Single Cell Gene Expression in Bacillus subtilis: An Investigation
into the Molecular Basis of Culture Heterogeneity
by
John David Chung
B.S. Chemical Engineering
University of California, Santa Barbara
(1986)
Submitted to the Department of Chemical Engineering in Partial
Fulfillment of the Requirements for the Degree of
DOCTOR OF PHILOSOPHY
in Chemical Engineering
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 1994
© Massachusetts Institute of Technology, 1994, All Rights Reserved
lT-\
4
X
Signature of Author:
Department of Chemical E ieering
B
by:
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Certified
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y
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-
Jan
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-
/ Gregory Stephanopoulos
Professor of Chemical Engineering
Thesis Supervisor
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Accepted by:
25, 1994
1\' A
.
Robert E. Cohen
Professor of Chemical Engineering
Chairman, Committee for Graduate Students
Science
MASSACHUSETTS
INSTITUTE
OFTFCHNOLOGY
JUN 06 1994
LIBRARIES
Single Cell Gene Expression in Bacilw subtilie An Investigation
into the Molecular Basis of Culture Heterogeneity
by
John David Chung
Submitted to the Department of Chemical Engineering on January 25, 1994 in partial
fulfillment for the Degree of Doctor of Philosophy in Chemical Engineering
ABSTRACT
An experimental and theoretical investigation was undertaken in order to better
understand the molecular basis of physiologicalheterogeneity. Experimental studies were
confined to differentiating cultures of Bacillus subtilis and involved the analysis of early
developmental gene expression using single cell measurements and conventional genetic
techniques. Quantitative studies of adaptive gene expression were performed to demonstrate
the feasibility of the experimentally derived conclusions and to provide a framework in which
to analyze adaptive cellular responses. It is concluded that: 1) threshold mechanisms control
initiation into spore development via processes interacting with the phosphorelay (Burbulys
et a. 1991),2) the intracellular concentration of phospho-SpoOAdetermines a transcriptional
state of a B. subtilis cell and 3) that threshold mechanisms provide the cell with a stable
means of initiating adaptive cellular responses.
Experimental support for these conclusions was obtained from flow cytometry-based
measurements of gene expression using lacZ fusions to loci known to be directly or indirectly
activated by the phospho-SpoOAtranscription factor. Culture average measurements of
phospho-SpoOA controlled gene expression were found to reflect the behavior of two cell
populations. One population exhibited high expression while the other exhibited little or
none.
The size of the high expressing population was found to correlate with the final
sporulation frequency. Mutations that reduce the intracellular level of phospho-SpoOA
resulted in a redistribution of the relative sizes of these populations with fewer cells residing
in the high expressing population and also a reduction in the sporulation frequency. These
results indicate that the cell regulates the level of phospho-SpoOAvia processes interacting
with the phosphorelay and that the level of phospho-SpoOAdefines a transcriptional state
of the cell.
Theoretical support for these conclusionswas obtained through the identification of
a prevalent biological control structure that allows threshold mechanisms to exist. So long
as the intracellular concentration of the transcriptional activator responsible for initiating
adaptive gene expression is regulated by a positive feedback loop, a non-linear generation
rate, of the cooperative type, and a concentration dependent removal rate, threshold
mechanisms are possible. The fact that this control structure can be found in a variety of
biological systems including those associated with phage development, nutritional adaptation
and spore formation suggests that it represents a conserved mechanism used by cells to cope
with changing environmental conditions.
Thesis Supervisor: Dr. Gregory Stephanopoulos
Title: Professor of Chemical Engineering
2
Acknowledgements
One of the principal reasons behind coming to M.I.T. was to take advantage
of the opportunities available at a premier research institution by expanding my
horizons in a number of scientific disciplines with the organizing center being cell
biology. The overall strength of the faculty at M.I.T. and the breadth of research
being performed has not been disappointing.
While such a purpose can lengthen
ones tenure and is in many ways contrary to the focus needed to complete the
doctoral program, such an opportunity arises only once in ones life. I leave M.I.T.
having accomplished, in general, all that was sought. However, this would not have
been possible without the support of a number of people. While they might all seem
to contribute in different ways, I have learned that putting up with me can sometimes
be an endeavor.
I would like to first thank my advisor, Greg Stephanopoulos, for finding the
right combination of support, guidance, freedom and of course pressure, needed to
bring this work to completion. While there were undoubtedly tense moments, they
can fortunately be looked upon as enriching experiences and perhaps well needed to
persevere at a place like M.I.T. I am confident that this relationship has better
prepared me for the real world.
I am indebted to the assistance provided by the Cell Sorter Facility at M.I.T.,
particularly Stewart Conner, who not only was a great colleague, but also a supportive
and encouraging friend particularly during the low point of my tenure. Much thanks
also goes to Glen Paradis who picked up where Stewart left off and greatly assisted
me during crunch time. Without the assistance of these two, this work would have
never been completed. Their competence, support and friendship will not be
forgotten.
I would also like to thank Professor Alan Grossman for his advise and insight.
If not for him, "my wheels might still be spinning." Working with him and Keith
Ireton, previously one of his grad students, I have had a great opportunity to see top
notch biologists in action. I would also like to thank my undergraduate UROP, Kirk
Noda, for his assistance during those long experiments. He allowed me to realized
how valuable the undergraduates can be to the research environment at M.I.T. Much
thanks also goes to the other members of my thesis committee; Daniel Wang, Charlie
Cooney, Arnold Demain, and Abraham Sonenshein. I can only hope that someday
I will be able to exhibit the patience that was required of them during several of my
"progress" reports. While it was often difficult to juggle between the disparate
perspectives of the scientists and the engineers on my committee, valuable lessons
were obtained through these interactions. In particular, all of them taught me the
importance of presentation and the bottom line.
Finally, I would like to thank the members of the 66-257Stephanopoulos lab,
both past and present, for their support. Sung Park; thank you for your
encouragement during a critical point in this work. Thanks also goes to Joe Vallino
and Bob Kiss, for demonstrating that graduation was possible, and to Grace Colon,
Catherine Shaw and Roy Kawamura for providing listening ears.
3
Table of Contents
Abstra.................................
...................
Aknowledgements
...................................
2
3
I Introduction
1.1 Present Interest in Species of Bacillus .......................
1.2 Problems Associated with Use of Bacillus ....................
1.3Hypothesis...............................
1.4 Objective
............
...........................................
8
9
10
1.6 Significance ..........................................
11
11
13
1.7 Thesis Organization ....................................
14
1.5 Approach ...............................
2 Background
2.1 Physiological Heterogeneity
.............
..............................
18
2.2 The Developmental Program of Spore Formation in B. subtilis ....
2.2.1 Signal Transduction via the Phosphorelay ..............
2.2.2 Transcriptional Activation via SpoOA-P ...............
2.2.3 Establishing Late Sporulation Gene Expression .........
2.3 Mechanisms Leading to Physiological Diversity ................
2.3.1 The Threshold Theory ............................
2.3.2 The Continuum Theory ...........................
2.3.3 Common Ground ...............................
20
20
21
23
24
24
26
27
3 Development of Flow Cytometry-Based Techniques for Studying Gene
Expression in Single Cells of a subtili
3.1 Introduction ................
3.1.1 Flow Cytometry .......
3.1.2 Bacterial Flow Cytometry
3.2 Problem Statement ...........
3.2.1 Signal Strength .......
3.2.2 Bacterial Diversity .....
..........................
36
.........................
..........................
..........................
39
41
41
..........................
........ ....... 43
..........................
3.3 Approach ..................
3.4 Summary of Staining Conditions . ..........................
3.4.1 C,-FDG ............
..........................
3.4.2 PKH26 .............
..........................
3.5 Description of Molecular Probes . ..........................
3.5.1 f-Galactosidase Fluorogetaic Substrates ...............
3.5.2 The Second Fluorochrome : PKH26 ..................
3.6 The Internal PKH26 Negative Conitrol......................
.............................
3.6.1 Tracking Background .............................
4
37
44
45
46
47
48
48
50
51
51..4
51
........
3.6.2 Fluorescence Compensation ........................
3.6.3 Controlled Mixtures ............................
3.7 Staining Cells with 5-Dodecanoylaminofluorescein
53
55
di-p-D-galactopyranoside
................................
55
3.7.1 Approach ....................................
3.7.2 Thermal Deactivation of p-galactosidase ...............
3.7.3 Constant Temperature Staining .....................
3.7.4 The Two-Step Temperature-Time
55
Pulse Matrix .........
3.7.5 Controlled Mixtures ..............................
56
57
58
59
3.8 Staining Cells with 5-Octanoylaminofluorescein
di-f-D-galactopyranoside
................................
