ3(x)1.8,  x[0,π]}.

    In this section, we generalize the above type of Lyapunov functional to such model with Logistic growth rate. Then the model is described as follows:

    {u1(x,t)t=au1(x,t)(1bu1(x,t)R)f(u1(x,t),u3(x,t))+d1u1(x,t)u2(x,t)t=rf(u1(x,tτ1(ut)),u3(x,tτ1(ut)))pg(u2(x,t))+d2u2(x,t)u3(x,t)t=kg(u2(x,tτ2(ut)))qu3(x,t)+d3u3(x,t). (16)

    In the following, these results are completed by the method analogous to that used above.

    Lemma 4.1. Let τj in Lemma 2.3 be valid. Suppose (H1) and (H2) hold. Then initial value problem has a unique global classical solution for t0.

    Denote ˆM(Rb,g1(rμRpb),rkμRqpb)T.

    Lemma 4.2. Let τj in Lemma 2.3 be valid. Assume that (H1)-(H4) are satisfied. Then [0, ˆM]C is invariant i.e., for each initial value ϕ=(ϕ1,ϕ2,ϕ3)[0, ˆM]C, the unique classical solution of initial value problem satisfies ut[0, ˆM]C for all t0.

    Next we are interested in nontrivial stationary solutions of (16). Consider

    {0=au1(1bu1R)f(u1,u3)0=rf(u1,u3)pg(u2)0=kg(u2)qu3. (17)

    Then we have u2=g1(arpu1(1bu1R))  and u3=karpqu1(1bu1R). Set

    ˉEf(z)f(z,karpqz(1bzR))az(1bzR). (18)

    In the sequel the following assumption will be need.

    (ˉHf) ˉEf(z)=0 has at most finite roots on (0, Rb].

    We now obtain the following result.

    Theorem 4.3. Let τj, (j=1,2) be locally Lipschitz on C and be continuously differentiable in a neighbourhood of the equilibrium (u1, u2, u3). If (H1)(H6) and (ˉHf) hold, then the non-trivial steady-state solution (u1, u2, u3) is asymptotically stable (in Xf[0,ˆM]C).

    In the proof we use the following Lyapunov functional with state-dependent delay along a solution of (16). Choose the Lyapunov functional

    Vsd(t)=ΩVsd_x(x,t)dx

    where

    Vsd_x(x,t)u1(x,t)u1u1(x,t)u1f(u1,u3)f(s,u3)ds+1r(u2(x,t)u2u2(x,t)u2g(u2)g(s)ds)+prku3v(u3(x,t)u3)+f(u1,u3)ttτ1(ut(x,θ))v(f(u1(x,s),u3(x,s))f(u1,u3))ds+f(u1,u3)ttτ2(ut(x,θ))v(g(u2(x,s))g(u2))ds, θ[h,0].

    It is easy to verify that

    ddtVsd(t)=ΩtVsd_x(x,t)dx

    where

    tVsd_x(x,t)=(1f(u1,u3)f(u1(x,t),u3))d1u1(x,t)+1r(1g(u2)g(u2(x,t)))d2u2(x,t)+prk(1u3u3(x,t))d3u3(x,t)+(1f(u1,u3)f(u1(x,t),u3))ba(u1)2R(1u21(x,t)(u1)2)+(1f(u1,u3)f(u1(x,t),u3))au1(u1(x,t)u11)+f(u1,u3)[v(f(u1,u3)f(u1(x,t),u3))[v(u3(x,t)u3)v(f(u1(x,t),u3(x,t))f(u1(x,t),u3))]v(u3u3(x,t)g(u2(x,tτ2(ut)))g(u2))v(g(u2)g(u2(x,t))f(u1(x,tτ1(ut)),u3(x,tτ1(ut)))f(u1,u3))]+f(u1,u3)v(g(u2(x,tτ2(ut)))g(u2))dτ2(ut)dt+f(u1,u3)v(g(u2(x,tτ2(ut)))g(u2))dτ2(ut)dt.

    The next works are similar to the proof of Theorem 3.4. We do not repeat here detailed calculations.

