Uropathogenic Escherichia coli (UPEC) is the most common bacterial agent associated with urinary tract infections, threatening public health systems with elevated medical costs and high morbidity rates. The successful establishment of the infection is associated with virulence factors encoded in its genome, in addition to antibacterial resistance genes, which could limit the treatment and resolution of the infection. In this sense, plant extracts from the genus Echeveria have traditionally been used to treat diverse infectious diseases. However, little is known about the effects of these extracts on bacteria and their potential mechanisms of action. This study aims to sequence a multidrug-resistant UPEC isolate (UTI-U7) and assess the multilocus sequence typing (MLST), virulence factors, antimicrobial resistance profile, genes, serotype, and plasmid content. Antimicrobial susceptibility profiling was performed using the Kirby-Bauer disk diffusion. The antibacterial and anti-adherent effects of the methanol extracts (ME) of Echeveria (E. craigiana, E. kimnachii, and E. subrigida) against UTI-U7 were determined. The isolate was characterized as an O25:H4-B2-ST2279-CH40 subclone and had resistant determinants to aminoglycosides, β-lactams, fluoroquinolones/quinolones, amphenicols, and tetracyclines, which matched with the antimicrobial resistance profile. The virulence genes identified encode adherence factors, iron uptake, protectins/serum resistance, and toxins. Identified plasmids belonged to the IncF group (IncFIA, IncFIB, and IncFII), alongside several prophage-like elements. After an extensive genome analysis that confirmed the pathogenic status of UTI-U7 isolate, Echeveria extracts were tested to determine their antibacterial effects; as an extract, E. subrigida (MIC, 5 mg/mL) displayed the best inhibitory effect. However, the adherence between UTI-U7 and HeLa cells was unaffected by the ME of the E. subrigida extract.
Citation: Ana M. Castañeda-Meléndrez, José A. Magaña-Lizárraga, Marcela Martínez-Valenzuela, Aldo F. Clemente-Soto, Patricia C. García-Cervantes, Francisco Delgado-Vargas, Rodolfo Bernal-Reynaga. Genomic characterization of a multidrug-resistant uropathogenic Escherichia coli and evaluation of Echeveria plant extracts as antibacterials[J]. AIMS Microbiology, 2024, 10(1): 41-61. doi: 10.3934/microbiol.2024003
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Uropathogenic Escherichia coli (UPEC) is the most common bacterial agent associated with urinary tract infections, threatening public health systems with elevated medical costs and high morbidity rates. The successful establishment of the infection is associated with virulence factors encoded in its genome, in addition to antibacterial resistance genes, which could limit the treatment and resolution of the infection. In this sense, plant extracts from the genus Echeveria have traditionally been used to treat diverse infectious diseases. However, little is known about the effects of these extracts on bacteria and their potential mechanisms of action. This study aims to sequence a multidrug-resistant UPEC isolate (UTI-U7) and assess the multilocus sequence typing (MLST), virulence factors, antimicrobial resistance profile, genes, serotype, and plasmid content. Antimicrobial susceptibility profiling was performed using the Kirby-Bauer disk diffusion. The antibacterial and anti-adherent effects of the methanol extracts (ME) of Echeveria (E. craigiana, E. kimnachii, and E. subrigida) against UTI-U7 were determined. The isolate was characterized as an O25:H4-B2-ST2279-CH40 subclone and had resistant determinants to aminoglycosides, β-lactams, fluoroquinolones/quinolones, amphenicols, and tetracyclines, which matched with the antimicrobial resistance profile. The virulence genes identified encode adherence factors, iron uptake, protectins/serum resistance, and toxins. Identified plasmids belonged to the IncF group (IncFIA, IncFIB, and IncFII), alongside several prophage-like elements. After an extensive genome analysis that confirmed the pathogenic status of UTI-U7 isolate, Echeveria extracts were tested to determine their antibacterial effects; as an extract, E. subrigida (MIC, 5 mg/mL) displayed the best inhibitory effect. However, the adherence between UTI-U7 and HeLa cells was unaffected by the ME of the E. subrigida extract.