3.8.1 Approach .....................................
3.8.2 C8 -FDG is Optimal ..............................
3.8.3 Staining Time ..................................
3.8.4 Heterogeneous Expression in Stationary Phase Samples ...
3.8.5 Controlled Mixtures ..............................
61
61
61
63
63
65
3.8.6 Correlation Between Fluorescence and Average Enzyme
Activity
......................................
66
66
3.9 Discussion ...........................................
4 Characterization of Stationary Phase Cultures of R subtils
4.1 Introduction ...................................
4.2 Stationary Phase Protein Expression .................
4.3 Single Cell Gene Expression: spoVG-lacZ .............
4.4 spoVG-lacZ Cell Sorting ..........................
96
97
98
101
.... .tions
.
4.5 Single Cell Gene Expression: spoIIA-lacZ .............
103
4.6 Correlation Between Spores and the Expressing Cell Populiations .. 104
4.7 Discussion ....................................
106
5 Genetic Analysis of Threshold Phenomena
5.1Introduction
........
....
...............
.......
119
.......
120
.......
.......
.......
................. .......
123
123
123
124
5.4 kinA Null Mutations: Population Demographics ........
......
5.4.1 f-Galactosidase Expressing Populations ........
......
5.4.2 Spores .................................
.......
5.5 kinA Null Mutations: Distribution of spo Gene Products .. ......
5.5.1 Fluorescence Distributions ..................
......
124
5.2 Stationary Phase Signal Transduction and Gene Expression
5.3 kinA Null Mutations: Culture Average Behavior ........
5.3.1 spoVG-lacZ.............................
5.3.2 spoIIG-lacZand spoIIA-lacZ ................
5.3.3spoIID-lacZ
.....
.......
5.5.2 spolID-JacZ Cell Sorting ...................
5.6 Observations on spoIIA-lacZ Gene Expression .........
5.7 The Effects of spoOK Mutations ....................
5
.......
.......
.......
124
126
127
127
128
129
129
5.8 Discussion ...........................................
131
5.8.1 Threshold Activation of Sporulation ..................
131
5.8.2 Multiple Thresholds ..............................
132
5.8.3 Implications of a Threshold Mechanism ...............
134
6 Quantitative Analysis of Threshold Phenomena
6.1 Introduction .....................
.....................
6.2 Mathematical Conceptualization ...........................
161
163
6.3 Phage A
163
...........................................
6.3.1 Background ....................................
163
6.3.2 Characteristics of Transcriptional Regulation ...........
6.3.3 The Rate Law for CI Generation ....................
165
166
6.3.4 The Single Cell Model ............................
169
6.3.5 Steady State or Balanced Growth Solutions ............
171
6.3.6 Initiating the Switch ..............................
6.3.7 The Dynamics Associated with the Transition ...........
6.3.8 Static versus Dynamic Thresholds ...................
174
175
177
6.4 The E. coli lac Operon ..................................
178
6.4.1 Background ....................................
178
6.4.2 Characteristics of the lac Operon .........
6.4.3 Derivation of the Rate Law ........................
...........
6.4.4 The Single Cell Model ............................
179
180
183
6.4.5 The Steady States ...............................
6.4.6 The Hysteresis or Maintenance Phenomenon ...........
6.5 Sporulation by B. subtilis ...................
.............
6.5.1 The Requirements for Threshold Behavior .............
6.5.2 The Threshold Requirements are Satisfied .............
6.5.3 Threshold Model Describing Sporulation ..............
185
186
189
189
191
193
6.5.4 kinA Mutations .................................
6.5.5 spoOK Mutations ................................
6.6 Appendix: Fitting Experimental Observations .................
6.6.1 Determination of A ................
..............
196
197
199
199
6.6.2 Generation of the Hysteresis Curve ..................
199
7 Summary and Concusions
7.1 Summary ...........................................
7.1.1 Single Cell Measurements .........................
222
222
7.1.2 Experimental Analysis of Gene Expression .............
7.1.3 Quantitative Analysis of Gene Expression .............
7.2 Conclusions ..........................................
8 Recommendations and Future Research Areas
8.1 Biological Studies ......................................
8.1.1 Signals Inputs versus Signal Amplification
6
223
224
226
.............
227
227
8.1.2 Adaptive Cellular Processes in Other Biological Systems ...
8.1.3 Biological Control Systems Theory ...................
8.2 Single Cell Measurements ................................
8.2.1 Single Cell Analysis: Fluorescence Microscopy ..........
8.2.2 The Future: Strain Improvement via Flow Cytometry .....
9
aterials and Methods
9.1 Strains, Maintenance and Cultivation Conditions ...............
228
228
229
229
230
231
....................................231
9.1.1Strains ....
9.1.2 Routine Stratin Maintenance and Storage ..............
9.1.3 Cultivation
9.2 Analytical Technique s...................................
9.2.1 Specific f-G; alactosidase Activity (ONPG) .............
9.2.2 Relative f-Gralactosidase Activity (MUG) ..............
231
....................................
232
233
233
234
....................................236
9.2.3 Spores ....
..................................
236
9.2.4 Cell Number rs
....................................237
9.3 Cell Staining ......
9.3.1 PKH26 Staix iing .................................
237
....................................
238
9.3.2 C 12 -FDG ..
....................................
239
9.3.3 C8 -FDG ..
....................................240
9.4 Flow Cytometry ...
240
..........
9.4.1 The Flow Cy(tometer
...................
241
9.4.2 Cell Sorting ....................................
242
9.4.3 Data Acquis ition and Analysis ......................
9.5 Theoretical Computattions ................................
10 References...................
243
248
7
Chapter 1: Introduction
1.1 Present Interest in Species of BaciEs
Species of the genus Bacillus have a long history of industrial and academic
use. For centuries, Bacillus subtilis (natto) has been used for the production of
itohiki-natto, a fermented soybean foodstuff popular in Japan (Maruo and Yoshikawa
1989). Species of Bacillus have also been used for the production of a number of
important products including amylases, proteases, nucleases, amino acids, nucleotides
and antibiotics (Schaeffer 1969, Preist 1977, Maruo and Yoshikawa 1989). More
recently, with the arrival of the biotechnology industry, the secretory apparatus of
Bacillus has attracted much interest in these organisms as possible recombinant hosts.
A number of heterologous proteins, both prokaryotic and eukaryotic, have been
successfully expressed and secreted in species of Bacillus (Gray and Chang 1981,
Palva et al. 1982, 1983, Honjo et al. 1985, 1986A, 1986B, 1987, Meyer and Fiechter
1985, Ulmanen et al 1985,Yamane et al. 1985,Nicaud et al. 1986,Schein et al. 1986,
Nakayama et al. 1987, 1988, Chang 1987, Saunders et al. 1987). While Escherichia
coli continues to be the most widely used recombinant host, advances in our
understanding of Bacillus genetics and physiology should make this genus an
attractive alternative to be considered in the future.
In addition to its long time industrial use, the ability of species of Bacillus to
initiate spore formation, a primitive form of cellular differentiation,
has also
generated a great deal of academic interest in this organism. The ease with which
8
species of Bacillus, particularly B. subtilis, can be genetically manipulated
has
provided scientists with an amenable system in which to study the complex processes
regulating developmental gene expression. Over the past few decades, an enormous
amount of research has been directed at identifying and understanding how
environmental and physiological factors influence the process of spore formation.
Such work has led to great insights into the molecular mechanisms responsible for
initiating adaptive cellular changes in response to changing environmental conditions.
Furthermore,
in the quest for such knowledge, powerful genetic tools have been
developed for studying this species, many of which might be applicable to other
bacterial species (Errington 1986, Harwood and Cutting 1990, Sandman et al 1987,
Yoshikawa and Sueoka 1963, Spizizen 1958, Zuber and Losick 1983).
1.2 Problems Associated With the Use of Bacillus
Despite significant increases in our understanding of the molecular processes
that control growth and protein expression processes in single cels of B. subtilis,
explanations for the behavior often exhibited by culturesremains elusive. Cultures
of Bacillus have been found to exhibit a rich variety of dynamic behavior. Long term
oscillations,multiple steady states, genetic instabilities and uninterpretable transience
have all been found to be associated with cultures of Bacillus (Humphrey et al. 1966,
Heineken and O'Conner 1972, Fencl and Pazlarova 1982, Koizumi and Aiba 1989,
this laboratory). In an effort to explain such behavior, a number of theoretical
investigations have been undertaken (Humphrey et al. 1966,Heineken and O'Conner
9
1972, Koizumi and Aiba 1989), typically without much success. The inability of such
studies to explain even qualitative observations suggests that cultures of Bacillus are
much more complicated than often acknowledged. Clearly such behavior must reflect
complex biological processes acting on the single cell and population levels.
