Chemotaxis of Escherichia Coli to Controlled Gradients of Attractants(1)
description
Transcript of Chemotaxis of Escherichia Coli to Controlled Gradients of Attractants(1)
-
Chemotaxis of Escherichia coli to controlled
gradients of attractants: Experiments and
Mathematical modeling
Submitted in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
in
CHEMICAL ENGINEERING
by
VUPPULA RAJITHA
(Roll No. 04402602)
Supervisor: Prof. Mahesh S. Tirumkudulu
Co-Supervisor: Prof. K. V. Venkatesh
Department of Chemical Engineering
Indian Institute of Technology Bombay
2009
-
Declaration
I declare that this written submission represents my ideas in my own words
and where others ideas or words have been included, I have adequately cited and
referenced the original sources. I also declare that I have adhered to all principles
of academic honesty and integrity and have not misrepresented or fabricated or
falsified any idea/data/fact/source in my submission. I understand that any vio-
lation of the above will be cause for disciplinary action by the Institute and can
also evoke penal action from the sources which have thus not been properly cited
or from whom proper permission has not been taken when needed.
Vuppula Rajitha
(Roll No. 04402602)
i
-
Abstract
Chemotaxis is a phenomenon in which a microorganism or multi-cellular organisms
are able to direct their movements in the presence of certain chemicals in their envi-
ronment. This could be either towards favorable chemicals called chemoattractants
or away from unfavorable chemicals called chemorepellents. The understanding
of this phenomena is important in many biological processes including immune
response, embryo-genesis, wound healing, bio-film formation and bio-remediation
of subsurface contaminants. The objective of this thesis is to study the response
of microorganisms in the presence of controlled gradients of chemoattractants us-
ing a micro-capillary. This would involve developing mathematical models that
describe the chemotaxis pathway in a chemotactic cell and to relate the kinetics
to the motion of cell in the presence of chemoattractants. The predictions of the
model will be compared with experiments where the motion of cell in the presence
of chemoattractants will be tracked. The previous studies have reported average
drift velocities for a given gradient and do not measure drift velocities as a func-
tion of time and space. To address this issue, a novel experimental technique was
developed to quantify the motion of E. coli cells to varying concentrations and
gradients of chemoattractant so as to capture the spatial and temporal variation of
the drift velocity. The statistics of single cell motions such as cell velocity, tumbling
frequency, rotational diffusion, drift velocity and translational diffusivity are ob-
tained independently. An existing two state receptor model of Barkai and Leibler
(1997) was used for the intracellular pathway and into this the extra-cellular in-
fluence such as ligand concentration and Brownian motion was incorporated to
predict the response for the experimental conditions. The model predicted the ex-
ii
-
perimentally observed increase in drift velocity with gradient and the subsequent
adaptation. Our study revealed that the rotational diffusivity induced by the ex-
tracellular environment is crucial in determining the drift velocity of E. coli. The
model predictions matched with experimental observations only when the response
of the intracellular pathway was highly ultra-sensitive to overcome the extracel-
lular randomness. The parametric sensitivity of the pathway indicated that the
dissociation constant for the binding of the ligand and the rate constants of the
methylation/demethylation of the receptor are key to predict the performance of
the chemotactic behavior. The study demonstrates the key role of oxygen in the
chemotaxis response and that the response to a ligand cannot be analyzed in isola-
tion to effects of oxygen. Further, the study noted that the chemotactic response
also depends on the nature of the chemical used as a chemoattractant which in
turn decides the energy status of the cell.
iii
-
Contents
Abstract ii
List of Figures xii
List of Tables xiii
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Materials and Methods 19
2.1 Experimental Protocols . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Modeling Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Mathematical modeling and Experimental validation of chemo-
taxis under controlled gradients of methyl-aspartate in Escherichia
coli 36
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4 Chemotaxis of Escherichia coli to L-serine 56
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
iv
-
4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5 Chemotaxis of Escherichia coli in the presence of glucose via phos-
photransferase (PTS) pathway 76
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
6 Conclusions and Future study 88
References 96
v
-
List of Figures
1.1 Schematic diagram of E. coli with multiple flagella. It is typically
rod-shaped and is about 2 m long and 1 m in diameter. (Eisen-
bach, 1994) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Swimming behavior of E. coli or Salmonella in the absence of
chemotactant. It executes a random walk composed of runs and
tumbles with zero drift. . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Swimming behavior of E. coli or Salmonella in the presence of
chemotactant. It executes a biased random walk composed of runs
and tumbles with significant drift. . . . . . . . . . . . . . . . . . . . 5
1.4 Experimental data using fluorescence correlation spectroscopy: Re-
sponse of individual motors as a function of CheY-P concentration.
(Cluzel et al., 2000) Each data point describes a simultaneous mea-
surement of the motor bias and the CheY-P concentration in an
individual bacterium. CW bias was computed by analyzing video
recordings for at least 1 min. . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Chemotaxis signaling pathways in E. coli (A) in the absence of
chemoattractant, and (B) in the presence of chemoattractant. The
dotted line represents the reactions with reduced rate in response to
ligand binding to the receptor. It can be noted that on adaptation
the pathway returns to steady state as shown in (A). . . . . . . . . 7
2.1 Linear calibration curve: Fluorescence intensity with varying con-
centration of 2-NBDG. . . . . . . . . . . . . . . . . . . . . . . . . . 22
vi
-
2.2 Micro-capillary experimental setup for establishing glucose gradi-
ents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3 Measured fluorescence intensity in the capillary for 10 min (solid
line) and 540 min (dashed line) after the start of the experiment.
It can be noted that the gradient was stable for more than 9 hours. 23
2.4 (A) Agar plate containing 0.3 % agar and motility buffer (absence
of glucose) with 107 cells introduced at the center of the plate.The image was taken after 6 hours of incubation. It can be noted
that no ring was formed. (B) Agar plate containing 0.3 % agar,
motility buffer and glucose (5 mM), with 107 cells introducedat the center of the plate. The image was taken after 6 hours of
incubation. Clearly, the cells have migrated forming a distinct ring. 23
2.5 Micro-capillary experimental setup for establishing MeAsp/serine
gradients. x = 0 is located at the end of the pellet with a ligand
concentration, L0. The experimental values of L0 and the estab-
lished gradients (G) are given in Table 2.1 and these conditions
were used in the model simulations. . . . . . . . . . . . . . . . . . . 26
2.6 (A) A magnified view of the micro-capillary set-up (B) Stable linear
gradients; Measured concentration profiles for two gradients: t =
1.5 min (black line) and t = 5 min (blue line) forG = 0.016 M/m,
t = 1.5 min (red line), t = 5 min (green line) and t =30 min (pink
line) for G = 0.16 M/m respectively. . . . . . . . . . . . . . . . . 27
3.1 Mean square displacements (MSD standard error) about themean value (from Gaussian fit) as a function of time. MSD was
obtained using data from all experiments including those with and
without gradients. The solid line represents the linearity obtained
for a diffusive regime. The diffusive regime occurs beyond 3.8 sand is shown by a dotted arrow. . . . . . . . . . . . . . . . . . . . 39
vii
-
3.2 Measured drift velocity (u0 standard error) as a function of dis-tance in the absence of aspartate using normal buffer (), 100 Maspartate in normal buffer (N), and 10000 M aspartate in normal
buffer (). The solid line represents the average trend for the three
experiments. The empty circle () represents the drift velocity whenthe buffer is saturated with oxygen in the absence of aspartate. . . . 40
3.3 Average run speeds ( standard error) as a function of aspartategradients at: 500 m (+), 1000 m (), and 1500 m (). Thesolid line represents the average value for the entire data set. The
arrow indicates the average run speed of 18 m/s obtained in the
absence of MeAsp. . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4 Drift velocity (uasp standard error) as a function of distance forvarious gradients of aspartate (G) and initial ligand concentration
(L0) (A) G = 0.016 M/m and L0 = 16 M; (B) G = 0.08 M/m
and L0 = 80 M; (C) G = 0.16 M/m and L0 = 160 M; (D)
G = 1.6 M/m and L0 = 1600 M. represents data from ex-periments and solid line represents model prediction. . . . . . . . . 42
3.5 (A) Drift velocity obtained through simulation as a function of as-
partate gradients at four different spatial locations: dotted line in
gray at 100 m, dotted line in black at 500 m, dashed line at
1500 m and solid line at 3000 m, respectively (B) Normalized
CW bias with time for four different gradients: solid line for G =
0.016 M/m and L0 = 16 M, dashed line for G = 0.08 M/m
and L0 = 80 M, dotted line in black for G = 0.16 M/m and L0
= 160 M and dotted line in gray for G = 1.6 M/m and L0 =
