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Bacteria under stress. How bacteria perceive and process information

Projektarbeit 2014 25 Seiten

Biologie - Mikrobiologie, Molekularbiologie

Leseprobe

Outline

Part A:
Transcriptional regulation of the cadBA operon by the one-component
system CadC
I. Introduction
II. Aims and Tasks
III. Experimental Procedure
IV. Results and Discussion

Part B:
Energetics of the uptake of 14C-lysine by LysP
I. Introduction
II. Aims and Tasks
III. Experimental Procedure
IV. Results and Discussion

Attachment

Part A: Transcriptional regulation of the cadBA operon by the one-component system CadC

I. Introduction

The analysis of stress response systems in microorganisms can reveal molecular strategies for regulatory control and adaptation. For example, external pH implicates as a signal in growing number of genetic and molecular responses in enteric bacteria like Escherichia coli. In number of cases, acid-induced gene expression functions to decrease the acidity of bacterial products in an acidic environment. Thus, enhancing the growth at a low pH.

In this experiment, we examine the gene expression of the E. coli cadBA operon.

The cad operon consists of the enzyme CadA, which is a lysine decarboxylase, the transport protein CadB and the regulatory protein CadC (Figure 1).

The cad operon is active when there is a low pH and lysine in the periplasm of bacteria cells. The pH sensor CadC, which is located in the periplasm, recognizes the low pH and lysine. As a result, DNA can bind and activate the transcription, so that there can be an expression of the two downstream genes cadB and cadA. Furthermore, the CadA enzyme helps to produce cadaverine. The reaction of lysine to cadavarine effectively consumes protons (H⁺) and lead to an increase of the internal pH. Moreover, the antiporter CadB imports the substrate lysine and exports its product cadaverine. Together, they reduce the intracellular H⁺ concentration.

CadC is a member of the ToxR family and can measure the external amount of cadaverine. CadC consists of a sensor domain which is monitoring the extracellular pH and cadavarine; a transmembrane domain which interacts with LysP (Co-sensor for lysine) and the effector domain, which is a DNA-binding domain. CadC can inhibit the transcription of CadA. When the amount of cadaverine increases in the bacterial cell, the cad operon switches off of producing cadaverine by a signalling molecule called ppGpp. Under acidic conditions, ppGpp can bind to CadA and stop the cadaverine production.

In the cad-Module, there is a co-sensor for lysine called LysP which acts as an inhibitor. LysP can inhibit the production of cadaverine. But LysP can also turn into a lysine transporter.

The Cad-module of the bacterial cells require at least two signals, the low pH and a high concentration of lysine. The most important effect of cadaverine is the protection against acidic stress.

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Figure 1: The Cad-module of E. coli. (assumed of Tetsch, L., Jung, K., (2009), “The regulatory interplay between membrane-integrated sensors and transport proteins in bacteria”, Molecular Microbiology 73(6), 982–991; doi:10.1111/j.1365-2958.2009.06847.x)

II. Aims and Tasks

In this part of the practical course, we study how CadC as a pH sensor can activate its downstream genes and can lead to an expression of the genes cadB and cadA. Furthermore, we study how CadC as regulator functions.

In this part of the experiment, we use wild-type strain of E. coli and a mutant strains called ∆cadC. Both types of strains will have different conditions like pH5.8 or pH7.6 and with or without lysine. For ∆cadC, we use samples of different time points. The experiments consist of a qRT-PCR for analysing the expression of CadBA and CadC. Followed by measuring the β-Galactosidase activity with a P cadBA -lacZ reporter gene fusion analysis. At least, we do a single cell analysis of the cadBA-egfp expression with fluorescence microscopy to see the location and distribution of CadA and CadB in the bacterial cells of E. coli.

As a result, we want to find out, analyse and study how E. coli respond to pH stress.

III. Experimental Procedure

The practical procedure and the methods are the same like described in the script of the practical course molecular microbiology I by H. Jung, L. Plener, J. Lassak and S. Bracher.