Past analysis have failed to account for the possibility that cultures of B.
subtiis can be physiologicallyheterogeneous. Due to the ability of cells of B. subtilis
to enter the developmental pathway of spore formation, the culture demographics
might be anticipated to be more complex than those found in other bacterial systems
(figure 1.1). Since only a fraction of the cell population eventually develops into
spores, some eight to twelve hours after development is induced by starvation, late
stationary phase cultures are observably heterogeneous. This observation introduces
the possibility that the heterogeneity known to exist late in the culture is but a
manifestation of heterogeneity that exists earlier during the stationary phase.
Therefore, the proper analysis of gene expression will depend on if and when
heterogeneity is established.
L3 Hypothsis
The central hypothesis behind this work is that culture heterogeneity and its
effect on culture behavior are not sufficientlyunderstood. Since the observed culture
behavior could "...represent the events occurring within the individual cells or some
average of a heterogeneous population" (Novick and Weiner 1957),failure to account
for culture heterogeneity in the analysis of data might act to obscure the real
10
behavior of cells within the culture. This might even lead to erroneous conclusions.
A corollary of this hypothesis is that traditional approaches used to describe
microbial culture dynamics may not be applicable. Hence, physiological heterogeneity
could provide a plausible explanation for the major discrepancies that exist between
experimental and theoretical studies. In contrast to homogeneous cultures, the
behavior of cultures of Bacillus must now be addressed with respect to the different
cell types that constitute the population. It appears that while much has been
identified about the molecular mechanisms that control cell development and gene
expression, the consequences of these results, and their ramifications on the
demographic makeup of the culture, have not been fully appreciated.
.4 Objective
The principal objective of this work was to experimentally and theoretically
investigate how early stationary phase events influenced the decision made by cells
of B.subtilis to initiate sporulation. It was of particular interest to elucidate the
degree to which active or threshold mechanisms influenced this decision. A natural
consequence of this investigation would be a better appreciation of the effects, if any,
that culture heterogeneity have on the observed culture average behavior.
1.5 Approach
The
investigative
approach
adopted
is
summarized
in
figure
1.2.
Experimentally, the goal was to investigate how some of the earliest processes
11
involved in stationary phase signal transduction and early gene activation in B. subtilis
influenced the cell's ability to acquire the terminally differentiated state. Since one
of the earliest events associated with spore formation is the activation, via
phosphorylation, of the developmental transcription factor encoded by spoOA
(Burbulys et al. 1991, Grossman 1991), the pattern of SpoOA controlled gene
expression was used to characterize early development. Gene expression was then
studied in cells containing well characterized mutations in the signal transduction
genes, kinA (Antoniewski et al. 1989, Perego et aL 1989, Burbulys et al 1991) and
spoOK(Perego et al. 1991,Rudner et al 1991). Because kinA and spoOK mutations
reduce the intracellular level of phospho-SpoOA, cells carrying such mutations
provided the means of studying how reduced levels of phospho-SpoOAaffect early
developmental gene expression and spore formation. To account for the possibility
that not all cells expressed SpoOA controlled genes, a flow cytometry based technique
suitable for studying gene expression on the single cell level was developed.
This
allowed data representing events occurring at the single cell level to be gathered as
opposed to data that might reflect an average taken over a heterogeneous
population.
Theoretical studies focused on developing mathematical models aimed at
incorporating system non-linearities that predicted the existence of multiple
physiological states.
All models were derived from first principles in a manner
consistent with what is known about gene regulation. The consistency of the derived
models, with reported observations, was then checked through simulations using
12
parameters reported in the literature. Since very little quantitative information on
the sporulation system can be found in the literature, a detailed analysis of the
molecular processes responsible for decision making was performed on two well
characterized, qualitatively and quantitatively, genetic systems; cells of Eschenichia
coli infected with phage A and the E. coli lac operon (Ackers et al. 1982, Ptashne
1987, Barkley and Bourgeois 1980). Under the appropriate conditions, heterogeneity
of transcriptional states has been found in both systems (Bailone et al. 1979, Novick
and Wiener 1957).
The principles derived from this analysis were then used to
develop a model aimed at describing the decision making processes involved in the
initiation of spore development. The resulting model is shown to be consistent with
the experimental results presented in this thesis and throughout the literature.
16 Significanoe
Since many proteins of academic and industrial significance are produced by
Bacillus during the early stationary phase, when culture heterogeneity might exist,
information obtained from this study has fundamental and applied significance.
Under heterogeneous culture conditions, the possibility that not all cells express the
protein of interest exists. If it is desired to optimize the production of a particular
protein, one would like to know this information. Not only would such information
be valuable in its own right, but it would also lead to a more accurate
characterization of the culture. Traditional definitions used to characterize culture
performance, such as the specific productivity or the yield, could be replaced by more
13
precise terms reflecting the heterogeneous nature of the culture.
Process
manipulations that affect culture productivity could be assessed based on the effects
they have on the constituent cell subpopulations. In particular, changes in culture
productivity might result from a distribution of the culture demographics or from
variations in the level of expression within individual cells. Information on the culture
demographics might also provide a simple means of estimating the upper limits on
culture productivity under a given set of cultivation conditions.
Proper analysis of
culture data will inevitably rely on the ability to address or account for culture
heterogeneity. Pioneers in the area of molecular and cell biology realized the
problems associated with heterogeneity and went to great length to either account for
(Benzer 1953, Novick and Wiener 1957) or eliminate such factors (Herzenberg 1959).
1.7 Thesi Oranization
In the followingchapters these ideas are further developed. Chapter 2 serves
to introduce the concept of physiologicalheterogeneity. Also discussed is the present
state of knowledge, which is evolvingat this very moment, surrounding the molecular
processes known to be involved in regulating stationary phase gene expression in
cultures of B. subti&s. In chapter 3, the development of the tools needed to study
culture heterogeneity is described. The flow cytometry-based procedures used to
study gene expression in single cells of B. subtilis, using lacZ fusions, are described.
In chapters 4 and 5, these techniques are applied to study early stationary phase gene
expression in differentiating cultures of B. subtilis.
14
In chapter 6, physiological
heterogeneity is studied quantitatively. It will be shown that threshold mechanisms
control adaptive gene expression in a number of biological systems.
Chapter 7
summarizes the work performed and restates the principal conclusions. Chapter 8
suggests areas of future research. Chapter 9 is devoted to the materials and methods
used throughout this work. Chapter 10 presents the literature cited throughout this
thesis. Following each chapter, the tables and figures cited are presented.
15
Growth Phase
Stationary Phase
8
-- 40
Homogeneous
Homogeneous or Heterogeneous ?
Homogeneous
Heterogeneous
a - (a
0-
Growth Cycle
uu
-- 40
Development Time
Figum1.1 Bacillus subtilis culture heterogeneity. During exponential growth, the culture
may be characterized by a single cell type. Upon entrance into the stationary phase, some
cells develop into spores (top right) and some do not (bottom). This introduces the
possibility that early stationary phase cultures are comprised of more than one cell type.
16
Investigative Approach
Biology of Bacillus
Follow phospho-SpoOAcontrolled
developmental gene expression.
Perturb early gene expression
with knA and spoOK mutations.
Account for Heterogeneity
Mathematical Modeling
Identify mechanisms allowing
multiple transcriptional states.
I
Apply principles to develop a
sporulatlon Initiation model.
I
I
I
Figue 12 Approach taken to study the molecular basis of physiologicalheterogeneity.
17
Chapter 2: Background
21 Physiological Heterogeneity
How genetically identical cells adopt different developmental
fates is a
question that biologists have focused on for quite some time (Horvitz and Herskowitz
1992). This question applies to relatively simple developmental systems like the lysislysogeny decision of phage lambda, as well as to development
in complex
multicellular organisms. In addition, even in organisms that do not undergo elaborate
development, there can be physiologicalheterogeneity within a culture of genetically
identical cells.
With E. coli under some conditions, it is possible to maintain a
subpopulation of cells that are induced for expression of the lac operon, while a
distinct subpopulation remains uninduced (Novick and Weiner 1957).
In principle, physiological diversity could be produced in two different ways
(figure 2.1). In the simplest case, different cell types might be generated at the time
of cell division. The sister cells that result from this asymmetric cell division would
then be expected to behave quite differently. Each cell type might be expected to
exhibit a pattern of gene expression as defined by its inherited constituents. The
cellular diversity generated through this process is illustrated by the asymmetric cell
divisionundergone by budding cells of Saccharomycescerevisiae. Upon cell division,
two distinct cell types are generated. The mother cell, which is typically larger carries
a scar on its surface whereas the surface of the daughter cell is entirely new.
Additionally, the mother cell is capable of undergoing a mating type conversion
18
whereas this cannot normally occur in the daughter cell (Strathern and Herskowitz
1979). The asymmetric division exhibited by cells of the bacterium Caulobacter, also
results in two distinct cell types (Newton and Ohta 1990). A motile swarmer cell and
a non-motile stalked cell result from the division process.