1600 M (C) Normalized active receptor concentration with time
for four different gradients: solid line for G = 0.016 M/m and
L0 = 16 M, dashed line for G = 0.08 M/m and L0 = 80 M,
dotted line in black for G = 0.16 M/m and L0 = 160 M and
dotted line in gray for G = 1.6 M/m and L0 = 1600 M. . . . . . 44
viii
-
3.6 (A) Drift velocity obtained through simulation as a function of lig-
and dissociation constant for a gradient of 0.06 M/m at four
different spatial locations: dotted line in gray at 100 m, dotted
line in black at 500 m, dashed line at 1500 m and solid line
at 3000 m, respectively (B) Drift velocity as a function of ratio
of methylation and demethylation rate constants for a gradient of
0.06 M/m at four different spatial locations: dotted line in gray
at 100 m, dotted line in black at 500 m, dashed line at 1500 m
and solid line at 3000 m, respectively (C) Drift velocity as a func-
tion of gradient with varying Hill coefficients (n) at x = 500 m
(D) Variation of Hill coefficient (n) with rotational diffusivity (Dr)
to obtain a drift velocity of 1 m/s at x = 500 m, for a gradient
of 0.06 M/m. The arrow indicates the experimentally observed
rotational diffusivity, Dr = 0.32 rad2/s, corresponding to n = 50. . . 46
3.7 (A) Measured drift velocity ( standard error) as a function of thespatial gradient of the logarithmic aspartate concentration: G =
0.016 M/m (), G = 0.08 M/m (), G = 0.16 M/m (N)and G = 1.6 M/m () (B) Average value of the drift velocity
( standard error) at each d(lnL)/dx with location. The solid linerepresents a linear curve fit while the dashed line represents the
trend observed by (Kalinin et al., 2009) . . . . . . . . . . . . . . . . 49
ix
-
4.1 (A) Mean square displacements about the mean value as a function
of time for varying serine concentrations: 0 M (), 250 M (),1000 M (N), 5000 M (), 24000 M () and 240,000 M() (B) The translational diffusivity for various serine concentra-tions. The values of the diffusivities were obtained from rotational
diffusivity and average run speed () and compared with those ob-tained using MSD (). (C) Average run speeds ( standard error)as a function of distance in the absence of serine (), and in thepresence of serine, 5000 M () when the buffer is saturated withair. Average run speeds in the absence of serine (), and in thepresence of serine, 5000 M (N) when the buffer is saturated with
oxygen. (D) Average run speeds ( standard error) as a functionof serine concentration at 500 m. N indicates the average run
speed of 18.65 0.62 m/s obtained in the absence of serine whensaturated with air. . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2 (A) Rotational diffusivity as a function of serine concentration at
500 m. N indicates the rotational diffusivity 0.380.05 rad2/sobtained in the absence of serine when saturated with air. (B)
Measured drift velocity ( standard error) as a function of distancein the absence of serine (), and in the presence of serine, 5000 M() when the buffer is saturated with air. Measured drift velocityin the absence of serine (), and in the presence of serine, 5000 M(N) when the buffer is saturated with oxygen (C) Measured drift
velocity ( standard error) as a function of distance for varyingserine concentration when saturated with air: 0 M (), 50 M(), 250 M () and 5000 M (). . . . . . . . . . . . . . . . . 64
x
-
4.3 The measured drift velocity ( standard error) as a function ofdistance for various gradients of serine (G) and initial ligand con-
centration (L0); (A) G = 0.0016 M/m and L0 = 1.6 M () (B)G = 0.016 M/m and L0 = 16 M () (C) G = 0.16 M/mand L0 = 160 M () (D) G = 1.6 M/m and L0 = 1600
M () (E) G = 16 M/m and L0 = 16000 M ( N) and (F)
G = 160 M/m and L0 = 160000 M (). . . . . . . . . . . . . . 67
4.4 (A) Drift velocity obtained through experiments and simulation as a
function of serine gradients at three different spatial locations: solid
line and at 500 m, dashed line and at 1000 m, dotted lineand at 1500 m respectively. (B) Drift velocity obtained throughexperiments and modified predicted values by addition of an average
drift velocity due to oxygen gradient and serine concentration as a
function of serine gradients at three different spatial locations: solid
line and at 500 m, dashed line and at 1000 m, dottedline and at 1500 m respectively. . . . . . . . . . . . . . . . . . . 68
4.5 (A) Drift velocity obtained through experiments as a function of
serine gradients at 500 m: solid line with represent our experi-mental data while the dotted line with represents that observedby Berg and Turner (1990). (B) Measured drift velocity ( stan-dard error) as a function of the spatial gradient of the logarithmic
serine concentration: G = 0.0016 M/m (), G = 0.016 M/m(), G = 0.16 M/m (), G = 1.6 M/m (), G = 16M/m () and G = 160 M/m (N) (C) Average value of thedrift velocity ( standard error) at each d(lnL)/dx with location.The solid line represents a linear curve fit. . . . . . . . . . . . . . . 70
xi
-
5.1 (A) The number of E. coli cells as a function of displacement mea-
sured over a time period of 3.3 s for uniform glucose concentra-
tion of 28 M in the capillary: Experimental data:, Gaussianfit:solid line (B) Gaussian fits for zero gradient: solid line, and
0.05 M/m, ...... . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 (A) Mean square displacement (variance from Gaussian fit) for all
experiments (B) Apparent diffusivity as a function of time for var-
ious glucose concentrations and gradients. . . . . . . . . . . . . . . 79
5.3 (A) Measured drift velocity for varying glucose gradients (B) Mea-
sured drift velocity in the absence of glucose. The data excludes the
contribution of non-chemotactic drift (C) Measured drift velocity
for varying glucose concentrations (D) Drift velocity as a function
of effective glucose gradients: for varying glucose concentrationexperiments in the absence of initial gradients (see Figure 5.3 (C)),
for initially established glucose gradients (see Figure 5.3 (A)),and .... for Hill equation obtained by fit. . . . . . . . . . . . . . . . 81
5.4 (A) Measured drift velocity as a function of the spatial gradient of
the logarithmic glucose concentration. The dotted line represents
the linear variation (see equation 5.2) with slope 1000 m2/s. (B)
Average run speeds as a function of effective glucose gradients:for varying glucose concentration experiments in the absence of ini-
tial gradients (Figure 5.3 (C)), for initially established glucosegradients (see Figure 5.3 (A)), for absence of glucose and ....for average value in the presence of glucose. . . . . . . . . . . . . . 83
xii
-
List of Tables
1.1 Mathematical models overview . . . . . . . . . . . . . . . . . . . . . 14
2.1 Gradients and ligand concentration at x = 0 obtained through ex-
periments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1 Comparison of chemotactic parameters in E. coli with those re-
ported in literature. (Berg and Brown, 1972; Liu and Papadopou-
los, 1996; Berg and Turner, 1990; Ahmed and Stocker, 2008; Kalinin
et al., 2009) These values were obtained by averaging ( standarddeviation) over all experiments. . . . . . . . . . . . . . . . . . . . . 38
3.2 Parameters used to simulate the model. . . . . . . . . . . . . . . . . 48
xiii
-
Chapter 1
Introduction
1.1 Background
Chemotaxis is a phenomenon in which a microorganism or multi-cellular organ-
isms are able to direct their movements in the presence of certain chemicals in
their environment. Cells capable of chemotaxis are bacteria, protozoa, amoeba,
cellular slime moulds, sperm and fibroblasts phagocytes. Chemotaxis is used by
organisms to find food by swimming towards the highest concentration of food
molecules, or to flee from poisons. Chemicals which are capable of eliciting such a
response from chemotactic cells are called Chemotactants. Chemotactants may act
as attractants, in which case the chemotactic cell will move towards them, thereby
exhibiting a positive chemotactic response. On the other hand, some chemicals act
as repellents, in which case the chemotactic cell will move away from them, thereby
exhibiting a negative chemotactic response. Examples of chemoattractants include
nutrients such as sugars, amino acids and small peptides, while chemorepellents
include antibiotics and noxious chemicals such as phenol.
Chemotaxis plays an indispensable role in our life from birth to death. During
embryonic process cells migrate to their target destinations to become components
of arms, legs, liver, heart, brain and other organs. In the brain, immature neurons
move from their birthplace to final targets where they make the right connections
and allow functions such as learning and memory. In the process of wound healing,
1
-
white blood cells migrate towards the site of infection to destroy the foreign organ-
ism. (Condliffe and Hawkins, 2000) In metastasis, cancer cells secrete their own
chemotactic stimulus to direct their migration towards lymphatic vessels using the
lymphatic system as a major route to spread throughout the body. (Carlos, 2001;
Wood et al., 2006; Shields et al., 2007) Cell migration is also important in bio-
film formation and bio-remediation of surface contaminants. (Duffy et al., 1997;
Pandey and Jain, 2002) In all these processes, how the cells sense the environment
and migrate to specific locations is not clear. Bacterial chemotaxis provides a
basic platform for learning how these processes work which in turn will help in
unraveling similar processes in more complex systems.
Escherichia coli is a model organism for the study of bacterial chemotaxis,
because its genetics is comparatively simple, is generally harmless, can be grown
quickly on a wide range of media and much of the biochemical pathway is known.
Thus, it has been used for experimental and theoretical studies of chemotaxis over
the last 40 years. Since the discovery of chemotaxis by Pfeffer and Englemann in
1880, many studies on bacterial chemotaxis have been reported in the literature.