Wide-type fruit flies, Drosophila melanogaster, might be the most extensively studied organism in circadian rhythm research. The researches of endogenous activity rhythm on Drosophila generally involve two different kinds of clock genes, called period (per, for short) [14,10] and timeless (tim, for short) [20,27]. Their encoded proteins, PER and TIM, bind to each other [5,0,27,29].
PER protein and per mRNA cycle in a
An alternative way to study circadian rhythms is based on a positive feedback, introduced by PER phosphorylation being an activator to PER [26]. Phosphorylation of PER is operated by a double-time gene encoded kinase, DOUBLE-TIME (DBT, for short) [13,16]. As suggested by the dbt mutants phenotypes, PER phosphorylation might be precluded to its degradation. PER and TIM stimulate transcription of per and tim genes by activating dClOCK [2]. Experimental results suggest that per mRNA is stabilized by PER/TIM dimers [24], and PER is stabilized by dimerization with TIM [13,16].
The idea that PER phosphorylation introduces a positive feedback in PER accumulation can be expressed in a model of three-dimensional ordinary differential equations [26] (see (1) below). In [26], by imposing assumptions that the dimerization reactions were fast and dimeric proteins were in rapid equilibrium, they reduced the three-dimensional model to a pair of nonlinear ordinary differential equations of mRNA and total protein concentrations (see (2) below). Then they used the powerful phase plane portraiture to study the simplified two-dimensional model. In this paper, we explore the original three-dimensional model directly. It is shown that the circadian rhythms occur if the model possesses a unique equilibrium which is unstable. Furthermore, we deeply investigate how circadian rhythms are affected by several model parameters, including mRNA translation, mRNA degradation, monomer phosphorylation, protein proteolysis, association of PER/TIM protein and equilibrium constant for dimerization. The results help to explain some former-observed phenomena of circadian rhythms. In particular, our numerical results extremely agree with those given in [26], indicating that their reduction work is greatly reasonable.
In this section, we restate the model proposed by Tyson et al. [26]. The molecular mechanism for the circadian rhythm in Drosophila is summarized in Figure 1. Here the total PER (monomer
The mechanism in Figure 1 could be translated into a set of six differential equations, for per and tim mRNAs, PER and TIM monomers, and PER/TIM dimers in the cytoplasm and nucleus. Such a complicated set of equations could not efficiently illustrate the importance of positive feedback in the reaction mechanism. So by noticing that PER and TIM messages and proteins followed roughly similar time courses in vivo, Tyson et al. [26] lumped them into a single pool of clock proteins. In addition, they assumed that the cytoplasmic and nuclear pools of dimeric protein were in rapid equilibrium. Then they established the following differential equations for [mRNA]=
{dMdt=vm1+(P2/Pcrit)2−kmM,dP1dt=vpM−k′p1P1JP+P1+rP2−kp3P1−2kaP21+2kdP2,dP2dt=kaP21−kdP2−kp2P2JP+P1+rP2−kp3P2. | (1) |
Here monomer was assumed to be phosphorylated more quickly than dimer, i.e.,
In their work, it was further assumed that the dimerization reactions were fast (
{dMdt=vm1+(Pt(1−q)/(2Pcrit))2−kmM,dPtdt=vpM−kp1Ptq+kp2PtJP+Pt−kp3Pt, | (2) |
where
q=q(Pt)=21+√1+8KeqPt. |
Two widely concerned points of circadian rhythms are whether the endogenous rhythms exist and how long the periods are. Since the mechanism has already been translated into mathematical models, attentions are drawn to examine the existence of periodic orbits and calculate the periods. In their work, system (2) has been thoroughly analyzed. In this paper, we try to study system (1). A typical oscillating solution of system (1) is illustrated in Figure 2, where the corresponding parameter values are chosen from Table 1.