Both cell types behave
quite differently and both have distinctly different patterns of developmental gene
expression (Gober and Shapiro 1991). While the mechanisms that regulate the
process of asymmetric cell division are likely to involve both spatial and temporal
aspects acting within the predivisional mother cell, in the final analysis, culture
heterogeneity results as a natural consequence of the reproductive process. Since
culture heterogeneity is inherent to these systems, these cases will no longer be
considered.
In contrast to the heterogeneity that results from asymmetric division,cellular
diversity can also arise when identical progeny adopt different fates as a consequence
of some later event. This situation arises in sporulating cultures of B. subtilis where
under appropriate conditions only a fraction of the cells develop endospores (Ireton
and Grossman 1992). Since the sister cells that result from cell division during
vegetative growth, appear identical, this system raises interesting questions regarding
"When and Why ?" sister cells adopt different developmental pathways.
The answers to such questions are difficult to obtain since they require
detailed knowledge of the intracellular concentrations of key components within cells.
However insights into such questions can be gained from a detailed account of the
sequence of events that are set in motion once cells initiate the developmental
19
program.
Because cells eventually develop into distinctly identifiable cell types, an
accurate account of the events leading up to this point could in principle provide a
means of localizing the point during development at which physiologicaldiversity is
established. In the next section, the sequence of biochemical events that are set in
motion, once cells receive the physiological and environmental signals required for
spore formation, are described.
2.2 The Developmental Program of Spore Formation in R subtiis
2.2.1 Signal Transduction va the Phosphorelay
When cells of B. subtilis encounter nutrient limitations, as characterized by
entrance into the stationary phase, exponential growth ceases and the cells initiate a
number of adaptive response mechanisms to cope with the changing environmental
conditions. Depending on the exact nature of the cultivation conditions, the adaptive
responses include degradative enzyme expression, antibiotic secretion, motility,
competence development and spore formation (Ferrari et al. 1986, Dubnau 1991,
Errington 1993, Strauch and Hoch 1993). However when faced with continued
starvation, cells eventually initiate the developmental process of spore formation.
One of the earliest events controlling the initiation of spore formation is the
activation, va phosphorylation, of the developmental transcription factor encoded by
spoOA This process involves, in part, the sequential transfer of inorganic phosphate
by the kinA, spoOF, and spoOB gene products by what has been termed a multicomponent phosphorelay (Burbulys et al. 1991; figure 2.2). Upon entrance into the
20
stationary phase, KinA autophosphorylates in an ATP dependent reaction on a
histidine residue and transfers the phosphate to an aspartate residue in SpoOF.
SpoOF-P then serves as a phosphate donor by transferring this phosphate to SpoOB.
Finally, SpoOB-Ptransfers this phosphate to the N-terminal domain of SpoOA. In
addition to KinA, other proteins kinases have been identified (Burbulys et al. 1991,
Rudner et al. 1991).
SpoOA and SpoOF belong to a class of procaryotic regulatory proteins, the
response regulators of the two-component regulatory systems (Albright et al. 1989,
Ronson et al. 1989, Stock et al. 1989). The response regulators play a role in signal
transduction in response to changes in the environment.
These proteins are
characterized by their conserved N-terminal domains and their activity appears to be
modulated by their phosphorylated state.
A histidine protein kinase, or sensor component, is the other component of
the two-component regulatory systems. In response to certain signals, the sensor
component autophosphorylates a histidine residue and transfers the phosphate to the
cognate regulator. Histidine protein kinases are characterized by their conserved Cterminal domains. Two component regulatory systems have been found to control
the adaptive responses of a number of processes including nitrogen regulation,
phosphate regulation, motility and genetic competence.
2.22 Transcriptional Activation via SpoOA-P
Upon activation, one of the earliest functions of SpoOA-P is to indirectly
21
activate transcription of a number of genes involved in the adaptive mechanisms
available to the cell. SpoOA-P represses abrB, a gene which encodes for a repressor
which is present during vegetative growth (Perego et al. 1988, Strauch et al 1990).
AbrB in turn represses of a number of stationary genes involved in the adaptive
responses available to the cell (figure 2.3A). AbrB has been found to prevent the
transcription
of genes involved in degradative enzyme expression such as aprE
(Strauch et al. 1989), sporulation such as spoOE (Perego and Hoch 1987, 1991,
Strauch et al. 1989), spoOH(Weir et al. 1991) and spoVG (Zuber and Losick 1987,
Robertson et al. 1989), antibiotic synthesis such as tycA (actually a Bacillus brevis
antibiotic synthesis gene, Robertson et al. 1989) and itself (Strauch et al. 1989). It
is also known to play a role in competence development (Dubnau 1991). Hence the
early action of SpoOA-P prepares the cell for possible entrance into a number of
adaptive pathways. The eventual path chosen depends on the nature of the
cultivation conditions.
The removal of AbrB initiates a positive feedback loop that leads to more
activated SpoOA (figure 2.3B). An important gene whose transcription is indirectly
activated by SpoOA-Pis SpoOH(Yamashita et al. 1986, Dubnau et al 1987). spoOH
encodes the oHsigma factor (Weir et al. 1991), which directs the transcription from
loci that encode phosphorelay components including kinA, spoOF, and spoOA
(Predich et al. 1992). Since the products of these loci lead to more SpoOA-P,the net
result of this loop is the amplification of SpoOA-P.
SpoOA-P also serves to activate transcription from the essential sporulation
22
loci spolIA (Trach et al. 1991), spollE (York et al. 1992), and spollG (Satola et al.
1991, 1992, Bird et al. 1993) (figure 24). The products of these loci are involved in
establishing and maintaining differential gene expression in the mother cell and the
forespore (Margolis et al. 1991, Losick and Stragier 1992). SpoOA-P directly activates
transcription from these loci by binding to upstream regions of the respective
promoters.
Additionally, because transcription from these loci appear to be
negatively controlled by the sinR gene product, SpoOA-P also appears to play a role
in countering the effects of SinR (Smith et al. 1991, Mueller and Sonenshein 1992).
While the mechanism by which this occurs has not been identified, recent findings
suggests that it is indirect being mediated by the developmentally expressed sinlgene
product (Bai et al. 1993).
2.2.3 Establishing Late Sporulation Gene Epression
Following spollIA and spoIIG transcription, the sigma factors encoded by
spolIAC, aF, and spoIIGB, au, are activated and the temporal program of gene
expression is continued. Activation of
ja
involves the spollA and spoIIE gene
products (Stragier ct al. 1988) and is believed to coincide with the morphological
changes associated with the formation of the asymmetricseptum, the physical barrier
that separates the mother cell from the developing forespore. Once this barrier is
in place, gene expression in the mother cell relies in part on aE, and gene expression
in the forespore relies in part on af. Support for this model follows from the fact
that the spoIIGB gene product, oE, is needed to direct transcription from spolID, a
23
gene known to be expressed exclusively in the mother cell (Driks and Losick 1991).
More detailed accounts of these and subsequent events can be found in a recent
review by Errington (1993).
2.3 Mechanisms Leading to Physiological Diversity
The sequence of events beginningwith signal recognition and transduction and
ending at spore formation indicates that a sporulating cell must successfully pass
through a number of developmental stages on its way to a spore. The question raised
in section 2.1, namely " When and why identical cells adopt different developmental
programs ?" can now be addressed in terms of the pattern of gene expression
exhibited by sporulating cultures of B. subtlis. Specifically,"At what point along the
sequence of events do sister cellsadopt different developmental fates ?" As discussed
below, at least two explanations exist.
2.3.1 The Threshold Theory
The threshold theory proposed by Aubert et al. (1961), hypothesized that the
level of an extracellular carbon source is responsible for triggering the developmental
changes associated with spore formation.
This hypothesis was based on the
observation that "mass sporulation" of Bacillus megaterium cells, in continuous
culture, could be effected by a shift down in the feed rate to the reactor. Dawes and
Mandelstam (1969, 1970) attempted to test this hypothesis,however their results were
inconclusive (section 2.3.3).
24
There are two major implications of the threshold theory. The first follows
straight from the mathematical concepts upon which threshold phenomena find their
origins (Arnold 1986, Golubitsky and Schaeffer 1985). Namely that small changes in
a certain system variable, often referred to as a bifurcation parameter, might lead to
dramatically different system behavior.
According to the Aubert hypothesis, the
bifurcation parameter would be represented by the concentration of the extracellular
carbon source and this parameter would determine whether or not a cell entered the
developmenal pathway of sporulation.
The second major implication of this theory is that physiological differences
between sporulating and non-sporulating cell-typesmust be established at one of the
earliest events in the sequence of events along the developmental pathway. Given
that nutritional, intercellular and cell-cycle dependent signals are integrated,
processed and, transmitted through the phosphorelay (Ireton et a. 1993, Ireton and
Grossman 1992), this theory is consistent with the suggestion that continuous inputs
into the phosphorelay generate bifurcated outputs. This is represented in figure 2.5,
where the effect that the phosphorelay network might have on the distribution of a
developmental specific molecule, within a cell population, is illustrated.