However, the much needed impetus to the study of bacterial chemotaxis began in
the 1960s by Julius Adler who noted that bacteria used specific receptors to recog-
nize the chemicals. Since then, numerous studies have identified these receptors,
unraveled the workings of the receptors and the signaling pathway, recorded the
motion in detail, in the presence of various chemoattractants and, more recently,
tried to integrate this knowledge into detailed mathematical models to predict
the motion of E. coli in the presence of chemicals. Many of these studies will be
reviewed in the following pages of this chapter.
E. coli are extremely tiny creatures. They are rod-shaped, about 2 m long
and 1 m in diameter and are mostly motile. They can achieve motility via the
utilization of specialized structures known as flagella. These flagella allow the
bacteria to exhibit both positive and negative chemotactic responses. A single
bacterial cell may possess multiple flagella as shown in Figure 1.1. (Eisenbach,
1994)
2
-
Figure 1.1: Schematic diagram of E. coli with multiple flagella. It is typically
rod-shaped and is about 2 m long and 1 m in diameter. (Eisenbach, 1994)
Swimming in the absence of chemotactant: In the absence of chemo-
tactant, E. coli executes a random walk composed of runs and tumbles. Smooth
swimming in a straight line is called a run and an abrupt turning motion is called
a tumble as shown in Figure 1.2. A cell is propelled by a set of several helical
flagellar filaments that arise at random points on its sides and extend several body
lengths out into the external medium. Each filament is driven at its base by a
rotary motor embedded in the cell envelope. The energy for the motor comes from
proton motive force, i.e., by the flow of protons (hydrogen ions) from the outside
to the inside of the cell. During the runs, the filaments coalesce into a bundle
that pushes the cell forward. When viewed from behind the cell, the bundle ro-
tates counterclockwise (CCW), and, to balance the torque, the cell body rotates
clockwise (CW). Tumbles are initiated by CW motor rotation and during this the
reversed filaments come out of the bundle and go through a series of polymorphic
transformations from normal to semi-coiled and then to curly. (Berg, 2004) This
change in course defines the tumble interval. When the motor switches back to
CCW rotation, the filaments regain its normal conformation and rejoin the bun-
3
-
dle. Turner et al. (2000) noted that tumbling occurs only when 25% or more ofthe flagellar filament on a given cell reverse to clockwise direction.
Figure 1.2: Swimming behavior of E. coli or Salmonella in the absence of chemo-
tactant. It executes a random walk composed of runs and tumbles with zero drift.
Swimming in the presence of chemotactant: An increasing chemoat-
tractant concentration or a decreasing chemorepellent concentration decreases the
probability of CW rotation and, therefore, the probability of tumbles. Thus, the
final outcome is a random walk of the bacterial cell, biased towards the chemoat-
tractant or away from the chemorepellent as shown in Figure 1.3. Segall et al.
(1986) studied the impulsive chemotactant response of the cell and observed that
the cell compares the concentration over the past 1 s with that observed over
the previous 3 s and responds to the difference. This implies that the concen-
tration/gradient sensed by the bacteria is temporal in that the bacteria possess
a memory, which compares the past information with the present information to
make a decision. So the cell decides whether life is getting better or worse. If its
getting better, they continue in the same direction and if its getting worse, they
wont worry about it.
The regulation of this chemotaxis phenomena in the bacteria is achieved by a
network of interacting proteins. (Eisenbach, 1994) The basic mechanism in flagel-
lated bacteria involves a receptor mediated phosphorylation of a cytoplasmic pro-
tein CheY that binds to the flagellar motor and changes the frequency of tumbles.
There are essentially six cytoplasmic proteins viz., CheA, CheB, CheR, CheW,
4
-
Figure 1.3: Swimming behavior of E. coli or Salmonella in the presence of chemo-
tactant. It executes a biased random walk composed of runs and tumbles with
significant drift.
CheY and CheZ that are needed to process the sensory signals and transmit con-
trol signals to the flagellar motor. E. coli has five methyl-accepting chemotaxis
proteins (MCPs) (referred to as chemotaxis receptors) that mediate responses to
serine (Tsr), aspartate and maltose (Tar), ribose and galactose (Trg), and dipep-
tides (Tap). These MCPs are transmembrane proteins that span from the internal
to the external surface of membrane and are made of 550 amino acids. Of these
receptors, Aer is the most specific one that senses oxygen and energy levels of the
cell. The signaling domain of the MCP modulates the activity of the CheA to
elicit chemotactic response. The CheW protein couples CheA to MCPs to form a
complex. The CheA phosphorylates which in turn phosphorylates two other regu-
lator proteins, CheY and CheB. The phosphorylated CheY activates the flagellar
motor switch protein FliM (M) resulting in increased tumbling frequency. In vivo
experimental studies (Cluzel et al., 2000) have reported CW bias (the fraction of
time spent in CW rotation) as a function of CheY-P concentration (Yp) and is
shown in Figure 1.4. It is known that for a fixed value of the ligand concentration
there exists a fixed concentration of Yp which yields the corresponding CW bias.
This in turn decides the probability of a run or a tumble event. If CheY-P concen-
tration is very low, which means negligible CW value, cell will run and vise versa.
Also, the CheZ promotes the CheY dephosphorylation. The phosphorylated CheB
5
-
removes the methyl groups from MCPs, where as CheR continuously adds methyl
groups to MCPs. The level of methylation of MCPs decides the tumbling fre-
quency of the organism. (Falke et al., 1997) A schematic diagram of the signaling
pathways in E.coli is shown in Figure 1.5.
Figure 1.4: Experimental data using fluorescence correlation spectroscopy: Re-
sponse of individual motors as a function of CheY-P concentration. (Cluzel et al.,
2000) Each data point describes a simultaneous measurement of the motor bias
and the CheY-P concentration in an individual bacterium. CW bias was computed
by analyzing video recordings for at least 1 min.
When an attractant is added, it binds to the receptor (specific to that attrac-
tant) and the rate of autophosphorylation of CheA decreases. Consequently, the
rate of phosphorylation of CheY and CheB decrease causing a reduction in the ac-
tivity of FliM and decrease in the tumbling frequency. However, the CheR contin-
ues to methylate MCPs, thereby increasing the level of methylation of MCPs. This
progressively reduces the attractant binding affinity to the MCPs and increases
the rate of autophosphorylation of CheA. The phosphorylated CheY and CheB
return to their initial state. Thus, after the introduction of a chemoattractant, the
6
-
Figure 1.5: Chemotaxis signaling pathways in E. coli (A) in the absence of
chemoattractant, and (B) in the presence of chemoattractant. The dotted line
represents the reactions with reduced rate in response to ligand binding to the
receptor. It can be noted that on adaptation the pathway returns to steady state
as shown in (A).
7
-
tumbling frequency reduces for a short time, after which the tumbling frequency
increases to the preattractant level. The initial reduction in tumbling frequency
increases the run length and biases the motion of cell towards the attractant. On
the other hand, in the presence of a repellent, the rate of autophosphorylation
of CheA increases resulting in the increase in frequency of tumbles. (Eisenbach,
1994) Quantification of this chemotaxis signaling pathway requires construction
of mathematical models that describe the operation of the whole network.
Experimentally, the chemotactic behavior of E. coli can be quantified using
various parameters such as average run speed, clock-wise bias, drift velocity, cell
diffusivity, rotational diffusivity, etc. Of the four commonly reported parameters,
the first is the run speed that gives the average speed of the cells in between
consequent tumbles. The second is the clock-wise bias which gives the fraction
of time spent by the cells in tumble mode. Of the next two parameters, the
drift velocity measures the mean speed of cells while the translational diffusivity
characterizes its random motion. Note that the random motion is induced by the
collisions of cells with the solvent molecules as well as due to their own tumbling
and is calculated as the mean square distance traversed by the cells upon time.
A related parameter is the rotational diffusivity calculated as the mean square
angular deviation upon time. A wide range of experimental methods have been
developed so far to analyze these chemotactic parameters. (Eisenbach, 1994) They
include capillary (Adler, 1966a), swarming plate or ring forming (Wolfe and Berg,
1989), stopped flow diffusion chamber (Ford et al., 1991), micro-capillary (Liu
and Papadopoulos, 1996), diffusion gradient chamber (Widman et al., 1992) and
microfluidic assays (Berg and Turner, 1990), (Hanbin et al., 2003), (Ahmed and
Stocker, 2008), (Kalinin et al., 2009), all of which are reviewed in the next section.
Experimental methods for analyzing chemotaxis
Capillary assay: This was the first technique developed by Adler (Adler, 1966a) for
measuring bacterial chemotaxis. A capillary tube containing a chemoattractant-
free buffer is placed in a suspension of bacteria. The bacteria enter the cap-
8
-
illary and accumulate in it. The extent of this accumulation is a measure of
the motility of the bacteria. When the capillary contains a chemoattractant, a
gradient is developed by diffusion. The bacteria follow this gradient and accumu-
late in the capillary. For measuring negative chemotaxis, a capillary containing
chemorepellent-free buffer is immersed in a chemorepellent containing suspension
of bacteria. The bacteria escape from the chemorepellent into the capillary and
accumulate in it. The bacteria in the capillary can be counted by plating them
or by observing them under a microscope. The chemotaxis receptors become sat-
urated when the capillary contains very high concentrations of chemoattractant.