    In this paper, we study a virus dynamics model with diffusion, a general nonlinear functional response and state-dependent delays. Such delays τ1(ut) and τ2(ut) which are both related to the number of system populations, represent the latent period that the cell survives once the virus contacts the target cell and the time that the newly produced viruses are infectious, respectively. We mainly establish asymptotic stability of the interior equilibrium by applying a novel Lyapunov functional. Moreover, we generalize such type of Lyapunov functional to such model with Logistic growth rate. More specifically, target cells, infected cells, and free viruses do not extinct and ultimately survive at the equilibrium level (u1,u2,u3) if the following conditions are satisfied:

    (Ⅰ) target cells and free viruses have strong intercellular infection, i.e., |f(u1,u3)|μu1, f(u1,u3)f(u1,u3) lies between u3u3 and 1 for u1, u3>0.

    (Ⅱ) the death rate of the infected cells is of a linear growth bound, i.e., |g(u2)|λu2 for u2>0.

    (Ⅲ) the rate of change with respect to time of the state-dependent delays is limited, i.e., |ddtτj(ut)|Mjε.

    We are extremely grateful to the critical comments and invaluable suggestions made by anonymous honorable reviewers. This research is supported by National Natural Science Foundation of China (No.11771109).

    The authors declared that they have no conflicts of interest.


    Acknowledgments



    Transcriptome sequencing and analysis were performed in the Genomics Core Facility of the Skolkovo Institute of Science and Technology (https://www.skoltech.ru/research/en/gcf-2/ (accessed on March1, 2024). qRT–PCR was performed using the scientific equipment of the Core Research Facility ‘Bioengineering’ (Research Center of Biotechnology RAS). A.V. Gannesen and coauthors sincerely thank V.V. Sorokin and A.V. Mulyukin (Core Facility “UNIQEM collection” of Research Center of Biotechnology RAS) for their kind assistance and for providing liquid nitrogen for this study. The authors are grateful to the staff of the Laboratory of Molecular Ecology and Phylogenomics of Bacteria of the Research Center of Biotechnology RAS (the head Svetlana N. Dedysh) and, personally to Svetlana E. Belova, Igor. Yu. Oshkin, Kirill K. Miroshnikov, and Anastasiya A. Ivanova for their kind assistance and giving access to the FastPrep disintegrator. Authors cordially thank Maya M. Polovitskaya for her kind assistance in the editing of the manuscript.

    Funding



    The work is supported by the RSCF, the grant nº 19-74-10071. The work of V.K. Plakunov is supported by the Ministry of Science and Higher Education of Russian Federation.

    Conflict of interests



    Authors declare no conflict of interests.

    Data availability statement



    The transcriptomic data are deposited at the Sequence Read Archive (SRA) database under the identifiers SRR27884912 and SRR27884920. The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository [57][58] with the data set identifier PXD049410.

    Author contributions



    Conceptualization, A.V.G.; investigation, M.A.O., M.I.S., R.H.Z., N.A.L., A.M.M., N.E.M., S.V.M., E.D.N., E.D.V. and A.V.G.; validation, M.A.O., M.I.S., S.V.M., R.H.Z., N.A.L., A.M.M., N.E.M., S.V.M., E.D.N., V.K.P., and A.V.G.; formal analysis, M.A.O., M.I.S., R.H.Z., and A.V.G.; writing—original draft, M.A.O., M.I.S., S.V.M., R.H.Z., E.D.N., N.E.M., N.A.L., A.M.M., E.D.V., and A.V.G.; data curation, M.A.O., M.I.S., R.H.Z., N.E.M., and A.V.G.; writing—review and editing, M.A.O., M.I.S., R.H.Z., S.V.M., V.K.P. and A.V.G.; visualization, M.A.O., M.I.S., R.H.Z., S.V.M., E.D.N., N.A.L., A.M.M., and A.V.G.; supervision, V.K.P. and A.V.G.; resources, A.V.G.; funding acquisition, A.V.G.; project administration, A.V.G. All authors have read and agreed to the published version of the manuscript.