Given a
population of cells containing a smoothly distributed concentration of some threshold
molecule, dramatically different cell types would result from the threshold mechanism
associated with the phosphorelay.
More recently, as genetic and molecular biologicalevidence has indicated that
the spoOA gene product is of critical importance to both sporulation specific gene
25
expression and stationary phase signal recognition, the threshold hypothesis has been
resurrected (Antoniewski et al. 1990). The effects that null mutations in kinA, a gene
encoding for one of the earliest acting proteins in the sequence of events leading to
sporulation
specific gene expression, have on early gene expression has been
investigated by a number of researchers (Perego et al. 1989,Antoniewski et al. 1990,
Mueller and Sonenshein 1992). kinA null mutations have been found to have no or
little effect on the expression of genes indirectly activated by SpoOA-P, via AbrB,
including spoVG, aprE and itself (Perego et al. 1989,Mueller and Sonenshein 1992).
However kinA null mutations have been shown to reduce spore formation and the
expression of spollA,
spollE
and spoIIG to approximately 10% of wild type
(Antoniewski et a. 1990, Mueller and Sonenshein 1992). In contrast to kinA,
mutations in other components of the phosphorelay such as spoOA,spoOBand spoOF
completely abolish spore formation and early sporulation gene expression.
In
contrast to the hypothesis set forth by Aubert, the activated SpoOA threshold
hypothesis suggests that outputs from the phosphorelay are needed to elevate the
concentration of a particular molecule to threshold levels.
2.3.2 The Contiuum
Theory
An alternative to the threshold theory is the "continuum theory."
In its
simplest form, such a theory might suggest that all cells initiate adaptive gene
expression, as controlled by activated SpoOA, and that spore formation results only
if cells successfully pass through a number of developmental checkpoints. This theory
26
implies that culture heterogeneity is a consequence of the cells failure to overcome
the multiple barriers associated with the developmental program. Such an idea was
proposed by Coote (1972) based on his studies of various oligosporogenous mutants
(i.e. reduced sporulation mutants). Evidence for this hypothesis was provided by the
finding that some oligosporogenous mutants resulted from leaky mutations in genes
where null mutations otherwise resulted in asporogenous phenotypes.
2.3.3 Common Ground
A closer examination of the finer points behind each theory reveals that both
theories contain common elements. This follows from the fact that the ability of a
cell, within a population, to obtain a threshold level of a particular molecule can be
viewed as a stochastic event.
Hence even if threshold mechanisms exist, random
factors might still be involved in determining which cells initiate the developmental
program.
Conversely, once cells have initiated spore formation, the ability to
successfully complete spore formation might be viewed in terms of the cells ability to
overcome multiple thresholds.
Support for this interpretation is provided by results of experiments aimed at
testing the threshold hypothesis (Dawes and Mandelstam 1969, 1970). By following
the incidence of spore formation in continuous cultures of B. subtilis, as a function
of the feed rate to the culture, Dawes and Mandelstam hoped to distinguishbetween
these hypothesis. They reasoned that if the threshold hypothesis were true, then as
the feed rate to the reactor was continuouslychanged, a discontinuityin the incidence
27
of spore formation would be observed.
Such a discontinuity was never observed.
Instead, their results showed that the probability that a cell would form a spore
varied smoothly, but inversely,with changes in the feed rate to the reactor.
However, these results do not rule out the threshold hypothesis. To the
contrary, such results would be expected if the threshold theory were true. Insight
into this claim can be obtained by considering how changes in the feed rate to the
reactor might affect the distributionof a particular molecule within the culture
population. Given a threshold molecule, smooth changes in the reactor feed rate
would be expected to alter the intracellular concentration of this molecule in a
smooth and uniform manner in all cells. Furthermore, feed rate changes would be
expected to change the average threshold molecule concentration in a smooth
manner. Therefore, for a fixed threshold concentration, the fraction of the cells in
the culture crossing this threshold would vary smoothly with the feed rate (figure 2.6).
Given the complexity of the developmental process, it is likely that the actual
situation involves elements of both theories. The fact that cell-cycle signals are
involved in the initiation process (Mandelstam and Higgs 1974) clearly indicates that
purely stochastic arguments represent an oversimplification. Likewise, even if the
threshold theory were true, it is likely that multiple thresholds are involved. Evidence
suggesting the existence of multiple thresholds can be found in early studies aimed
at identifying the point at which cells of B. subtilis become committed to the
sporulation (Mandelstam and Sterlini 1969). The results of this work suggest that no
single point of commitment exist. Instead there appear to exist multiple checkpoints
28
along the developmental pathway, each of which appears to be associated with stable
mRNA species (Mandelstam and Sterlini 1969). Additionally, as will be discussed
later in this work (Chapters 5 and 6), the repressive effects exhibited by AbrB and
SinR very much points to the existence of multiple thresholds.
Given that the
program of gene expression associated with spore formation represents a complex
process involvingthe spatial and temporal coordination of multiple developmental
sigma factors, a major challenge will be to reconcile what has been learned in the
past with the molecular biological mechanisms that are presently being elucidated.
29
Mother Cells
Sister Cells
A
A
B
A
At Birth
Saccharomyces cerevisiae
*
Caulobacter
A
After Birth
Bacillus subtilis
Escherichia coli
FEge 2.1 Schematic illustration of the possible origins of population heterogeneity. Culture
heterogeneity may result be a natural consequence of asymmetric cell division or a
consequence of a later event (adopted from Horvitz and Herskowitz 1992).
30
KinA +ATP
Phosphorelay
KinA-P\
SpoOF
SpoOB
Stationary Phase
Signals
*.
Ki-P
/
SpoOF-P
S1oOB-P
- V- -
Activated
. t
SpoOA
Figure 22 Schematic illustration of the phosphorelay signal transduction mechanism used
by cells of B. subtilis to sense and transmit stationary phase signals (originallypresented by
Burbulys and coworkers 1991).
31
SpoOA-P
A
AbrB
aprE
tycA
spoOH
spoVG
spoOE
degradative
enzymes
sporulation
protein
antibiotics
--
Repression
+
Activation
SpoOA
B
Signas .......
4
SpoOA-P
'1
I
AbrB
SpoOH
Fgue 2.3 Schematic illustration of the initial events set in motion once SpoOA is activated
via phosphorylation. The AbrB pathway is first activated (A) and this leads to more
activated SpoOA via SpoOH (B).
32
SpoOA-P
I
t inH
spollA/
spollG
spollE
+
spollD
Figure24
Schematic illustration of the stage II gene expression via activated SpoOA.
33
0
Threshod
z
Threshold Molecule
INPUT i
Z
3
z
50%
50%
Phosphorelay Component
Figure2. Population model explaining the threshold hypothesis of Aubert and coworkers
(1961). It was hypothesized that the concentration of an extracellular metabolite acted as
a threshold molecule.
If it is assumed that the extracellular concentration effectively
represents some intracellular metabolite, then the concentration of this intracellular species
would be distributed throughout a cell population in a "normal" manner.
Only those cells
containing at least the critical concentration go on to form spores as illustrated by the
bifurcated "output" population distribution.
34
0
C)
D0
E
z
Threshold Molecule
Figue 2.6 Model explaining the observations of Dawes and Mandelstam (1970). This
conceptualization indicates that stochastic effects are not incompatible with threshold
mechanisms since a reduced feed may simply shift the entire population so that more cells
are able to attain the critical threshold.
35
Chapter 3: Development of Flow Cytometry-Based Techniques for Studying Gene
Expression in Single Cells of R subtilis
3.1 Introduction
In this chapter, the development of fluorescence-activated cell sorting (FACS)
protocols suitable for detecting fi-galactosidase in single cells B. subtiis is described.
These procedures are simple, easily implementable and take advantage of
commercially available probes, 5-octanoylaminofluorescein di-f-D-galactopyranoside,
C-FDG, 5-dodecanoylaminofluoresceindi-f-D-galactopyranoside, C12 -FDG and the
membrane binding dye PKH26 along with standard flow cytometry instrumentation.
The suitability of these protocols for physiological studies was assessed based on the
ability of the procedures to accurately recover f-galactosidase expressing populations
in controlled mixture experiments. Additionally a correlation found to exist between
bulk culture assays and flow cytometry-based fluorescence measurements (with C8 -
FDG) indicates that these procedures are suitable for quantitating the relative figalactosidase activity in a sample population. When the optimized staining conditions
were used in conjunction with what is termed the "internal PKH26 negative control"
(see section 3.6), a robust system capable of analyzing gene expression in single cells
of B. subtilis results. The approach taken to develop these techniques is flexible and
allows the procedures to be easily modified to meet the experimental demands of a
given system. This was demonstrated by successfully applying these techniques to
analyze cells obtained from two different growth media. The development of these
protocols demonstrates that FACS-based technology can be applied successfully to
36
study single cell gene expression in bacteria. These developments should be viewed
as steps towards the more widespread use of flow cytometry in microbiology. Since
the completion of this work, an alternate technique for detecting B-galactosidase
activity in single cells of the Gram-negative bacterium Myxococcus xanthus was
published (Russo-Marie et al. 1993). While this alternate technique does not include
a control for background fluorescence, it offers the advantage in that cell viability is
maintained.