In this case the bacteria were unable to sense the gradient and a sharp drop was
observed in the number of bacteria accumulated in the capillary. This is the most
commonly used method for chemotaxis but the response depends on chemotactant
transport, metabolism, and growth of E. coli.
Swarming plate or Ring forming assay: Bacteria are placed at a certain spot
in a plate containing semisolid agar and a low concentration of a metabolizable
chemoattractant. The bacteria metabolize the chemoattractant and thereby pro-
duce a chemoattractant gradient. The bacteria use up the local supply of chemoat-
tractant and follow the induced gradient. Due to this, a continuous expanding ring
of dense bacteria is formed. This ring marks the boundary between the region that
has been depleted of chemoattractant and the region still containing the chemoat-
tractant. When the plate contains a number of chemoattractants, a number of
expanding rings are formed. Note that this method of quantifying chemotaxis is
restricted to metabolizable chemoattractants only (Wolfe and Berg, 1989). Also,
there is no control over the induced gradient and the final response depends on
both metabolism and growth.
Stopped-flow diffusion chamber assay: In the study by Ford et al. (1991), the
bacterial migration behavior to the gradients was measured using a stopped flow
diffusion chamber. In this assay, two bacterial suspensions differing in stimulant
concentrations were contacted by impinging the two flows. As long as there is
a flow through the chamber, there is no mixing between the two suspensions.
9
-
However, once the flow is stopped, a transient attractant concentration gradient is
created by diffusion and the variations in bacteria density can then be measured
by light scattering. This method can be used to quantify the bulk properties of
populations such as drift velocity and translational diffusivity.
Micro-capillary assay: The micro-capillary assay used by Liu and Papadopou-
los (1996) consisted of two reservoirs communicating through a long capillary of
50 m inner diameter. The linear concentration profile for the chemoattractant
was achieved by filling one of the reservoirs with motility buffer while the other
one was filled with the chemoattractant solution. It was assumed that after 24
hours, the concentration profile would attain the steady-state linear concentration
profile. Here, the single cell parameters such as run speed, run length, turn angles
etc were measured in the capillary with the help of a microscope.
Diffusion gradient chamber assay: Widman et al. (1992) used a diffusion gradi-
ent chamber assay that consists of a square arena bounded by a reservoir on each
side. Each reservoir was separated from the arena by a semi permeable mem-
brane. From the source and sink reservoirs, mediums containing attractants and
containing no attractant respectively were pumped using a syringe pump. The
system was allowed to establish the gradients partially after which the cells were
inoculated at the center point in the arena of the chamber. Glucose and oxygen
concentrations in the chamber were measured using micro-biosensor and the cell
growth and migration patterns were observed with time. In this study, however,
the concentration of the aspartate which was used as a chemoattractant was not
measured.
Microfluidic assays: Berg and Turner (1990) studied the chemotaxis in glass
capillary arrays. In this assay, two stirred chambers were separated by a micro-
channel plate comprising a fused array of capillary tubes. Here, the cells added to
first chamber migrate to the second chamber via multichannel plate. The density
of these cells were then calculated by measuring the scattering of light beam
incident from a laser diode. The study found that the flux of bacteria increased
on addition of an attractant into the second chamber compared to when there was
10
-
no attractant. A linear concentration profile of the chemoattractant was assumed
between the chambers. The drift velocity was calculated from the knowledge
of total flux for varying chemoattractant gradients. In this study, the motility
medium consisted of sodium lactate which is a carbon source for E. coli and
whose effect on chemotaxis was neglected. Later, Ahmed and Stocker (2008) used
a micro-channel connected at right angles to a side branch. Initially, a solution
of chemoattractant and fluorescein was injected into the main channel. After the
main channel was completely filled with the solution, the motility buffer containing
cells was injected into the side channel at a constant flow rate so that the cells
migrate to a constant gradient into the main channel established by diffusion. The
trajectories of the bacteria along with the concentration gradient were recorded.
The chemotactic parameter diffusivity was computed from the motion of a single
cell as well as population.
Recently, Kalinin et al. (2009) have used microfluidic assays to quantify the
chemotaxis. In this study, three parallel channels were patterned in an agarose
gel. A fluorescent solution initially flowed through the upper source channel which
was later replaced with the chemoattractant while a blank buffer flowed through
the lower sink channel. This established a linear chemical gradient in the central
channel because of the diffusion that takes place through the agarose gel from the
source channel to the sink channel. The cells were then introduced into the central
channel and their trajectories were recorded. Using the tracked positions, the
cell diffusivity and the chemotactic migration coefficient (CMC), i.e., the average
vertical position of all the cells tracked with respect to the central position of the
channel were calculated. Further, the study suggested that the cells respond to
the spatial gradient of the logarithmic attractant concentration. However, it is
important to note that the motility buffer used in this study contained lactic acid
which is a carbon source and whose influence on chemotaxis was ignored.
11
-
Mathematical studies on chemotaxis
A number of mathematical models have been developed to describe different as-
pects of chemotaxis at the level of a single cell (Levin et al., 1998; Novere and
Shimizu, 2001; Lipkow et al., 2005) in addition to descriptions of entire bacterial
populations (Keller and Segel, 1970, 1971; Alt, 1980; Rivero et al., 1989; Newman
and Grima, 2004). Most of the population studies used Keller and Segel model
(Keller and Segel, 1970) for analyzing chemotactic population behaviors, which
was basically developed for modeling the movement of slime molds. According to
this model, the flux of cells ~Jb will be given by,
~Jb = ~b+ b~c (1.1)
where b is the cell density, c is the chemical stimulus concentration, is the ran-
dom motility coefficient that measures the translational diffusivity of a population
of bacteria resulting from the random walk behavior, while is the chemotactic
sensitivity coefficient which represents the strength of the attraction of a popula-
tion of bacteria to a given chemical. In physical terms, the bacterial flux comprises
of two parts, namely, diffusion (or random motility) and chemotactic motion char-
acterized by drift velocity.
Lovely and Dahlquist (1975) were the first to relate the individual-cell observa-
tions to the random motility coefficient (), a macroscopic cell transport parameter
describing population-scale motility,
=v2
3(1 cos) (1.2)
where v is the bacterial swimming speed, is the run time and is the turn angle.
Recall that, E. coli chemotaxis relies on temporal sensing mechanism to detect
spatial gradients as they swim through them and this mechanism is governed by
the ability of an individual cell to compare the fraction of bound chemoreceptors
on its cell surface at various points in time. A change in the number of bound
12
-
chemoreceptors allows a cell to change its CW bias. Experimentally, Berg and
Brown (1972) found that, when cell is traveling in the direction of an increasing
spatial chemical gradient, it extends their run lengths to continue traveling in
that direction. The study suggested that, in the presence of a chemical attractant
the number of bound receptors on a cells surface increases with time resulting in
reduced tumbling frequency. To describe this behavior Rivero et al. (1989) used
the Keller and Segel model. At the population level, the chemotactic migration
of bacteria was described using the drift velocity (vc),
vc =2
3v tanh
(o2v
Kd(Kd + L)2
L
x
)(1.3)
where v is the run speed, o is the chemotactic sensitivity coefficient and L is the
attractant concentration. Kd, the attractant-receptor dissociation constant, is a
measure of the ability of a membrane-bound chemoreceptor on the cell surface
to detect a specific attractant. However, these population studies do not account
for changes in the intracellular pathway to variations in extracellular attractant
concentration.
Chemotaxis intracellular signaling pathway of E. coli provides a unique op-
portunity to identify and develop computational methods required to obtain a
quantitative understanding of the intracellular signaling pathways. Recent models
for chemotaxis incorporate the signaling pathway at molecular level and integrate
it with motor response to predict bacterial motion. Examples include AgentCell
(Emonet et al., 2005), E solo (Bray et al., 2007) and RapidCell (Vladimirov et al.,
2008). AgentCell simulates the complete signaling pathway stochastically in a
single cell and predicts the motion in a three-dimensional environment. Esolo
is a deterministic model that solves ordinary differential equations represent-
ing the signaling reactions in the pathway and predicts bacterial movement in
two-dimensional environment. RapidCell utilizes the Monod-Wyman-Changeux
(MWC) (Mello and Tu, 2005; Keymer et al., 2006) two-state receptor model for
mixed receptor clusters, incorporates the adaptation dynamics and connects the
CheY-P values to the cells tumble/run to predict the motion of E. coli in a
13
-
Table 1.1: Mathematical models overview
Model Description
Spiro et al. (1997) Used a three methylation state model and simulated the
response to temporal variation of aspartate.
Barkai and Leibler (1997) Model ensured that adaptation is robust and showed
that adaptation time is inversely proportional to
receptor-complex activity.
Levin et al. (1998) Receptor modification reactions catalyzed by CheR and
CheB incorporated in Bacterial ChemoTaxis (BCT)
model. Model explored the consequence of variation
in protein expression CheY-P and the phenomenon of
non-genetic individuality.
Morton-Firth et al. (1999) Stochastic simulation of bacterial chemotaxis
(StochSim): Here, the activity of receptor com-
plexes was determined by free-energy changes due to
both ligand binding and changes in methylation state.