Part A is divided into three parts with different experiments and methods.

Additions and deviations from the script are noted subsequent to each sub-part.

II – Dynamics of cadBA expression – a PcadBA-lacZ reporter gene fusion analysis

Day 2: We use Over Night cultures and inoculate into 120ml LB pH 7.6.

Day 3: Step two and three which are described in the script, we do not execute.

III. – Single cell analysis of cadBA-efgp expression

Day 1: We inoculate E.coli MG1655 cadB-egfp and cadA-egfp in 5ml LB pH 7.6.

IV. Results and Discussion

I – Conditions of cadBA and cadC expression – a qRT – PCR analysis

For the qRT-PCR analysis, we have eight different samples of the wild-type and ∆cadC mutant measured at different time points and consisting of two different pHs (pH5.8 and pH7.6) and with or without lysine. In table one, there are the different samples listed with different measured concentrations of DNA by NanoDrop. The expected value of the concentration should be about 2-5µg/µl. As shown in table 1, the concentrations of the different samples are high, which tells us that we have a high purity of the DNA samples. The last sample pH5.8 ∆cadC with lysine has a concentration of 498.6ng/µl which is very low.

Table 1: NanoDrop measurement of concentrations. All samples were prewarmed in KE-medium at 37°C. WT stands for wild-type.

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After measurement of concentration, we make a gel-electrophoresis with our samples. In all samples, there is present the 23S rRNA and 16S rRNA (Figure 2). Not visible in our gel-electrophoresis is the 5S rRNA. All bands are visible except sample pH 5.8 WT with lysine after 30 min. This band is not visible but it should be. The 23S rRNA has about 1000bp and the 16S rRNA has about 900bp.

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Figure 2: Gel-electrophoresis. The first bands are the 23S rRNA (yellow arrow), followed by the 16S rRNA (red arrow). M stands for Marker, named 2-log DNA ladder (0,1-10kb); [10000bp, 8000bp, 6000bp, 5000bp, 4000bp, 3000bp, 2000bp, 1500bp, 1200bp, 1000bp, 900bp, 800bp, 700bp, 600bp, 500bp, 400bp, 300bp, 200bp, 100bp].

For the qPCR, we used the samples of above with the different pHs and with or without lysine concerning the wild-type and the mutant ∆cadC. Furthermore, we use digested RNA with cadC specific primers and in addition cDNA with cadA specific primers and cadC specific primers. As a control we used cDNA with recA specific primers. The PCR-product of the control recA, there should not be some changes. Figure 3a shows the qPCR results in a graph. The qPCR of our group 8 is not good, so we make the calculations with the qPCR data of group 5 and group 6. We take an average of all samples of both groups concerning the threshold-CT.

First, we have to calculate the ΔCt with the following formula:

ΔCt=Ct (Target [average tresholdCT] )-Ct (reference [RecA sample]) (1)

The relative quantification can be define by ΔΔCt- method. This method has the following formula:

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Hereby, this formula deals with the number of cycles and the amount of DNA, which has been amplified concerning the amount of fluorescence. So, it is a comparison of the Ct value and the target/reference gene. The PCR efficiencies (E) for this method should not have a difference above 10%.

The PCR efficiencies (E) can be calculated by the following formula:

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The Slope can be calculated by the logarithmic scale of the fluorescence against the cycle threshold. As you can see in figure 3b, the logarithmic scale shows us that the exponential phase is like a linear regression line.

[illustration not visible in this excerpt] Figure 3a: Results of the qPCR. The samples with the sign ^^ are RNA. The sample with the sign * is cDNA (cadA) and sample without a sign is cDNA (cadC). WT means wild-type.

[illustration not visible in this excerpt] Figure 3b: Results of the qPCR. Logarithmic scale. E.g. Linear regression line. The samples with the sign ^^ are RNA. The sample with the sign * is cDNA (cadA) and sample without a sign is cDNA (cadC). WT means wild-type.