    [1] Lyte M (2014) Microbial endocrinology and the microbiota-gut-brain axis. Microbial Endocrinology: The MicrobiotaGut-Brain Axis in Health and Disease . New York: Springer 3-24. https://doi.org/10.1007/978-1-4939-0897-4_1
    [2] Luqman A (2023) The orchestra of human bacteriome by hormones. Microb Pathog 106125. https://doi.org/10.1016/j.micpath.2023.106125
    [3] Niu L, Gao M, Wen S, et al. (2023) Effects of catecholamine stress hormones norepinephrine and epinephrine on growth, antimicrobial susceptibility, biofilm formation, and gene expressions of enterotoxigenic Escherichia coli. Int J Mol Sci 24: 15646. https://doi.org/10.3390/ijms242115646
    [4] Reading NC, Rasko D, Torres AG, et al. (2009) A transcriptome study of the QseEF two-component system and the QseG membrane protein in enterohaemorrhagic Escherichia coli O157: H7. Microbiology 156: 1167. https://doi.org.10.1099/mic.0.033027-0
    [5] Moreira CG, Sperandio V (2010) The epinephrine/norepinephrine/autoinducer-3 interkingdom signaling system in Escherichia coli O157: H7. Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health . New York: Springer 213-227. https://doi.org/10.1007/978-1-4419-5576-0_12
    [6] Lopes JG, Sourjik V (2018) Chemotaxis of Escherichia coli to major hormones and polyamines present in human gut. ISME J 12: 2736-2747. https://doi.org/10.1038/s41396-018-0227-5
    [7] Qin T, Xie J, Xi B, et al. (2021) Integrative transcriptomic and proteomic analyses of pathogenic Aeromonas hydrophila in response to stress hormone norepinephrine. Aquac Res 53: 1693-1705. https://doi.org/10.1111/are.15700
    [8] Cambronel M, Nilly F, Mesguida O, et al. (2020) Influence of catecholamines (epinephrine/norepinephrine) on biofilm formation and adhesion in pathogenic and probiotic strains of Enterococcus faecalis. Front Microbiol 11: 1501. https://doi.org/10.3389/fmicb.2020.01501
    [9] Cambronel M, Tortuel D, Biaggini K, et al. (2019) Epinephrine affects motility, and increases adhesion, biofilm and virulence of Pseudomonas aeruginosa H103. Sci Rep 9: 20203. https://doi.org/10.1038/s41598-019-56666-7
    [10] Intarak N, Muangsombut V, Vattanaviboon P, et al. (2014) Growth, motility and resistance to oxidative stress of the melioidosis pathogen Burkholderia pseudomallei are enhanced by epinephrine. Pathog Dis 72: 24-31. https://doi.org/10.1111/2049-632X.12181
    [11] Perraud Q, Kuhn L, Fritsch S, et al. (2022) Opportunistic use of catecholamine neurotransmitters as siderophores to access iron by Pseudomonas aeruginosa. Environ Microbiol 24: 878-893. https://doi.org/10.1111/1462-2920.15372
    [12] Beasley FC, Marolda CL, Cheung J, et al. (2011) Staphylococcus aureus transporters Hts, Sir, and Sst capture iron liberated from human transferrin by Staphyloferrin A, Staphyloferrin B, and catecholamine stress hormones, respectively, and contribute to virulence. BMC Microbiol 11: 199. https://doi.org/10.1128/IAI.00117-11
    [13] Boukerb AM, Cambronel M, Rodrigues S, et al. (2021) Inter-kingdom signaling of stress hormones: sensing, transport and modulation of bacterial physiology. Front Microbiol 12: 690942. https://doi.org/10.3389/fmicb.2021.690942
    [14] Axelrod J, Gordon Ayhitby L, Hertting G, et al. (1961) Studies on the metabolism of catecholamines. Circ Res 9: 715-719. https://doi.org/10.1161/01.RES.9.3.715
    [15] Notelovitz M, Funk S, Nanavati N, et al. (2002) Estradiol absorption from vaginal tablets in postmenopausal women. Obstet Gynecol 99: 556-562. https://doi.org/10.1016/S0029-7844(01)01385-0
    [16] Leis S, Drenkhahn S, Schick C, et al. (2004) Catecholamine release in human skin—a microdialysis study. Exp Neurol 188: 86-93. https://doi.org/10.1016/j.expneurol.2004.03.013
    [17] Stradford AF, Zoutman DE, Dick E, et al. (2002) Effect of lidocaine and epinephrine on Staphylococcus aureus in a guinea pig model of surgical wound infection. Plast Reconstr Surg 110: 1275-1279. https://doi.org/10.1097/01.PRS.0000025427.86301.8A