Shimizu et al. (2001) This extended the model of Morton-Firth et al. (1999)
and allowed interactions between neighboring receptor
complexes arranged in a regular lattice according to the
free energy of a receptor complex determined by the
activity states of its immediate neighbors. Model simu-
lated with different lattice sizes and geometries to find
the effect of the coupling energy between neighboring
receptors on the signal to-noise ratio and gain.
Mello and Tu. (2003) Deterministic model used to determine the full set of
conditions under which the system achieves perfect
adaptation.
14
-
Rao et al. (2004) Used both Barkai and Leibler (1997) and Sourjik and
Berg (2002) models to compare the intracellular path-
ways of E. coli and B. subtilis. Both pathways shown
to be adaptive and robust.
Lipkow et al. (2005) Using the Smoldyn program, model simulated the diffu-
sive movement of individual CheY molecules and their
binding with receptor complexes, CheZ and FliM.
Emonet et al. (2005) AgentCell, an agent-based program that relates stochas-
tic intracellular processes to the behavior of individ-
ual cells and bacterial populations. Cells represented
as agents made up of chemotaxis proteins, motors and
flagella that can move through a three dimensional en-
vironment.
Bray et al. (2007) E solo model which uses ordinary differential equa-
tions of the signaling reactions in the pathway and gives
the graphical display of bacterial movement in two-
dimensional environment.
Vladimirov et al. (2008) RapidCell model uses the Monod-Wyman-Changeux
(MWC) model for mixed receptor clusters, adaptation
dynamics and a model of cell tumbling to give the mo-
tion of E solo in two-dimensional environment.
15
-
two-dimensional environment. It is only recently that Kalinin et al. (2009) using
MWC model, predicted the bacterial motion to varying chemoatttactant gradi-
ents and compared them with experimental results. The computational single cell
mathematical models used for bacterial chemotaxis are briefly listed in Table 1.1.
In addition to the chemical stimuli, microorganisms also sense other stimuli
such as light, temperature, electric field, etc. In all cases, the name of the response
includes a prefix that describes the stimulus. The suffix taxis means moving to-
wards or away from the stimulus. They include phototaxis movement directed by
light, thermotaxis by temperature changes, electrotaxis by electrical field, mag-
netotaxis by magnetic field, geotaxis by gravity, elasticotaxis by elastic force etc.
(Berg, 2004)
1.2 Motivation
The traditional techniques to characterize chemotaxis were the agarose gel as-
say (Wolfe and Berg, 1989) and the capillary assay (Adler, 1966a; Liu and Pa-
padopoulos, 1996) due to their simplicity. In both these methods, E. coli moves
up a gradient set by the consumption of the attractant and there is no control
over the induced gradients. More recent experiments have been able to establish
controlled gradients using microfluidic techniques (Kalinin et al., 2009) that al-
low the measurements of drift velocities over a wide range of gradients. However,
these techniques fail to capture temporal and spatial variation of the chemotactic
response of E. coli to spatial variations of chemoattractants. In parallel with the
experimental work, a number of extensive mathematical models have been pro-
posed for E. coli chemotaxis, but there exists no study which predicts the bacterial
motion along a gradient and compares them with experiments. Recall that the
motion of E. coli not only depends on the local ligand concentrations but also on
past history of concentration experienced by it. Further, the extent of influence
of oxygen and energy source on the motion is not clear. How the intracellular sig-
naling response reflects the extracellular response characterized by motion is still
16
-
an active research area. The understanding of the available intracellular signaling
pathway and absence of experimental and theoretical study which quantifies the
chemotaxis in both time and space in controlled gradients motivated us to carry
out the work presented in this thesis.
1.3 Objectives
The main objective of the current study is to quantify the response of microorgan-
isms to varying chemoattractant concentrations/gradients using a micro-capillary.
This would involve developing mathematical models that describe the chemotaxis
pathway in a chemotactic cell and to relate the kinetics to the motion of cell in
the presence of chemoattractants. The predictions of the model will be compared
with experiments where the motion of cell in the presence of chemoattractants will
be tracked.
The overall objectives are summarized below :
Develop a novel technique to quantify the chemotaxis in E.coli in the pres-ence of chemoattractants in controlled environment at the phenotypic level
in a suitable device.
Develop a mathematical model to quantify the motion of E.coli using avail-able signaling pathway and connect it to the motion.
Validate the model results with experimental data to get better insight ofthe chemotaxis mechanism.
1.4 Organization
The work presented in this thesis is organized into six chapters describing the
research methodology and results, followed by conclusions and recommendation
for future work. In chapter 1, an overview of bacterial chemotaxis phenomena is
presented. Chapter 2 describes the various experimental and theoretical methods
17
-
used in the present work. Here, the methods developed for modeling the motion
of E. coli and experimental techniques used for establishing various controlled
gradients have been described. In chapter 3, we describe the mathematical model
along with experimental validation of chemotaxis under controlled gradients of
methyl-aspartate in E. coli while chapter 4 deals with a similar study in the
presence of a metabolizable attractant, namely, L-serine. Chapter 5 discusses the
phenomena of chemotaxis in the presence of glucose via the phosphotransferase
(PTS) pathway. Finally, the overall conclusions and directions for future work are
presented in chapter 6.
18
-
Chapter 2
Materials and Methods
2.1 Experimental Protocols
Microorganism
Recall that E.coli has five methyl-accepting chemotaxis proteins (MCPs) that
mediates metabolism-independent chemotactic responses in E.coli. The high-
abundance chemoreceptors of this family (Tsr and Tar) mediate responses to serine
and aspartate, respectively. The low-abundance chemoreceptors (Tap and Trg)
are present at only 10 percent of the concentration of Tsr and while Tap mediates
response to dipeptides, Trg senses galactose and ribose by means of their respective
periplasmic binding proteins. (Li and Hazelbauer, 2004) In this study, we used
Escherichia coli K-12 (MTCC 1302, IMTECH Chandigarh, India) strain and
chemoreceptors characterization was done by Chromous Biotech Pvt Ltd. The
detailed report can be found at the end of this chapter and shows that the strain
used throughout our study has four MCPs (Tsr, Tar, Tap and Aer) excluding Trg
receptor. The culture was revived over monthly intervals.
Rectangular micro-capillaries
Micro-capillaries were obtained from Arte Glass Associates Co., Ltd, Japan. The
dimensions of the capillaries are 5 cm (L) 1000 m (W) 100 m (H).
19
-
Chemicals
Luria Bertani (LB) broth was obtained from Hi-media company. KH2PO4, K2HPO4,
(NH4)2SO4, MgS04. 7H20, Ehylenediaminetetraacetic acid (EDTA), Polyvinyl
pyrrolidine (PVP), D-glucose (glucose), -methyl-DL-aspartate (MeAsp) and L-
serine (serine) were obtained from Sigma-Aldrich company. 2-(N-(7-nitro-benz-2-
oxa-1,3-diazol-4-yl)amino)-2-deoxy-glucose (2-NBDG) was obtained from Invitro-
gen Corporation. Tryptone (Difco) and Bacto agar (Difco) were obtained from
BD Biosciences company.
Media
The motility buffer (MB) contained (/l of distilled water) K2HPO4, 11.2 g; KH2PO4,
4.8 g; (NH4)2SO4, 2 g; MgS04. 7H20, 0.25 g; PVP, 1 g; and EDTA, 0.029 g. (Adler,
1973) Luria-Bertani (LB) broth contained LB (/l of distilled water) 25 g. Tryptone
medium contained (/l of distilled water) tryptone, 10 g and Nacl, 5 g. Glucose
medium contained (/l of distilled water) glucose, 4 g and the salts (/l distilled wa-
ter): K2HPO4, 11.2 g; KH2PO4, 4.8 g; (NH4)2SO4, 2 g; and MgS04. 7H20, 0.25g.
Different concentrations of chemotaxis medium was prepared by adding different
amounts of chemoattractant to the motility buffer under sterilized conditions.
Growth conditions
The growth conditions of the bacteria are critical to the success of the chemotaxis
experiments. In glucose experiments, the bacteria were grown as per the following
procedure: One loop full of culture from the slant was inoculated into LB media
and allowed to grow for 9 hours in the exponential growth phase. The incubation
was always carried out at 37oC and 240 rpm. After the culture was grown for
9 hours, 1 ml of the culture sample was transferred to the sterilized LB medium
and left to further grow for 6 hours (early exponential phase). Next, 1 ml of the
culture broth from LB medium was transferred to the glucose medium and allowed
to grow for 6 hours (exponential phase). To ensure that the cells are adapted to
20
-
glucose, 10 ml of sample was transferred to glucose medium and grown again for
4 hours (early exponential phase). The biomass was separated by taking 50 ml of
the culture into sterilized tubes and centrifuged at 4000 rpm for 10 minutes. The
supernatant was decanted and the settled pellet was gently re-suspended in 10 ml
motility buffer. In order to provide energy to the bacteria, 28 M of D-glucose was
added to the motility buffer in all experiments. The above procedure was repeated
three times before introducing the cells into the capillary. High levels of bacterial
motility were observed on viewing the cells under an optical microscope. Finally
for the chemotaxis measurements, cells were introduced gently by touching the
pellet with the mouth of the capillary. Then, concentration of cells in the capillary
were calculated by diluting them in the buffer followed by plating. Approximately
106-107 bacteria/ml were taken into the capillary in all experiments.