The results of the ΔΔCt-method shows cadA and cadC after 0, 15, 30 and 60 minutes incubation time with a pH5.8 (WT) and lysine (figure 3c). To be considered to the formula of the ΔΔCt-method, we use as the calibrator the sample of 0 minutes. After 60 min incubation time, the cadC has a positive ΔΔCt-value.

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Figure 3c: Relative quantification of cadA and cadC with the ΔΔCt-method of the wild-type sample at different time points under the condition of pH 5.8 with lysine. Calibrator is time point 0.

With the ΔΔCt-quantification method, the results shows, that the cadC of the mutant strain Δ cadC has a very high value folloed by a high value of cadA by the WT pH7.6 sample (figure 3d).

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Figure 3d: Relative quantification of cadA and cadC with the ΔΔCt-method of the different samples under different conditions. Calibrator are WT pH7.6 and WT pH5.8 for each own conditions.

In table 4a, you can have a look at the calculated data of the slope and of E, the PCR efficiencies. As you can see below, the slope value is in most cases around 0,20 to 0,25. The E-value has some variability.

Table 4a: Calculated slope and PCR efficiencies (E) of different PCR samples.

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Moreover, there has to be a determination of the cDNA copies concerning the absolute quantification in each single bacterial cell which requires a plasmid. The plasmid is a reference DNA for defining the primers efficiencies and a linear equation from the standard curve for primers. As a result, this is a calculation of cDNA for absolute quantification. Below, there are the different linear equations of the standard curves for the three primers.

Primer CadA:

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Primer CadB:

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Primer CadC:

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As we know, the Ct-value of the three standard curves is 26. This value 26 stands for the plasmid concentration called DNAconcentration. You only have to solve the “primer equations” from above. Example for Primer CadA:

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This result shows us the plasmid concentration. Furthermore, we also know that the plasmid has 11330bp. Now, we have to calculate the average of bp of the molecular weight.

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Furthermore, we have to calculate the number of plasmids.

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The calculated number of plasmid at a CT- value of 26 is 2,5*1017mol/µl. This number is not correct and not realistic. As a results, we should have a number which makes sense concerning the minimum number of DNA copies which are detected by the qPCR.

To sum up, both genes cadA and cadC are expressed under low pH condiciotns and with lysine. The lysine concentration can play a role in the expression of the two genes.

II – Dynamics of cadBA expression – a PcadBA-lacZ reporter gene fusion analysis

The aim of this part of the experiment is to determine the β-galactosidase activity by measuring the OD420nm and the OD600nm of three different samples (WT pH5.8 with lysine, WT pH7.6 with lysine and mutant strain pH5.8 ∆cadC with lysine ) over a time frame of 120 minutes. The calculations occur with Miller Units formula:

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The calculated activity is shown in table 5 in the attachment. The results of the OD measurements are an average of group 5 to 8. The volume is 1ml.

The below-mentioned figure 6 shows the β-galactosidase activity (MU) measured over 120 minutes. Concerning the cad -operon, the β-galactosidase activity of WT pH7.6 with lysine is the highest one. In terms of the mutant pH5.8 ∆cadC with lysine and WT pH5.8 with lysine, there is a very low activity. This shows us that the cad -system need a low pH of 7.6 and lysine to be active, to produce cadaverine and to express the genes cadB and cadA.

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Figure 6: Measurement of the β-galactosidase activity over a time frame of 120 minutes. Three different samples: pH 5.8 ∆cadC with lysine; pH 7.6 WT with lysine; pH 5.8 WT with lysine. WT stands for wild-type; MU stands for Miller Units.

Furthermore, figure 7 shows also the β-galactosidase activity (MU) over a time frame of 120min. But this activity data is normalized to time point zero. The activity is the same as mention above. The normalized data is for prevention of errors. Errors like of the OD-measurement or by pipetting.