Name | Value | Units | | Description |
1 | | 6 | Maximum rate of synthesis of mRNA | |
| 0.1 | | 4 | First-order rate constant for mRNA degradation |
| 0.5 | | 6 | Rate constant for translation of mRNA |
| 10 | | 6 | |
| 0.03 | | 6 | |
| 0.1 | | 6 | First-order rate constant for proteolysis |
| 200 | | -12 | Equilibrium constant for dimerization |
| 0.1 | | 6 | Dimer concen at the half-maximum transcription rate |
| 0.05 | | -16 | Michaelis constant for protein kinase (DBT) |
This table is adapted from Tyson et al. [26]. Parameters |
It is well-known that for higher dimensional ordinary differential equations, there is no so-called Poincaré-Bendixson theory: any limit set is a limit cycle if it contains no steady state. So, in order to use the powerful phase plane analysis tools, Tyson et al. [26] reduced (1) into (2) by imposing some assumptions. Fortunately, we observe that (1) is a three-dimensional competitive system in some sense [8,0,22,23]. For
Theorem 3.1. Suppose (1) has a unique steady state
Numerical calculation suggests that (1) has a unique equilibrium in a large region of parameter values. However, limit cycles do not exist all the time. According to Theorem 3.1, when either
Equilibrium1 | Eigenvalues | ||
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1 Those zeros in equilibrium terms are actually very small positive numbers. Other parameter values are as given in Table 1. |
Comparing with the two-dimensional system (2), there are two more parameters
0.001 | 0.1 | 0.8 | 0.9 | 1 | 10 | 100 | |
Period | none | none | none | 72.44 | 63.10 | 50.89 | 32.51 |
| 500 | 1000 | 5000 | ||||
Period | 28.61 | 26.90 | 24.86 | 24.54 | 24.27 | 24.24 | 24.21 |
| |||||||
Period | 24.21 | 24.21 | 24.21 | 24.30 | 24.44 | ||
Periodic oscillations happen when |
| 0.001 | 0.1 | 1.1 | 1.2 | 2 | 10 | 100 | 500 |
Period | none | none | none | 57.19 | 55.67 | 41.34 | 30.98 | 29.21 |
1000 | 2000 | 5000 | ||||||
Period | 28.94 | 28.80 | 28.71 | 28.67 | 28.65 | 28.65 | 29.20 | 30.37 |
Periodic oscillations occur when |
Based on Tables 3 and 4, one can choose a suitable value of
Genotype | | Temp | Period | Genotype | | | Period |
Wild type | 245 | 20 | 24.2 | | 10 | 0.03 | 24.2 |
200 | 25 | 24.2 | | 15 | 0.06 | 24.3 | |
164 | 30 | 24.2 | | 20 | 0.09 | 25.7 | |
| 18.4 | 20 | 26.5 | | 10 | 0.3 | 17.6 |
15.0 | 25 | 28.7 | | 10 | 0.03 | 24.2 | |
12.3 | 30 | 30.4 | | 10 | 0.003 | 25.1 | |
To simplify the integration, we take |
Table 5 is due to the original three-dimensional system (1). As a comparison, we state Table 6, which is cited from [26] and based on the reduced two-dimensional system (2). Clearly, one can see that Table 5 and Table 6 are almost the same, which indicates that the reduction in [26] is greatly reasonable from this perspective.
Genotype | | Temp | Period | Genotype | | | Period |
Wild type | 245 | 20 | 24.2 | | 10 | 0.03 | 24.2 |
200 | 25 | 24.2 | | 15 | 0.06 | 24.4 | |
164 | 30 | 24.2 | | 20 | 0.09 | 25.7 | |
| 18.4 | 20 | 26.5 | | 10 | 0.3 | 17.6 |
15.0 | 25 | 28.7 | | 10 | 0.03 | 24.2 | |
12.3 | 30 | 30.5 | | 10 | 0.003 | 25.2 | |
This table is copied out of Tyson et al. [26]. It is assumed that each parameter |
In the next section, we will see more about the relation between circadian rhythms and parameters of (1).
In the actual experiment, parameters of the circadian rhythms models are hard to be measured, or even unmeasurable. Parameter values in Table 1 have been chosen to yield a period close to 24-hours and ensure temperature compensation of the wild-type oscillator. The parameter values are arbitrary. Other combinations of parameter values may also yield circadian oscillations with possibly different periods.
It is significant to study how parameters of (1) affect its periodic oscillations. The numerical results are given in Figure 3, where the following parameters are considered: mRNA translation, mRNA degradation, monomer phosphorylation, protein proteolysis, association of PER/TIM protein and equilibrium constant for dimerization.