We used similar procedure for MeAsp experiments and serine experiments with
some minor protocol modifications. After the culture was grown for 9 hours, 1
ml of the culture broth from LB medium was transferred to tryptone medium
and allowed to grow for 6 hours (exponential phase). To ensure that the cells
are adapted to tryptone medium, 10 ml of sample was transferred to tryptone
medium and grown again for 4 hours (early exponential phase). After this, the
washing procedure is similar except that the motility buffer does not contain any
chemoattractant in the gradient experiments. However, we added chemoattractant
to the motility buffer for observing the motion of E. coli for obtaining uniform
concentrations (zero gradient) of the attractant.
Calibration of intensity for 2-NBDG measurements
The micro-capillaries were first sterilized and were marked with graduations spaced
at 0.5 cm along its length. The concentration gradients were first established using
different concentrations of 2-NBDG (fluorescent glucose) solutions using motility
buffer. In order to calibrate the intensity of 2-NBDG solutions, a 5 cm plug of
2-NBDG solution was drawn into the capillary and the ends were sealed with
wax. Using a microscope with a 4X (NA 0.13) objective lens, the fluorescence
21
-
intensity of 2-NBDG was measured at the center of the capillary. The fluorescence
intensity was found to vary linearly with 2-NBDG concentration up to 1000 M.
The calibration chart is shown in Figure 2.1.
Figure 2.1: Linear calibration curve: Fluorescence intensity with varying concen-
tration of 2-NBDG.
Establishment of 2-NBDG gradients for glucose experiments
Figure 2.2: Micro-capillary experimental setup for establishing glucose gradients.
Experiments were performed to establish different gradients of 2-NBDG. A 2.5
cm plug containing a fixed concentration of 2-NBDG was drawn into the capillary
followed by a 2.5 cm plug of a lower concentration of 2-NBDG. Approximately 107
22
-
Figure 2.3: Measured fluorescence intensity in the capillary for 10 min (solid line)
and 540 min (dashed line) after the start of the experiment. It can be noted that
the gradient was stable for more than 9 hours.
Figure 2.4: (A) Agar plate containing 0.3 % agar and motility buffer (absence of
glucose) with 107 cells introduced at the center of the plate. The image wastaken after 6 hours of incubation. It can be noted that no ring was formed. (B)
Agar plate containing 0.3 % agar, motility buffer and glucose (5 mM), with 107
cells introduced at the center of the plate. The image was taken after 6 hours of
incubation. Clearly, the cells have migrated forming a distinct ring.
23
-
bacteria /ml were taken into the capillary by contacting the pellet (as described
earlier) into the mouth of the capillary. As before, the ends of the capillary
were sealed with wax. A schematic diagram of the micro-capillary experimental
setup is shown in Figure 2.2. Using a microscope with a 4X (NA 0.13) objective
lens, the fluorescence intensity of 2-NBDG was measured along the length of the
capillary as a function of time. The results are shown in Figure 2.3. It is noticed
that the intensity profiles attains a steady state within 10 minutes and negligible
change in the concentration profile was observed up to 540 minutes. These profiles
were robust and easily reproducible and were not affected by the cell movements.
Growth experiments with 2-NBDG demonstrated that there is no change in OD
for 4 hours (results are not shown) indicating that 2-NBDG was not metabolized.
Also, experiments were performed in agar plate assays to check the chemotactic
response of the current strain to glucose. Clear rings were formed within 6 hours
indicating that the E. coli strain lacking Trg shows normal chemotactic behavior
towards glucose (Figure 2.4). Further, for the chemotaxis experiments, normal
D-glucose was used instead of 2-NBDG as the cells did not respond to 2-NBDG.
Establishment of 2-NBDG gradients for MeAsp and Serine
experiments
Initially, a 4.5 cm liquid plug containing a fixed concentration of 2-NBDG was
drawn in the capillary followed by about 0.5 cm plug of a motility buffer without
2-NBDG. Approximately 107 bacteria/ml were taken into the capillary by con-
tacting the pellet (as described earlier) with the mouth of the capillary. Then, the
capillary ends were sealed with wax. A schematic diagram of the micro-capillary
experimental setup is shown in Figure 2.5. Using a microscope with an 4X (nu-
merical aperture = 0.13 ) objective lens, the fluorescence intensity of 2-NBDG was
measured over 0 < x < 1500 m as a function of time shown in Figure 2.6(A)
and the results are shown in Figure 2.6 (B). It is noticed that within the first
1.5 min, the intensity profiles attain the steady state and there is a negligible
change in the concentration profile up to 30 min. These profiles were robust and
24
-
easily reproducible and were effected neither by the cell movements nor by the
consumption of 2-NBDG by this strain. Further, for the E. coli chemotaxis to
MeAsp/serine, MeAsp/serine was used instead of 2-NBDG. It is assumed that the
gradients obtained with MeAsp/serine is identical to 2-NBDG, since the molecular
weight of both are approximately similar and so the diffusivity in water would also
be approximately the same. The experimental values of L0 and the established
gradients (G) are given in Table 2.1. These conditions were also used in the model
simulation.
Table 2.1: Gradients and ligand concentration at x = 0 obtained through experi-
ments.
S.No Concentration (L0, M) at x = 0 cm Gradient (G, M/m)
1 1.6 0.0016
2 16 0.016
3 160 0.16
4 1600 1.6
5 16000 16
4 160000 160
Quantification of chemotaxis
Image analysis was used to quantify the movements of E.coli. This involves the
analysis of moving objects in image sequences. In this section, specifications of
the optical microscope, image processing and procedure for calculating the cell
movements are discussed.
Specifications of optical microscope: IX71 Inverted Microscope (Olympus, Japan)
was adopted as the optical microscope. Images were taken using Evolution VF
cooled monochrome camera (Media Cybernetics, Japan) in Bright-field (BF) il-
lumination mode with magnification of 40X objective lens (numerical aperture =
0.75).
Image processing procedure: Image-Pro Plus 6.0 image analysis program was
used to locate E.coli in each frame and follow its motion in subsequent frames.
25
-
Figure 2.5: Micro-capillary experimental setup for establishing MeAsp/serine gra-
dients. x = 0 is located at the end of the pellet with a ligand concentration, L0.
The experimental values of L0 and the established gradients (G) are given in Table
2.1 and these conditions were used in the model simulations.
26
-
Figure 2.6: (A) A magnified view of the micro-capillary set-up (B) Stable linear
gradients; Measured concentration profiles for two gradients: t = 1.5 min (black
line) and t = 5 min (blue line) for G = 0.016 M/m, t = 1.5 min (red line), t = 5
min (green line) and t =30 min (pink line) for G = 0.16 M/m respectively.
This program uses a single stack TIFF image which contains 500 sequential TIFF
images taken at an interval of 0.11 s. In every experiment two stack images were
recorded at 500, 1000 and 1500 m. Note that the image captured using 40X
magnifications encompassed a physical area measuring 160 120 m2.Procedure for calculating the cell movement: To minimize errors in finding the
cell movements, the following two assumptions were made. First, the cells moving
close to each other or overlapping cell movements were neglected. Secondly, the
cells not in the field of view for the entire time series (minimum 1.1 s) were
neglected.
Using Matlab c, each stack image was thresholded so as to clearly distinguishcells from the image background. Then, with the help of the auto tracking option,
the movement of cells along the gradient direction (x) was tracked in a sequence of
images. The raw data containing spatial location of each cell as a function of time
was exported to an Excel c file. The procedure for calculating the movement ofcell over a period of time was straight forward. Starting from an initial position
in the first frame, the displacements between two consequent frames 0.11 s apart
27
-
were determined. The tracking data with time was collected from six identical
experiments repeated on different days to capture the variability, if any.