3.1.1 Flow Cytometry
The advent of the flow cytometer represented a revolutionary advance in the
area of single cell measurements. For the first time, the attributes of single cells
could be measured for a population large enough to accurately reflect the culture
demographics and to reliably detect rare events. In addition to rapid and robust
analysis of large populations, flow cytometry also makes it possible to sort single cells
or various subpopulations within the sample. The sorting process maintains cell
viability, assuming the cells remain viable after the staining process, permitting the
samples to be recultivated or subjected to further biochemical analysis. These
capabilities make the flow cytometer arguably the most valuable instrument available
today for studying the attributes of large populations of single cells.
In theory, a flow cytometer can be used to quantitate any fluorescing
constituent within or associated with a single cell. In practice, the molecule that one
desires to detect (i.e. DNA, RNA or other cellular constituents) either does not
37
fluoresce or fluoresces very weakly effectively making it undetectable.
Therefore
fluorescence probes specific to the molecule of concern are commonly employed.
Probes have been developed or identified for the detection of a number of cellular
constituents including DNA, RNA, cellular protein, surface markers and various
intracellular enzymes and molecules (Crissman et al. 1979). With the exception of
intracellular
enzymes and antigens, the procedures
for identifying the above
mentioned constituents are relatively straightforward. The complications with
detecting intracellular enzymes and antigens result from the physical barriers
presented by the cell wall and membrane. Many of the techniques that claim to have
overcome the problems with intracellular molecule detection do not provide
convincing data in support of these arguments (Srienc et al. 1983, Jaccoberger et al.
1986, Schepper et al. 1987). For an excellent categorization of the probes available
for studying the attributes in single cells, the reader is referred to a number of
references (Crissman et al. 1979, Waggoner 1990, Haugland 1992).
Despite the numerous flow cytometry-basedtechniques and probes that have
been developed for studying the attributes of single eukaryotic cells, the only
intracellular enzyme that has been reliably detected in a heterogeneous population
using flow cytometry is P-galactosidase (Srienc et al. 1986, Nolan et al 1988, Wittrup
and Bailey 1988, Fiering et al. 1992). However, given the heavy reliance that modern
day biology places on the use of lacZ gene fusions (Casadaban et al. 1983, Nielsen
et al. 1983, Shapira et al. 1983, Zuber and Losick 1983), this "limitation" is by no
means a major one. Presently lacZ fusions are used to study gene regulation and
38
expression in numerous bacterial and yeast species (Casadaban et al. 1983) as well
as various mammalian cell lines (Shapira et al. 1983, Nolan et al. 1990). Hence the
ability to detect P-galactosidase in single cells using flow cytometry provides a
powerful tool that complements traditional biochemical techniques.
3.L2 Bacterial Flow Cytometry
While flow cytometry has revolutionized many fields of science, particularly
immunology, its use in bacterial microbiology is less common. In other words "... only
a few dozen of several thousand papers in the flow cytometry literature deal with
microbiological applications." (Shapiro 1988). Furthermore most of these applications
have been aimed at identifying aggregate cellular constituents such as the total
cellular protein or DNA (Bailey et al. 1977, Bailey et al 1978,Hutter and Eipel 1978,
Boye et al. 1983,Robertson and Button 1989,Steen and Boye 1980,Steen et al 1982,
Steen 1990). Other applications of flow technology in microbiology have been either
aimed at detecting the cell itself or characterizing some physical feature of the cell
such as size (Betz et al. 1984, Miller and Quarles 1990, Phillips and Martin 1982,
Robertson and Button 1989). More recently, probes and protocols suitable for
detecting rRNA in single bacterial cells have been developed (Delong et aL 1989,
Amann et al. 1990, Wallner et al 1993).
However such applications represent a small fraction of the techniques that
have been developed for studying the attributes of single eukaryotic cells.
Specifically,one of the most powerful features of the flow cytometer, the ability to
39
detect specific constituents such as proteins and enzymes within or associated with
single cells, has eluded microbiological studies. The handful of attempts reported
either do not provide convincing results in support of their claims (Schepper et aL
1987) or have sought to exploit peculiarities of a particular system. A number of
workers have been able to correlate the presence of inclusion bodies, which
presumably correlate with the intracellular content of a particular intracellular
constituent, with light scatter measurements (Srienc et al. 1984, Wittrup et al. 1988).
More recently, the detection of gramicidin S via FITC staining has been reported
(Azuma et al. 1992). Again, this work was possible due to an apparent correlation
found to exist between gramicidin S and green fluorescence measurements. In
general these applications were possible only through the fortuitous existence of
system specific correlations and do not represent approaches upon which rational
strategies can be built. Furthermore, a closer examination of the results raise
questions about the resolution of such measurements. The most convincingstudies
where specific intracellular proteins have been detected in bacterial cells using flow
cytometry involved the detection of
consisting of hundreds
-galactosidase in microcolonies of E. col,
of cells (Nir et al. 1990A, 1990B).
However, such
measurements were only possible through the application of gel-microdroplet
technology, which adds a complicating factor to the analysis (Weaver et al. 1984,
Weaver 1987).
The scarcity of applications of flow cytometry to studying gene expression in
bacteria is due to a number of factors. While the technical barriers to overcome are
40
formidable (section 3.2) the implicit assumption that bacterial cultures are
homogeneous, may represent an even greater barrier to the widespread application
of flow cytometer-based
analysis of bacterial cultures.
While this homogeneity
assumption is often justified, the existence of culture heterogeneity under commonly
employed conditions suggests that this is not always the case (Benzer et al 1953,
Novick and Weiner 1957, Tyler and Magasanik 1969, Maloney and Rotman 1973).
Advances in eukaryotic applications have come about only after much time and effort
was spent in designing and developing suitable probes and protocols.
Until
bacteriologists commit equivalent resources, there is no reason to believe that the
problems specific for bacteria will be overcome. Certainly this will not occur unless
the importance of culture heterogeneity is acknowledged by more microbiologists.
3.2 Problem Statement
3.z1 Sgnal Strength
The small size of bacteria relative to eukaryotic cells presents a major
technical challenge that must be overcome if FACS-based technology is to be used
to study gene expression in single cells of bacteria (Steen 1990). Typically bacterial
cells are two to three orders of magnitude smaller than eukaryotic cells, on a volume
per cell basis.
Since flow cytometers detect the total number of fluorescing
molecules, as opposed to the concentration, signals generated by bacterial cells will
be two to three orders of magnitude lower than their eukaryotic counterparts, for the
same fluorochrome concentration. Additionally, the total amount of the cellular
41
constituent to be detected will be lower in bacterial cells. Whether it is desired to
detect a bulk quantity such as the total cellular DNA or a specific enzyme such as pgalactosidase, the signal generating potential will be considerably less. Therefore
probes and protocols developed for staining eukaryotic cells will not in general be
applicable to bacterial systems. To date, very few of the procedures developed for
staining eukaryotes have been successfullyapplied to bacteria.
The weak signals generated by bacteria present formidable barriers since they
must be detected above relatively high levels of background.
originate
from a number
Background signals
of sources including cellular autofluorescence
(i.e.
constituents within the cell that naturally fluoresce), electronic noise, and free
fluorochrome that might be present in the staining mixture (Shapiro 1988, Wittrup
and Bailey 1988). While these processes and problems are associated with the
development of any staining procedures, they become of critical importance when
weak signals are to be detected.
Spurious fluorescence signals will act to reduce the
already low signal to noise ratio inherent to bacterial systems.
In many cases,
detecting bacteria with forward light scatter, the most routine parameter acquired by
the flowcytometer, may not be possible or requires that the detection system be fully
optimized (Russo-Marie et al. 1993, Wittrup et al. 1988).
The technical barriers to detecting fl-galactosidase in single bacterial cells,
using a fluorogenic substrate, are illustrated in figure 3.1. The top object represents
a cell that contains /-galactosidase.
The goal is to identify this cell among cells that
may not be expressing f-galactosidase (bottom object). The heterogeneity might
42
reflect a difference in the transcriptional state of promoter X, the promoter
responsible for driving P-galactosidase expression. Alternatively, this heterogeneity
might be genetic in origin.
If the P-galactosidase expressing population is to be
detected, it is first necessary for the non-fluorescent substrate to enter the cell where
it may be cleaved by /-galactosidase.