    [18] Lyte M, Freestone PPE, Neal CP, et al. (2011) Stimulation of Staphylococcus epidermidis growth and biofilm formation by catecholamine inotropes. Lancet 361: 130-135. https://doi.org/10.1016/S0140-6736(03)12231-3
    [19] Mart'yanov SV, Botchkova EA, Plakunov VK, et al. (2021) The impact of norepinephrine on mono-species and dual-species staphylococcal biofilms. Microorganisms 9: 820. https://doi.org/10.3390/microorganisms9040820
    [20] Gannesen AV, Schelkunov MI, Geras'kina OV, et al. (2021) Epinephrine affects gene expression levels and has a complex effect on biofilm formation in Micrococcus luteus strain C01 isolated from human skin. Biofilm 3: 100058. https://doi.org/10.1016/j.bioflm.2021.100058
    [21] Gannesen AV, Ziganshin RH, Zdorovenko EL, et al. (2022) Epinephrine extensively changes the biofilm matrix composition in Micrococcus luteus C01 isolated from human skin. Front Microbiol 13: 1003942. https://doi.org/10.3389/fmicb.2022.1003942
    [22] Gannesen AV, Ziganshin RH, Ovcharova MA, et al. (2023) Epinephrine affects ribosomes, cell division, and catabolic processes in Micrococcus luteus skin strain C01: revelation of the conditionally extensive hormone effect using orbitrap mass spectrometry and proteomic analysis. Microorganisms 11: 2181. https://doi.org/10.3389/fmicb.2022.1003942
    [23] Fournière M, Latire T, Souak D, et al. (2020) Staphylococcus epidermidis and Cutibacterium acnes: two major sentinels of skin microbiota and the influence of cosmetics. Microorganisms 8: 1752. https://doi.org/10.3390/microorganisms8111752
    [24] Akaza N, Takasaki K, Nishiyama E, et al. (2022) The microbiome in comedonal contents of inflammatory acne vulgaris is composed of an overgrowth of Cutibacterium Spp. and other cutaneous microorganisms. Clin Cosmet Investig Dermatol 21: 2003-2012. https://doi.org/10.2147/CCID.S379609
    [25] Jahns AC, Alexeyev OA (2014) Three dimensional distribution of Propionibacterium acnes biofilms in human skin. Exp Dermatol 23: 687-689. https://doi.org/10.1111/exd.12482
    [26] Chiu A, Chon SY, Kimball AB (2003) The response of skin disease to stress: changes in the severity of acne vulgaris as affected by examination stress. Arch Dermatol 139: 897-900. https://doi.org/10.1001/archderm.139.7.897
    [27] Yosipovitch G, Tang M, Dawn AG, et al. (2007) Study of psychological stress, sebum production and acne vulgaris in adolescents. Acta Derm Venereol 87: 135-139. https://doi.org/10.2340/00015555-0231
    [28] Jusuf NK, Putra IB, Sutrisno AR (2021) Correlation between stress scale and serum substance P level in acne vulgaris. Int J Gen Med 1: 681-686. https://doi.org/10.2147/IJGM.S294509
    [29] Boyanova L (2017) Stress hormone epinephrine (adrenaline) and norepinephrine (noradrenaline) effects on the anaerobic bacteria. Anaerobe 44: 13-19. https://doi.org/10.1016/j.anaerobe.2017.01.003
    [30] Borrel V, Thomas P, Catovic C, et al. (2019) Acne and stress: Impact of catecholamines on Cutibacterium acnes. Front Med 6: 155. https://doi.org/10.3389/fmed.2019.00155
    [31] Ovcharova MA, Schelkunov MI, Geras'kina OV, et al. (2023) C-Type natriuretic peptide acts as a microorganism-activated regulator of the skin commensals Staphylococcus epidermidis and Cutibacterium acnes in dual-species biofilms. Biology 12: 436. https://doi.org/10.3390/biology12030436
    [32] Illumina: bcl2fastq and bcl2fastq2 Conversion Software (2024). Available from: https://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html
    [33] Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114-2120. https://doi.org/10.1093/bioinformatics/btu170
    [34] Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25: 1754-1760. https://doi.org/10.1093/bioinformatics/btp324
    [35] Patro R, Duggal G, Love MI, et al. (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14: 417-419. https://doi.org/10.1038/nmeth.4197
    [36] Camacho C, Coulouris G, Avagyan V, et al. (2009) BLAST+: Architecture and applications. BMC Bioinform 10: 421. https://doi.org/10.1186/1471-2105-10-421
    [37] Cold Spring Harbor Protocols: Lysis buffer for protein extraction (2024). Available from: https://cshprotocols.cshlp.org/content/2014/9/pdb.rec081273.full