As shown in Figure 3A, periodic oscillation disappears when the protein synthesis rate
The PER/TIM complex formation plays a key role in the model. Circadian rhythm is markedly affected by the dimerization reaction, precisely in the model, by the association rate constant
In Figure 3B we show how the oscillation is affected by mRNA synthesis. Periodic rhythm requires the mRNA synthesis rate
According to Theorem 3.1, in Figure 4 we inspect the dependence of oscillations on parameters
Appendix. The concentrations of mRNA, monomers and dimers are naturally nonnegative. We therefore focus on the first orthant
Let
{f1(a,P1,P2)=vm1+(P2/Pcrit)2−kma<vm−kma<0,f2(M,b,P2)=vpM−k′p1bJP+b+rP2−kp3b−2kab2+2kdP2<vpa−kp3b<0,f3(M,P1,c)=kaP21−kdc−kp2cJP+P1+rc−kp3c<−kp2cJP+P1+rc−kp3c<0. |
The vector field for (1) on the boundary of
Proposition 1. For any
From Proposition 1, there are at least one steady state in
By computing the Jacobian matrix of (1), one has
Df=(−0−+−+0+−), |
where ''
Theorem A. Suppose (1) has a unique steady state
Therefore, in order to study the oscillations for (1), one only needs to discuss its steady state and the local stability of the steady state.
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1. | Shuang Chen, Jinqiao Duan, Ji Li, Dynamics of the Tyson–Hong–Thron–Novak circadian oscillator model, 2021, 420, 01672789, 132869, 10.1016/j.physd.2021.132869 | |
2. | Alessio Franci, Marco Arieli Herrera-Valdez, Miguel Lara-Aparicio, Pablo Padilla-Longoria, Synchronization, Oscillator Death, and Frequency Modulation in a Class of Biologically Inspired Coupled Oscillators, 2018, 4, 2297-4687, 10.3389/fams.2018.00051 |
Name | Value | Units | | Description |
1 | | 6 | Maximum rate of synthesis of mRNA | |
| 0.1 | | 4 | First-order rate constant for mRNA degradation |
| 0.5 | | 6 | Rate constant for translation of mRNA |
| 10 | | 6 | |
| 0.03 | | 6 | |
| 0.1 | | 6 | First-order rate constant for proteolysis |
| 200 | | -12 | Equilibrium constant for dimerization |
| 0.1 | | 6 | Dimer concen at the half-maximum transcription rate |
| 0.05 | | -16 | Michaelis constant for protein kinase (DBT) |
This table is adapted from Tyson et al. [26]. Parameters |
Equilibrium1 | Eigenvalues | ||
| | | |
| | ||
| |||
| | ||
| | ||
| | | |
| | ||
| | ||
| | ||
| | ||
| | ||
| | | |
| | ||
| | ||
| | ||
| | ||
1 Those zeros in equilibrium terms are actually very small positive numbers. Other parameter values are as given in Table 1. |
0.001 | 0.1 | 0.8 | 0.9 | 1 | 10 | 100 | |
Period | none | none | none | 72.44 | 63.10 | 50.89 | 32.51 |
| 500 | 1000 | 5000 | ||||
Period | 28.61 | 26.90 | 24.86 | 24.54 | 24.27 | 24.24 | 24.21 |
| |||||||
Period | 24.21 | 24.21 | 24.21 | 24.30 | 24.44 | ||
Periodic oscillations happen when |
| 0.001 | 0.1 | 1.1 | 1.2 | 2 | 10 | 100 | 500 |
Period | none | none | none | 57.19 | 55.67 | 41.34 | 30.98 | 29.21 |
1000 | 2000 | 5000 | ||||||