Upon cleavage, the fluorescent product must
accumulate within the cell in order to generate a signal that is detectable above the
background signals due to cellular autofluorescence and electronic noise. However,
in the process of achieving this goal, spurious fluorescence signals typically develop.
Two common sources are; 1) the leakage of fluorescing products from the
-
galactosidase positive cells into the extracellular medium where they may become
associated with P-galactosidase negative cells and 2) fluorescing molecules that result
from spontaneous substrate hydrolysis, either before (i.e. in the substrate stock
solution) or during the staining process.
Therefore
the ability to detect /B-
galactosidase positive cells relies on the ability to overcome these competing
processes. If this is not achieved, the signal generated by a /-galactosidase positive
cell will be indistinguishable from that generated by a /-galactosidase negative cell.
Successful protocols that have been developed for detecting /-galactosidase in single
eukaryotic cells rely on the ability to optimize these competing processes (Nolan et
al. 1988, Wittrup and Bailey 1988).
3.2.2 Bacterial Diversity
The detection problems inherent in the analysis of bacterial cells are further
43
complicated by the large diversity that exists within the bacterial kingdom and the
plethora of cultivation conditions employed to study these organisms. Large
variations in cell size exist between different genera. Also, the membrane and cell
wall architectures are quite different in Gram-positive and Gram-negative bacteria.
A further complication results from the ability of bacteria to alter their size,
morphology and membrane properties depending on the cultivationconditions. Since
these parameters greatly influence the ability to stain and detect cells,procedures that
have been developed and optimized for a specific system will most likely require
modifications if the cultivation conditions are altered.
3.3 Approach
In order to study gene expression in single cells of B. subtilis, a FACS-based
technique capable of detecting P-galactosidase in single cells was developed.
This
approach was motivated by the success that a number of researchers have had with
detecting this enzyme in eukaryotes (Srienc et al. 1983, Nolan et al. 1988, Wittrup
and Bailey 1988, Fiering et al. 1991).
Additionally, a number of commercially
available fluorogenic substrates specific for P-galactosidase could then be evaluated
(Haugland 1992). Such a technique would then allow the expression of a number of
genes to be studied due to the availabilityof numerous lacZ fusions and the relatively
straightforward procedures by which new fusions can be created (Errington 1986).
To address the problems outlined in the previous section, it was first desirable
to assess the background problem under "typical" staining conditions.
44
This was
accomplished by introducing a control population, consisting of lacZ- cells stained
with the fluorochrome PKH26, into the staining mixture and independently
monitoring the fluorescence accumulated by this population using two-color flow
cytometry.
This population is referred to throughout this chapter as the PKH26
negative control or the internal control. The internal control was used to identify the
background levels associated with a number of staining conditions and to identify
conditions that minimized spurious fluorescence.
Additionally, use of the PKH26
negative control allowed the assay conditions to be rapidly modified when cells were
grown under different cultivation conditions.
3.4 Summary of Staini
Condition
In this section, the assay conditions found to be suitable for detecting /galactosidase activity in single cells of B. subtilis are summarized.
The rest of this
chapter is devoted to the presentation and the analysis of the experiments that led
to the identification of these conditions.
All staining experiments performed with C12 -FDG employed SMY::pAF25 as
the positive control. The strain contains a veg-lacZ fusion integrated into the amyE
locus of the parental wild-type strain designated SMY. The veg promoter is
constitutively active during vegetative growth. MB284 was employed as the positive
control in experiments performed using C8-FDG. This strain contains a spo VG'-'lacZ
fusion integrated into the SPfi site of the parental wild-type strain designated SMY.
The spoVG promoter is developmentally regulated and is active during the early
45
stationary phase. The wild-type strain SMY, which does not contain a lacZ fusion,
was used as the negative control in all experiments described in this chapter.
Endogenous f-galactosidase activity was not detected in this strain. It was necessary
to use different positive controls under different cultivation conditions since the
promoters driving the expression of f-galactosidase in these two strains are subject
to different regulatory controls (Zuber and Losick 1983,Fouet and Sonenshien 1990).
The cultivation conditions and the strains used are described in detail in Chapter 9.
3.4,1 C-FDG
Optimal conditions for staining cells of B. subtilis grown in nutrient broth with
and without glucose (5g/l) are summarized in table 3.1.
The staining solution
consisted of a substrate concentration of 33/uM C8 -FDG or C12-FDG in phosphate
buffered saline (PBS, pH 7.0) supplemented with 10mM Mg2 + (PBSM) and 0.4g/l
sodium azide (PBSMA).
A 33,uM fluorogenic substrate concentration was chosen
since it was suitable for generating a detectable signal in P-galactosidase positive cells
without the solubilityproblems encountered at higher concentrations (specificallyCl2FDG at a concentration of 66MM). The cell number concentration in the staining
mixture did not exceed 1,000,000 cells/ml, excluding the PKH26 control population.
This number concentration represented a compromise between the need to minimize
the number of cells in the staining mixture, which reduces the amount of dye leaked
into the medium, and the need to obtain a cell concentration that could be
conveniently analyzed using flow cytometry.
46
Cells to be stained with C1 2 -FDG were subjected to a 500C temperature pulse
for ten minutes then placed on ice. This pulse was found to permeabilize cells
allowing the substrate to enter once the sample was placed on ice.
Since the
substrate was present during the temperature pulse, cell staining also occurred during
this process.
On ice, the cells were able to accumulate detectable
levels of
fluorescing molecules with minimal leakage into the extracellular medium.
The
samples were incubated overnight, eight to twelve hours, to allow a steady state
fluorescence distribution to result, then analyzed by flow cytometry.
A detailed
protocol is presented in Chapter 9.
Cells to be stained with C8-FDG were permeabilized with a 1% suspension of
toluene, in PBSMA, and stained at room temperature. Cells present in a 1% toluene
solution were vigorously vortexed for at least 10 seconds, then diluted 1/100 into a
solution of 33iM C8 -FDG in PBSMA.
This dilution was necessary in order to
remove most of the toluene before the samples were analyzed by flow cytometry.
The sample was incubated at room temperature for at least 30 minutes, to allow a
steady state fluorescence distribution to result, then analyzed by flow cytometry.
These conditions are described in greater detail in Chapter 9.
3.42 PKH26
Optimal conditions for staining cells of B. subtilis grown in nutrient broth, with
and without glucose, with PKH26 are summarized in table 3.2. Cells were harvested
approximately two hours after exponential growth ceased and added to a solution of
47
5MiMPKH26 in Diluent C (the solvent provided by the manufacturers of PKH26) and
incubated at room temperature for five to seven minutes with occasional mixing. The
staining reaction was stopped by diluting this suspension ten-fold with a solution of
20g/1 bovine serum albumin (BSA) in PBSM followed by a one minute incubation at
room temperature.
Since PKH26 binds to hydrophobic sites (Horan et al 1990), the
step served to remove the residual PKH26 free in the mixture.
The cells were
recovered by centrifugation, washed three times with room temperature
PBSM,
resuspended in the original sample volume, then placed on ice. When fermentation
samples were to be analyzed by flow cytometry, an aliquot of these cells was
introduced into the sample when required. These conditions are described in greater
detail in Chapter 9.
3.
Description of Molecular Probes
3.5.1 -galactosidase Fluorogenic Substrates
In order to overcome the detection problems associated with the small size of
bacteria, it was necessary to employ a fluorogenic substrate whose fluorescing product
exhibited:
1) a high extinction
coefficient
in order
to efficiently
absorb
electromagnetic radiation (Shapiro 1988), 2) a high quantum efficiency in order to
efficiently convert the absorbed light into fluorescent radiation (Shapiro 1988), and
3) the ability to stay associated with the cell following hydrolysis. While a number of
probes satisfy the first two requirement, fluorogenic substrates that satisfy all of the
above requirements are few. The third requirement is of extreme importance since
48
it represents the means by which the problems associated with the small size of
bacteria can be overcome.
Namely, a larger cell-associated fluorochrome
concentration can compensate for the smaller cell volume.
The Cn-FDG series of -galactosidase substrates appeared to satisfy these
requirements.
A comparison between the spectral properties of several
fluorochromes upon whichmanyp-galactosidase substrates are derived, reveal several
important features (table 3.3). Fluorescein-based P-galactosidase probes are among
the most sensitive fluorochromes available today as estimated based on their ability
to absorb electromagnetic radiation. A comparison between the various extinction
coefficients presented in table 3.3 indicates that fluorescein-based substrates can be
expected to be approximately twice as sensitive as resorufin-based
-galactosidase
substrates and about five times as great as f-methylumbelliferone-based substrates.
Since fluorescein enjoys a relatively high quantum efficiency (0.98; Mathies and Stryer
1986), the sensitivity can be expected to be even greater in practice. Additionally,
fluorescein-based substrates have the advantageous property in that they can be
excited by light in the visible spectrum exhibiting an excitation maximum at 490nm.