    [38] Kulak NA, Pichler G, Paron I, et al. (2014) Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat Methods 11: 319-324. https://doi.org/10.1038/nmeth.2834
    [39] Kovalchuk SI, Jensen ON, Rogowska-Wrzesinska A (2019) FlashPack: fast and simple preparation of ultrahigh-performance capillary columns for LC-MS*[S]. Mol Cell Proteom 18: 383-390. https://doi.org/10.1074/mcp.TIR118.000953
    [40] Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11: 2301-2319. https://doi.org/10.1038/nprot.2016.136
    [41] Tyanova S, Temu T, Sinitcyn P, et al. (2016) The Perseus computational platform for comprehensive analysis of (prote) omics data. Nat Methods 13: 731-740. https://doi.org/10.1038/nmeth.3901
    [42] The UniProt Consortium.UniProt: the Universal Protein Knowledgebase in 2023. Nucl Acids Res (2022) 51: D523-D531. https://doi.org/10.1093/nar/gkac1052
    [43] STRING: Protein-Protein Interaction Networks, Functional Enrichment Analysis (2024). Available from: https://string-db.org/cgi/input?sessionId=bJepuA5XwxF1&input_page_show_search=on
    [44] NCBI Basic Local Alignment Search Tool: Protein BLAST (2024). Available from: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins
    [45] Benjamini Y, Hochberg (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B: Stat Methodol 57: 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
    [46] Benjamini Y, Krieger AM, Yekutieli D (2006) Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93: 491-507. https://doi.org/10.1093/biomet/93.3.491
    [47] Staudenmaier H, Van Hove B, Yaraghi Z, et al. (1989) Nucleotide sequences of the fecBCDE genes and locations of the proteins suggest a periplasmic-binding-protein-dependent transport mechanism for iron(III) dicitrate in Escherichia coli. J Bacteriol 171: 2626-2633. https://doi.org/10.1128/jb.171.5.2626-2633.1989
    [48] Brugger SD, Bomar L, Lemon KP (2016) Commensal–pathogen interactions along the human nasal passages. PLoS Pathog 12: e1005633. https://doi.org/10.1371/journal.ppat.1005633
    [49] Penberthy WT, Sadri M, Zempleni J (2020) Biotin. Present knowledge in nutrition . Cambridge: Academic Press 289-303. https://doi.org/10.1016/B978-0-323-66162-1.00017-2
    [50] Hu T, Wei Z, Ju Q, et al. (2021) Sex hormones and acne: State of the art. JDDG 19: 509-515. https://doi.org/10.1111/ddg.14426
    [51] Ray RR, Lahiri D, Chatterjee A, et al. (2021) Bacteria and biofilms as natural inhabitants of our body. Biofilm-Mediated Diseases: Causes and Controls . New York: Springer 47-71. https://doi.org/10.1007/978-981-16-0745-5_3
    [52] Ponomarenko EA, Krasnov GS, Kiseleva OI, et al. (2023) Workability of mRNA sequencing for predicting protein abundance. Genes 14: 2065. https://doi.org/10.3390/genes14112065
    [53] Bakker R, Ellers J, Roelofs D, et al. (2023) Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology. Sci Total Environ 869: 161740. https://doi.org/10.1016/j.scitotenv.2023.161740
    [54] Sharma VK, Akavaram S, Bayles DO (2022) Genomewide transcriptional response of Escherichia coli O157:H7 to norepinephrine. BMC Genomics 23: 107. https://doi.org/10.1186/s12864-021-08167-z
    [55] Achermann Y, Tran B, Kang M, et al. (2015) Immunoproteomic identification of in vivo-produced Propionibacterium acnes proteins in a rabbit biofilm infection model. Clin Vaccine Immunol 22: 467-476. https://doi.org/10.1128/CVI.00760-14
    [56] Perraud Q, Kuhn L, Fritsch S, et al. (2020) Opportunistic use of catecholamine neurotransmitters as siderophores to access iron by Pseudomonas aeruginosa. Environ Microbiol 24: 878-893. https://doi.org/10.1111/1462-2920.15372
    [57] Perez-Riverol Y, Bai J, Bandla C, et al. (2022) The PRIDE database resources in 2022: A Hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res 50: D543-D552. https://doi.org/10.1093/nar/gkab1038
    [58] Deutsch EW, Bandeira N, Perez-Riverol Y, et al. (2023) The ProteomeXchange Consortium at 10 years: 2023 update. Nucleic Acids Res 51: D1539-D1548. https://doi.org/10.1093/nar/gkac1040
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