Period | 28.94 | 28.80 | 28.71 | 28.67 | 28.65 | 28.65 | 29.20 | 30.37 |
Periodic oscillations occur when |
Genotype | | Temp | Period | Genotype | | | Period |
Wild type | 245 | 20 | 24.2 | | 10 | 0.03 | 24.2 |
200 | 25 | 24.2 | | 15 | 0.06 | 24.3 | |
164 | 30 | 24.2 | | 20 | 0.09 | 25.7 | |
| 18.4 | 20 | 26.5 | | 10 | 0.3 | 17.6 |
15.0 | 25 | 28.7 | | 10 | 0.03 | 24.2 | |
12.3 | 30 | 30.4 | | 10 | 0.003 | 25.1 | |
To simplify the integration, we take |
Genotype | | Temp | Period | Genotype | | | Period |
Wild type | 245 | 20 | 24.2 | | 10 | 0.03 | 24.2 |
200 | 25 | 24.2 | | 15 | 0.06 | 24.4 | |
164 | 30 | 24.2 | | 20 | 0.09 | 25.7 | |
| 18.4 | 20 | 26.5 | | 10 | 0.3 | 17.6 |
15.0 | 25 | 28.7 | | 10 | 0.03 | 24.2 | |
12.3 | 30 | 30.5 | | 10 | 0.003 | 25.2 | |
This table is copied out of Tyson et al. [26]. It is assumed that each parameter |
Name | Value | Units | | Description |
1 | | 6 | Maximum rate of synthesis of mRNA | |
| 0.1 | | 4 | First-order rate constant for mRNA degradation |
| 0.5 | | 6 | Rate constant for translation of mRNA |
| 10 | | 6 | |
| 0.03 | | 6 | |
| 0.1 | | 6 | First-order rate constant for proteolysis |
| 200 | | -12 | Equilibrium constant for dimerization |
| 0.1 | | 6 | Dimer concen at the half-maximum transcription rate |
| 0.05 | | -16 | Michaelis constant for protein kinase (DBT) |
This table is adapted from Tyson et al. [26]. Parameters |
Equilibrium1 | Eigenvalues | ||
| | | |
| | ||
| |||
| | ||
| | ||
| | | |
| | ||
| | ||
| | ||
| | ||
| | ||
| | | |
| | ||
| | ||
| | ||
| | ||
1 Those zeros in equilibrium terms are actually very small positive numbers. Other parameter values are as given in Table 1. |
0.001 | 0.1 | 0.8 | 0.9 | 1 | 10 | 100 | |
Period | none | none | none | 72.44 | 63.10 | 50.89 | 32.51 |
| 500 | 1000 | 5000 | ||||
Period | 28.61 | 26.90 | 24.86 | 24.54 | 24.27 | 24.24 | 24.21 |
| |||||||
Period | 24.21 | 24.21 | 24.21 | 24.30 | 24.44 | ||
Periodic oscillations happen when |
| 0.001 | 0.1 | 1.1 | 1.2 | 2 | 10 | 100 | 500 |
Period | none | none | none | 57.19 | 55.67 | 41.34 | 30.98 | 29.21 |
1000 | 2000 | 5000 | ||||||
Period | 28.94 | 28.80 | 28.71 | 28.67 | 28.65 | 28.65 | 29.20 | 30.37 |
Periodic oscillations occur when |
Genotype | | Temp | Period | Genotype | | | Period |
Wild type | 245 | 20 | 24.2 | | 10 | 0.03 | 24.2 |
200 | 25 | 24.2 | | 15 | 0.06 | 24.3 | |
164 | 30 | 24.2 | | 20 | 0.09 | 25.7 | |
| 18.4 | 20 | 26.5 | | 10 | 0.3 | 17.6 |
15.0 | 25 | 28.7 | | 10 | 0.03 | 24.2 | |
12.3 | 30 | 30.4 | | 10 | 0.003 | 25.1 | |
To simplify the integration, we take |
Genotype | | Temp | Period | Genotype | | | Period |
Wild type | 245 | 20 | 24.2 | | 10 | 0.03 | 24.2 |
200 | 25 | 24.2 | | 15 | 0.06 | 24.4 | |
164 | 30 | 24.2 | | 20 | 0.09 | 25.7 | |
| 18.4 | 20 | 26.5 | | 10 | 0.3 | 17.6 |
15.0 | 25 | 28.7 | | 10 | 0.03 | 24.2 | |
12.3 | 30 | 30.5 | | 10 | 0.003 | 25.2 | |
This table is copied out of Tyson et al. [26]. It is assumed that each parameter |