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Figure 7: Measurement of the β-galactosidase activity (MU) over a time frame of 120 minutes. The acticity (MU) data is normalize to time point 0. Three different samples: pH 5.8 ∆cadC with lysine; pH 7.6 WT with lysine; pH 5.8 WT with lysine. WT stands for wild-type; MU stands for Miller Units.

III. – Single cell analysis of cadBA-efgp expression

The aim is to make a fluorescence microcopy of the egfp (enhanced green fluorescence protein) of cadB and cadA. The used sample is the wild-type with pH7.6 and lysine. Lysine is used to induce the signal.

Generally, CadB is locate in the cytoplasmic membrane in contrast to CadA which is locate in the cytoplasm.

As you can see in figure 8, the whole cell is green fluorescence which indicates that is CadB. In contrast to CadB, there is only one part of the cell green fluorescent and this is CadA (figure 9 in attachment).

These results shows us, that there is a different localization of CadA and CadB and also a different distribution of the E. coli wild-type cells.

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Figure 8: Fluorescence microscopy of CadB over a time frame of 80min. The used sample is an Over Night culture of wild-type pH7.6 with lysine.

The amount of no signal, weak signal and a strong signal is important. To show this, we count the number of cells of three different signal types. Figure 10 to 12 shows the results. The strong signal of CadB is after 45 min. present. 51% of the cells have a strong CadB signal. Before and after 45 min. the amount of the strong CadB signal is nearly the same. It has to be mention that we induce the sample with lysine at time point zero.

Directly after the induction, there is no strong signal visible. The results of the weak signal is shown in figure 11. After 45min. the majority of cells which means 47% have a weak signal. A weak signal also exist after 15min.

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Figure 10: Fluorescence microscopy of CadB over a time frame of 80min. The diagram shows the amount of appearance of a strong signal over time. The used sample is an Over Night culture of wild-type pH7.6 with lysine. Numbers indicate the time frame in minutes.

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Figure 11: Fluorescence microscopy of CadB over a time frame of 80min. The diagram shows the amount of appearance of a weak signal over time. The used sample is an Over Night culture of wild-type pH7.6 with lysine. Numbers indicate the time frame in minutes.

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Figure 12: Fluorescence microscopy of CadB over a time frame of 80min. The diagram shows the amount of appearance of no signal over time. The used sample is an Over Night culture of wild-type pH7.6 with lysine. Numbers indicate the time frame in minutes.

The diagram of figure 12 shows that 38% of the cells have no signal. It is important to mention that we count most cells at time point 45min (Table 13 and 14 attachment).

CadA (figure 9 attachment) shows a different percentage amount of the three signals. Figure 15 (attachment) shows that at 40minutes there have 40% of the counted cells a strong signal. At 60 minutes, 31% of the counted cells have a strong signal. At 80%, 36% of the counted cells have a strong signal. Only 29% at a time point of 60min. shows a weak signal (figure 16). The majority of 24% have no signal at time point 15min (figure17).

To sum up, all results of the single cell analysis shows that a strong signal of CadB can be seen at a time point of 45minutes after the induction with lysine. A strong signal of CadA can be seen after 40/60 and 80minutes after induction. Most of the cells which have no signal can be seen directly after the induction to 30minutes. Neither CadA nor CadB have cells which shows a signal.

Part B: Energetics of the uptake of 14C-lysine by LysP

I. Introduction

All living cells are strictly separated from their surroundings by a membranous lipid bilayer. Into these membranes a variety of transport proteins is embedded that ensure the uptake and secretion of various molecules and ions. In order to respond properly to a changing nutrient supply or demand, as well as to external stress factors, cells must be able to adapt both amount and activity of the corresponding transporters.

There are several ways for a substrate to cross the membrane when solutes are small and polar like H₂O, urea and hydrophobic molecules (figure 18).

First, they can cross the membrane by simple diffusion. The second possibility is the carrier. A carrier transports molecules which bind to one side of the membrane and carry it through the membrane and release it on the other side. Carriers are used as energetic inhibitors. The typical examples of carriers are valinomycin (K⁺) and CCCP (H⁺).