2.2 Modeling Methods
Model for the intracellular pathway
A two state receptor model proposed by Barkai and Leibler (1997) was used to
simulate the intracellular signaling pathway. The model considers the methyl
accepting chemotaxis proteins (MCPs), CheA and CheW, as a single entity (re-
ceptor complex) and assumes that these receptor complexes, whose concentration
is denoted by T , exist in either an active (TA) or an inactive (T I) state. Let
Ti represent the concentration of receptor complexes with i residues methylated
and i(L) denote the probability that the receptor complex Ti is active when the
concentration of chemoattractant is L. The receptor complex can be in one of five
methylation states with i = 0, 1, 2, 3 or 4 methyl groups. The total concentration
of active receptors is given by,
TA =40
i(L)Ti, (2.1)
while the total concentration of inactive receptors is given by,
T I =
40
(1 i(L))Ti. (2.2)
The binding kinetic equation for active receptor complex is given by,
TAF + L [TAL] (2.3)
The total active receptor complex concentration TAT is given by,
TAT TAF + [T
AL] (2.4)
where TAF is free (non-ligand bound) active receptor complex concentration and
[TAL] is the ligand bound active receptor complex concentration respectively. The
28
-
fraction of free active receptor complex concentration from the above equation is
given by,
TAFTAT
=KL
KL + L(2.5)
where KL is the ligand dissociation constant. Similarly, the fraction of ligand
bound receptor complex concentration is given by,
[TAL]
TAT=
L
KL + L(2.6)
The total probability of the receptor complex being in active state is the sum of
the probabilities of the ligand bound and non-ligand bound receptors being in
active state and is given by,
i(L) =Li L
KL + L+
0iKLKL + L
(2.7)
where the parameters are assigned the following numerical values, L0 = 0, L1 =
0, L2 = 0.1, L3 = 0.5,
L4 = 1,
00 = 0,
01 = 0.1,
02 = 0.5,
03 = 0.75 and
04
= 1. These values are taken from Morton-Firth et al. (1999) and are estimated
from the free energy states of methylation and the ligand occupancy of receptor
complex for MeAsp. The corresponding phosphorylation rate equations with the
corresponding rate constants (Emonent and Cluzel, 2008) are given by,
dApdt
= 23.5(TA)A 100(Ap)Y 10(Ap)B (2.8)
dYpdt
= 100(AP )Y 30(Yp) (2.9)
dBpdt
= 10(Ap)B (Bp) (2.10)
Here, A, AP , Y , Yp, B and Bp represent, respectively, the concentrations of CheA,
phosphorylated CheA, CheY, phosphorylated CheY, CheB and phosphorylated
CheB. Li and Hazelbauer (2004) have measured these chemotaxis protein concen-
trations for wild type and are given by, A + Ap = 5.3 M, B + Bp = 0.28 M,
Y + Yp = 9.7 M and CheR (R) = 0.16 M. The total receptor concentration
29
-
(Tar+Tsr), T0 + T1 + T2 + T3 + T4 = 17 M and R = 0.16 M reflect the
reported (Sourjik and Berg, 2004) findings that both Tsr and Tar participate in
sensing aspartate.
Barkai and Leibler (1997) model assumes that CheR (R) binds to the inac-
tive receptors (T I) and the phosphorylated CheB (Bp) binds to the active recep-
tors (TA). Assuming that, the methylation and demethylation reactions follows
Michaelis - Menten kinetics, the rate of demethylation and methylation is given
by, respectively,
rB =kbBp
KB + TA(2.11)
rR =krR
KR + T I(2.12)
where, kb = 0.6 s1 and kR = 0.75 s
1 are the rate constants and KB = 0.54 M
and KR = 0.39 M are the Michaelis constants (Emonent and Cluzel, 2008) for
receptor demethylation and methylation, respectively.
The rate of methylation is proportional to the concentration of inactive recep-
tors (1-i(L))Ti, and the rate of demethylation is proportional to the concentra-
tion of active receptors i(L) Ti. For the receptor Ti, the rate of demethylation
is rB i(L) Ti and the rate of methylation is rB (1 i(L)) Ti, the mass balanceequations for the corresponding receptor can be given by,
dT0dt
= rR(1 0(L))T0 + rB1(L)T1 (2.13)
dT1dt
= rR(1 1(L))T1 + rB2(L)T2 + rR(1 0(L))T0 rB1(L)T1 (2.14)
dT2dt
= rR(1 2(L))T2 + rB3(L)T3 + rR(1 1(L))T1 rB2(L)T2 (2.15)
dT3dt
= rR(1 3(L))T3 + rB4(L)T4 + rR(1 2(L))T2 rB3(L)T3 (2.16)
dT4dt
= rR(1 3(L))T3 rB4(L)T4 (2.17)
Using Matlab c, we solved simultaneously the steady state phosphorylationreaction equations and the mass balance equations for the receptors.
30
-
E. coli motion model
The ligand concentration decides the protein CheY-P concentration which is the
output of the signaling pathway. In vivo experimental studies (Cluzel et al.,
2000) using fluorescence correlation spectroscopy have reported CW and switching
frequency (F ) as a function of CheY-P concentration (Yp). Recall that the CW is
the fraction of time spent in clock-wise rotation of the flagella while the switching
frequency is the number of times the motor switched its direction of rotation per
unit time. Further, Cluzel et al. (2000) used the Hill equation to describe the CW
bias as a function of Yp,
CW =Y np
(Y np ) + (Kn)
(2.18)
where n is the Hill coefficient and K is the half saturation constant. Cluzel et al.
(2000) reported n = 10 and K = 3.1 M for their single cell experiments in-
dicating a highly ultra-sensitive response (Figure 1.4). In our experiments, the
experimentally measured CW bias was used to set the value of K so as to yield
a steady state value of Yp. It was also observed that the switching frequency F
qualitatively behaves as F dCW/d Yp. (Cluzel et al., 2000) So, the inverse ofthe switching frequency gives the time period containing one change of direction
of rotation. The time spent in a single tumble mode is given by,
ttum =CW
F(2.19)
while the time spent in a single run mode, also referred to as the run time is,
trun =1 CW
F(2.20)
Assuming a Poisson process, (Vladimirov et al., 2008) we can now obtain the
probability that E. coli will switch from tumble to run mode and vice versa. If
the E. coli is in run mode, then the probability that it will switch to tumble mode
in time dt is Pruntum = dt/trun while 1 (dt/trun) is the probability that it willcontinue in the run mode after each time step dt. Similarly, Ptumrun = dt/ttum.
31
-
In the simulations, the E. coli starts from its initial position, x = 0, y = 0
with the ligand concentration varying linearly with x, L(x) = L0 + Gx, where G
is the gradient. At t = 0, the E. coli is made to run for time duration dt along
x, after which Pruntum is determined. A number between 0 and 1 is randomly
generated using a Matlab function that has a uniform probability in that range.
If the number obtained is less than Pruntum, then the E. coli is made to tumble
else it continues to run. In case of tumble, the position of the cell is held constant
and a new direction of the motion is chosen from a gamma distribution of turn
angles that is obtained independently from our experiments. The distribution
yielded a mean turn angle of 71o 1.1 that was close to that observed by Bergand Brown (1972). In case of run, the cell is made to move at a constant run speed
but at an angle chosen from a normal distribution with mean zero angle (about
its previous angle) and a variance2Dr dt, where Dr is the rotational diffusivity
and was determined independently from our experiments. Once the E. coli has
tumbled or run, the above procedure is repeated at the new location for t = t+dt
using the new local ligand concentration. The simulation was obtained for 1000
cells and the mean properties were calculated for comparison with experiments.
This model was used to simulate the motion of E. coli for varying -methyl-DL-
aspartate (MeAsp) and L-serine (serine) concentrations/gradients. It is important
to note that, MeAsp is a non-metabolized chemoattractant whereas serine is a
metabolized (a less source of carbon) chemoattractant. Further, the model results
are validated with the experimental results and are discussed in the next chapters.
The following three pages presents a concise report on the presence of different
chemotaxis receptors in the E. coli strain used in our experiments.
32
-
Chapter 3
Mathematical modeling and
Experimental validation of
chemotaxis under controlled
gradients of methyl-aspartate in
Escherichia coli
3.1 Introduction
In this chapter, we describe a novel technique to measure the chemoattractant
gradients with respect to space and time in the absence of fluid flow. Unlike the
previous studies, we track the motion of each cell and obtain the local drift velocity
as a function of time and space for a broad range of concentrations and gradients
of -methyl-DL-aspartate (MeAsp, a non-metabolized chemoattractant). We used
an existing two state model (Barkai and Leibler, 1997) for the intracellular path-
way and incorporate the extra-cellular influence such as ligand concentration and
Brownian motion to predict the response for the experimental conditions. The
statistics of single cell motions such as cell velocity, run angle, turn angle, tum-
bling frequency, rotational diffusion, drift velocity and diffusivity are obtained
36
-
independently. The predicted motion matched with the observation only when
the response of the intracellular pathway was highly ultra-sensitive. The detailed
comparison of the predictions with the observed behavior revealed bounds on the
parameters describing the intracellular pathways. Further, our studies also show
that oxygen plays a key role in the chemotaxis response and the response to a
ligand cannot be analyzed in isolation to oxygen.
Chemotaxis signaling model
A two state receptor model proposed by Barkai and Leibler (1997) was used to
simulate the intracellular signaling pathway. The model considers the methyl ac-
cepting chemotaxis proteins (MCPs), CheA and CheW, as a single entity (receptor
complex) and assumes that these receptor complexes, whose concentration is de-
noted by T , exist in either an active (TA) or an inactive (T I) state. Further, a
receptor is assumed to exist in one of the five methylation state. For a fixed value of
the ligand concentration, the model solution yields the active and inactive receptor
concentrations for each of the methylation states along with the concentrations of
chemotaxis proteins in the phosphorylated and dephosphorylated states. Finally,
the concentration of CheY-P decides the motion of E. coli. The model details of
the intracellular signaling pathway and motion details are given in Materials and
Methods (Chapter 2).
3.2 Results
Gradients of fluorescent glucose (2-(N-(7-nitro-benz-2-oxa-1,3-diazol-4-yl)amino)-
2-deoxy-glucose, 2-NBDG) were established in the capillary and their variation
with time were recorded. It was found that the gradient was established in the
first 1.5 minutes after which the variation was negligible for almost 30 min (see Ma-
terials and Methods). At x = 0, the concentration reaches a constant finite value
within this short time beyond which (x > 0) a stable linear gradient was achieved.
A wide range of gradients could be established by varying the concentration of the
37
-
ligand in the liquid plug. It can be noted that the gradient experiments with E.
coli were conducted for about 15 min after establishing the gradient. This implies
that the stable gradients could be maintained during the period of the experiment
(see Figure 2.6).
The experiments were conducted with various gradients of MeAsp and with
uniform concentrations in the absence of gradients, and the mean square displace-
ment (MSD) of over 1000 cells from these experiments are presented as a function
of time in Figure 3.1. Interestingly, all the measured points from various exper-
iments collapse to give a unique trend, wherein the MSD increases quadratically
up to t 3.8 s after which the increase is linear. The MSD was further usedto determine the translational diffusivity, ((x )2/2t) at large times (t > 3.8s) yielding a value of 110 26.22 m2/s, irrespective of the external conditions.Here, is the mean displacement in time t obtained using Gaussian distribution.
The measured angular displacement data was fit to a Gaussian distribution and
was used to calculate the rotational diffusivity, given by Dr = 2/2t where is the angular displacement and the angular brackets represent ensemble average.
Table 3.1 presents the various parameters related to E. coli motion obtained from
our experiments and the measured values are close to those reported in previous
studies.
Table 3.1: Comparison of chemotactic parameters in E. coli with those reported in
literature. (Berg and Brown, 1972; Liu and Papadopoulos, 1996; Berg and Turner,
1990; Ahmed and Stocker, 2008; Kalinin et al., 2009) These values were obtained
by averaging ( standard deviation) over all experiments.
Description Present study Reported
Average run speed (v, m/s) 18.6 2.2 14.2 3.4Mean tumble angle (degrees) 71.80 1.1 62 26
Rotational diffusivity (Dr, rad2/s) 0.32 0.07 0.06 - 0.28
Steady state CW bias (CWss) 0.16 0.05 0.1Cell diffusivity (D, m2/s) 110 26.22 125 - 360
38
-
Figure 3.1: Mean square displacements (MSD standard error) about the meanvalue (from Gaussian fit) as a function of time. MSD was obtained using data
from all experiments including those with and without gradients. The solid line
represents the linearity obtained for a diffusive regime. The diffusive regime occurs
beyond 3.8 s and is shown by a dotted arrow.
Next, the measured drift velocity, in the presence and the absence of MeAsp,
was recorded at three different locations along the capillary and is presented in Fig-
ure 3.2. In the absence of MeAsp, independent experiments were conducted with
only motility buffer that was saturated with air and with pure oxygen. Figure 3.2
shows that in the absence of MeAsp the cells show significant drift velocities (u0)
initially and the velocity drops steadily beyond 1000 m from the entrance. The
high concentration of the cells in the pellet would have rendered the interstitial
fluid devoid of oxygen. Consequently, the cells respond to an oxygen gradient
established when the cells are brought in contact with the motility buffer in the
capillary. However, a significant decrease in drift velocity with increasing distance
was observed beyond 5000 m and was probably due to a lack of endogenous en-
ergy source since the cells become non-motile. Further, the run speeds did not
vary up to 2500 m, suggesting that the endogenous energy source was not limit-
ing for x < 2500 m. Figure 3.2 also includes the drift velocity when the buffer is
saturated with oxygen. The velocities were higher by about 1.5 times everywhere
than that for the normal case although the cells again became non-motile for dis-
tances beyond 5000 m. The higher velocities could be attributed to the higher
39
-
gradients of oxygen sensed by the cells.
All experiments in the presence of MeAsp were performed at normal levels of
oxygen (saturated with air). Experiments were conducted to first quantify the
chemotactic response in the absence of gradient to uniform MeAsp concentrations
of 100 (N) and 10000 M () and the results are also presented in Figure 3.2. The
measured drift velocities for both the concentrations were similar to that observed
in the absence of MeAsp. This clearly indicates that cells respond solely to oxygen
in the absence of MeAsp gradients. To study the chemotactic behavior of E. coli,
various gradients of MeAsp were established in the capillary. Figure 3.3 presents
the measured average run speed as a function of the gradient of MeAsp at three
different locations in the capillary. The plot also includes measurements in the
absence of MeAsp (indicated by the arrow). It can be noted that the average run
speeds are in the range of 16-21 m/s irrespective of the gradient or the location.
An average value of 18 m/s was used in the model simulations.
Figure 3.2: Measured drift velocity (u0 standard error) as a function of distancein the absence of aspartate using normal buffer (), 100 M aspartate in normalbuffer (N), and 10000 M aspartate in normal buffer (). The solid line represents
the average trend for the three experiments. The empty circle () represents thedrift velocity when the buffer is saturated with oxygen in the absence of aspartate.
The measured drift velocities contain the influence of both oxygen and MeAsp
gradients. In order to separate the influence of oxygen gradient on the total drift
velocity, we subtracted (uo) from measured drift velocity values assuming alge-
40
-
Figure 3.3: Average run speeds ( standard error) as a function of aspartategradients at: 500 m (+), 1000 m (), and 1500 m (). The solid line representsthe average value for the entire data set. The arrow indicates the average run speed
of 18 m/s obtained in the absence of MeAsp.
braic addition of the responses. (Strauss et al., 1995) Thus, the drift velocity due
to MeAsp alone is given by, uasp = u uo. Note that this procedure not onlyeliminates the effect of oxygen but also the non-chemotactic drift induced by the
no flux condition on the plug side. The measured drift velocities at fixed locations
in the capillary for four different gradients with their corresponding initial con-
centrations are shown in Figure 3.4. For the lowest gradient (G = 0.016 M/m),
the response was minimal indicating that the E. coli had completely adapted
even at x = 500 m. However, on increasing the gradient to G = 0.08 M/m, a
drift velocity of 1.4 m/s was measured at 500 m which monotonically decreased
to 0.8 m/s at 1500 m demonstrating adaptation. On increasing the gradient
further, a similar profile but with higher initial drift velocities was observed. How-
ever, at the highest gradient tested (G = 1.6 M/m), the measured velocities at
500 and 1000 m matched with that for G = 0.08 M/m, but was significantly
lower at 1500 m indicating early adaptation. The model was simulated for the
above mentioned gradients and the predictions are compared with experiments in
Figure 3.4. Note that the simulations do not incorporate the effects of oxygen.
Further, simulations were run in the absence of attractants to determine the non-
chemotactic drift velocity as a function of distance. The non-chemotactic drift
41
-
velocity decreases gradually from 0.35 m/s at x = 0 to a negligible value at x =
1500 m (results not shown). These values were subtracted from the values ob-
tained for gradients to compare with experimental data, where the effects of both
oxygen and non-chemotactic drift have been eliminated. The spatial variation of
the predicted drift velocities matched reasonably well with the measurements for
all four gradients. Specifically, the model captures the increase in drift velocities
with gradient close to the plug and the subsequent monotonic decrease with dis-
tance. Further, the measured drift velocity indicates that the cells adapt faster at
higher gradients, again in agreement with experiments.
Figure 3.4: Drift velocity (uasp standard error) as a function of distance forvarious gradients of aspartate (G) and initial ligand concentration (L0) (A) G =
0.016 M/m and L0 = 16 M; (B) G = 0.08 M/m and L0 = 80 M; (C) G =
0.16 M/m and L0 = 160 M; (D) G = 1.6 M/m and L0 = 1600 M. represents data from experiments and solid line represents model prediction.
42
-
The model was used to obtain the drift velocities at different spatial locations
along the capillary as a function of varying gradients (see Figure 3.5 (A)). Below
a gradient of 0.01 M/m, the drift velocity is negligible and does not vary with
distance indicating no response. With increasing gradients, the drift velocity ini-
tially increases at all four locations reaching a maximum after which the velocity
drops. A maximum drift velocity of 1.8 m/s at 100 m was observed close to a
gradient of 0.1 M/m. Note that the peak value of drift velocity also decreases
with spatial distance. A negligible drift velocity is obtained for gradients greater
than 10 M/m but for locations greater than 3000 m due to adaptation.
The model also predicted the normalized CW bias with time for measured
gradients and the corresponding normalized value (with respect to the steady
state value) of the fraction of active receptors, TA/TAss (Figure 3.5 (B) and (C),
respectively). Note that TA decides the concentration of CheY-P and therefore the
CW. Perfect adaptation to a new environment requires that the TA and therefore
the CW return to their steady state values after the initial transients due to change
in the environment. At the lowest gradient tested in our experiments, TA drops
by a small value after which it regains the steady state value suggesting quick
adaptation. Consequently, this small change in TA leads to a step drop of CW
bias drops at x = 0 after which it attains the steady state value within a short time
(200 s). This indicates that the run length is higher than the steady state value
only for a short distance beyond which there is negligible response. At the higher
gradient of 0.08 M/m, the fraction of active receptors decrease further leading
to a slower recovery of the normalized CW bias to its steady state value. The plot
indicates that the cells do not completely adapt even after 2000 s. On increasing
the gradient further, a similar initial response is observed although the normalized
CW bias recovers faster to the steady state value. For very high gradients (G =
1.6 M/m), the bias takes a negligible value up to 500 s indicating very large run
lengths. However, the recovery to the steady state value is achieved within 1500
s, indicating faster adaptation at higher gradients. Note that we were unable to
measure t