Another way of transport are channels. Channels change their conformation between open and close. When the channel is open, it provides a continuous pathway through the bilayer, allowing flux of many ions. The examples of this type are KcsA (K⁺) and VDCC (Ca²⁺).

At least, a transporter named permease is another type of membrane transport protein that facilitate the diffusion of a specific molecule in or out of the cell. One type of permease has a chain of successive solute binding sites (e.g. bacteriorhodopsin (H⁺)). Another type has a single solute binding site such as LacY (lactose) and PutP (L-proline).

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Figure 18: Basic mechanisms of membrane transport (modified from H. Jung, lecture about transport).

II. Aims and Tasks

In this part of the experiment, we want to measure the lysine uptake of LysP which is a co-sensor for lysine and also the CadC inhibitor. In order to do this, we need 14C labelled lysine and we use inhibitors which affect the energy metabolism of the E. coli cells. The inhibitors are valinomycin and 2, 4-Dinitrophenol. Both were measured at a triple determination at 0 and 1 min. There is also a triple determination of DMSO at the same time point.

III. Experimental Procedure

The experimental procedure was the same like described in the script. Subsequent, the additions and deviations would be noted.

I - Investigation of the energetic basis of 14C-lysine uptake by LysP, a member of the LeuT structural family

We add 10µM 14C-labeled L-lysine (2µl of 1mM stock with 26Ci/mol) to the vial wall and vortex rapidly to start response. Measuring of wildtype, negative control and DMSO and also two inhibitors named Valinomycin and 2,4-Dinitrophenol.

IV. Results and Discussion

I - Investigation of the energetic basis of 14C-lysine uptake by LysP, a member of the LeuT structural family

As a result, the measurement of LysP shows a high increase of the lysine uptake in about 30 minutes (figure 19). As a negative control, it should be clear that there is no lysine uptake over the time. As it is shown in figure 19, there is a very little increase in the lysine uptake but it does not matter. The graph shows that there is almost no lysine uptake at the time zero and gradually over time the lysine uptake increase. The point is that if we measure for a longer time period (e.g. to 60min), we can see that the lysine uptake reaches saturation. This result, we can use for the inhibitor study because of the increase of the lysine uptake over time.

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Figure 19: Measured lysine uptake of LysP and the negative control.

Measurement of three different inhibitors named DMSO, 2,4-dinitrophenol and valinomycin is successful. As it shown in the chart figure (20), DMSO is a control. There is no inhibition but for dinitrophenol we can see that it is only 4% activity left so it almost completely inhibited. Valinomycin shows a very high percentage about more than 100% of inhibition of transport (it is a little higher than 100% but it is only because of scattering of data). So there is no inhibition of valinomycin. Considering the nature of the valinomycine we can say that there was a collapse of membrane but it does not affect the transport of molecules. Furthermore, it has to be mention that the error rate is different. Valinomycin has a higher error rate than DMSO and dinitrophenol.

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Figure 20: Measurement of the inhibition of transport of DMSO, dinitrophenol and valinomycin. Error bars are shown.

Attachment

II – Dynamics of cadBA expression – a PcadBA-lacZ reporter gene fusion analysis

Table 5: Measured β-galactosidase activity (MU) of ∆cadc, WT pH7.6 and WT pH5.8. WT stands for wild-type.

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III. – Single cell analysis of cadBA-efgp expression

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Figure 9: Fluorescence microscopy of CadA over a time frame of 80min. The used sample is an Over Night culture of wild-type pH7.6.

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Details

Seiten
25
Jahr
2014
ISBN (eBook)
9783668523074
ISBN (Buch)
9783668523081
Dateigröße
1 MB
Sprache
Englisch
Katalognummer
v374801
Institution / Hochschule
Ludwig-Maximilians-Universität München
Note
2.0
Schlagworte
Stress Bacteria Transcriptional regulation One-component-system E.coli Cad-module qPCR uptake membrane

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Titel: Bacteria under stress. How bacteria perceive and process information