Also included in this table, for comparison purposes, are the spectral properties of
one of the most sensitive class of fluorochromes available today; those derived from
the phycobiliproteins (Mathies and Stryer 1986, Shapiro 1988). Unfortunately Pgalactosidase substrates employing this fluorochrome do not exist.
The major attribute of the C,-FDG series of substrates results from the
increased affinity that the fluorescing product exhibits for the cell, when compared
49
to fluorescein. Figure 3.2 serves to compare the chemical structures of fluorescein
di-p-D-galactopyranoside
(FDG; A) with that of C 12 -FDG (B).
The difference
between these two molecules is the covalent link between the 5 position of the
fluorescein phthalate ring and the linear hydrocarbon. Members of the Cn-FDG
family differ only by the length of the linear hydrocarbon.
Upon hydrolysis by
fl-
galactosidase, the sugar residues shown on both of these molecules are released and
a highly fluorescent fluorescein or fluorescein derivative is released.
Since the
product resulting from Cn-FDG hydrolysis is amphipathic, due to the hydrocarbon
"tail", the fluorescing product can accumulate within the cell to a greater degree than
that of fluorescein.
While some leakage can be expected to occur, the increased
affinity exhibited by the fluorescent product for the cell, suggests that it might be
possible to identify staining conditions that would not exist had the hydrocarbon tail
been absent.
3.5.2 The Second Fluorochrome: PKH26
The sole purpose of the second dye was to provide the means of directly
assessing and tracking spurious fluorescence signal development. Therefore the
following requirements were sought: 1) the emission spectrum of the dye must be
different from that of fluorescein, 2) the dye must not interfere with the sample of
interest and, 3) spurious fluorescence accumulated by cells stained with this dye must
accurately reflect the spurious fluorescence accumulated by the sample cells. Based
on the successful use of membrane binding dyes for cell tracking (Horan et al. 1990),
50
PKH26 was identified as a candidate probe.
Figure 3.3 indicates that the first of these conditions is satisfied by PKH26.
These spectra were obtained by exciting ethanol solutions containing fluorescein and
PKH26 with 488nm light. These data indicate that both fluorochromes have well
separated emission maxima (515nm for fluorescein and 667nm for PKH26). Based
on the dichroic mirrors and fluorescence filters employed in this study (Chapter 8),
the differences in these spectra were exploited. As will be shown in the next section,
cells stained with PKH26 also satisfied the last two requirements.
3.6 The Internal PKH26 Negative Control
3.61 TrackingBacground
To identify the level of spurious or background fluorescence accrued under a
given set of staining conditions, two-color flow cytometry was employed. lacZ- cells,
stained with PKH26 (i.e. PKH26 positive), were added to the assay mixture
containing the fluorogenic substrate and the cells to be analyzed and the increase in
green fluorescence of this population was monitored. This strategy is schematically
illustrated in figure 3.4 where the various cell populations present in the mixture are
represented. The top object represents a P-galactosidase positive cell (i.e. a cell that
is expressing P-galactosidase). The object on the bottom right represents a galactosidase negative. This might reflect a lack of expression or a lacZ- genotype.
Both of these populations would presumably be present in a sample to be stained.
The object on the bottom left represents the lacZ- cells that have been stained with
51
PKH26. Should free C8 -fluorescein or C12-fluorescein interfere with the sample to be
stained, the ability to independently monitor the PKH26 positive population using
two-color flow cytometry, should allow this to be identified. Since any fluorescence
associated with the PKH26 negative control represents spurious signals, this approach
also provides a means of detecting fluorescing products free in the medium due to
contaminated substrate solutions.
This strategy was validated by performing an artificial staining experiment
aimed at simulating the conditions that might be encountered when real culture
samples are analyzed. Such an experiment is schematically represented in figure 3.5
where the three objects shown represent three distinct cell populations that were
added to the test mixture. The bottom two objects represent lacZ- populations that
were added to the mixture, one of which has been stained with PKH26 according to
the conditions outlined in table 3.2. The lacZ- population stained with PKH26
represents what has been referred to as the background control. The second lacZpopulation would presumably represent the non-expressing cells in a real sample.
The top object is meant to represent the B-galactosidase expressing population
present in a real sample. For the purpose of identification, this population was
stained with 10,uM PKH26. All three cell populations were combined and incubated
in the presence of a 33MMClrFDG solution, at 500C, and the time course increase
in fluorescence of the various populations was monitored using two color flow
cytometry. Under these conditions, spurious fluorescence due to C12 -fluorescein was
found to be due to both the spontaneous hydrolysisof C12 -FDG and leakage from the
52
Bf-galactosidase positive population.
The means by which these population were experimentally tracked is
illustrated in figure 3.6. This figure represents the two-color contour plot used both
online and offline to track the three populations described in figure 3.5. The various
populations are represented by the three "clusters"and correspond identicallyto the
populations shown in this figure 3.5. The X-axis represents the fluorescence signal
due to fluorescein. The movement of each population along this axis is our principal
concern. The Y-axis represents the fluorescence signal due to PKH26. This signal
represents the means by which each population is identified. These data where
obtained from the initial time point associated with the experiment described. By
monitoring
the increase in green fluorescence
(i.e. the fluorescence
due to
fluorescein) of each of the lacZ- populations, the accuracy with which the background
control population tracks spurious fluorescence was assessed (figure 3.7).
The
parallel fluorescence profiles exhibited by both of the lacZ- populations (figure 3.7)
indicates that both lacZ- populations experience the same level of non-specific
staining. These data indicate that the background control strategy can be used to
accurately monitor fluorescence increases due to non-specific staining processes.
Similar results were obtained when three population mixtures were stained under a
variety of conditions (see section 3.7.3).
3.6.2 Fuhaescence Compensation
The finite C12 -fluorescein signal exhibited by the top population in figure 3.6
53
at time 0, reflects a technical feature associated with the use of multiple dyes in flow
cytometry. This can be explained with reference to figure 3.3, where the emission
spectra of the fluorescein and PKH26 are shown. Because the "shoulders" of each
spectra overlap, PKH26 specific signals will generate an apparent fluorescein signal
and vice-versa. The degree to which this "bleedover" occurs will depend on the signal
strength and the optical filters employed.
Greater PKH26 specific signals will
generally lead to greater "bleedover," and thus a greater apparent fluorescein
component. This is the reason why the top population, shown in figure 3.6, appears
"green" while the middle population does not.
While this "bleedover" may be
aesthetically unpleasing, it does not affect changes in the green fluorescence signal
and is therefore inconsequential to the conclusion drawn from the described kinetic
experiment.
In multi-color flowcytometry,the problems associated with spectra overlap are
addressed using what is termed "compensation." A number of excellent references
can be found on this topic (Loken et al. 1977, Alberti et al. 1987). Compensation
involves electronically subtracting a percentage of the corrupting signal, in this case
PKH26, from the corrupted signal, in this case fluorescein. This analysis assumes that
all signals can be decomposed into elementary components as defined by the specific
signals associated with each fluorochrome used (Loken et al. 1977). This analysis is
mathematically identical to the projection methods employed in linear operator
theory (Strang 1986). However in this case, the "basis" functions or vectors are
represented by the signals specifically generated by each fluorochrome.
54
3.6.3 Controlled Mixtures
PKH26 does not interfere with the ability to analyze real samples. The
possibility that the PKH26 control could interfere with the sample of interest was
addressed by determining how well the PKH26 control populations
could be
recovered after sample staining. The data presented in figure 3.8 indicate that known
PKH26 positive populations can be recovered from controlled mixture experiments.
These results were obtained from a number of controlled mixture experiments where
known amounts of PKH26 positive cells were added to samples prior to staining with
either C8 -FDG or C1 2 -FDG. It was therefore concluded that the PKH26 negative
control does not interfere with the sample population of interest.
3.7 Staning Cells with 5-Dodecanylaminofluorescein di--D-galactopyranoside
3.7.1 Approach
Since the membrane
fluidity of cells has been found to be strongly
temperature dependent (Overath et a. 1970), it was reasoned that temperature
manipulations could be used to stain cells with C12 -FDG. This strategy was motivated
by the structure of ClrFDG and its resemblance with the phospholipid molecules that
constitute the cell membrane.
Experiments were performed in order to identify suitable staining
temperatures and time that lead to detectable specific signals with little background
due to fluorochromes free in solution. Due to the possible thermal denaturation of
-galactosidase (Cohn and Monod 1951), the permissible staining conditions were
55
constrained by the need to maintain enzymatic activity. The following sections
describe the experiments that were performed in order to achieve these objectives.
3.7.2 Thermal Deactivation ofp-galactoidase
Before the staining conditions could be identified, it was necessary to assess
the effect that temperature variations had on the enzymatic activityof f-galactosidase.
To identify the temperatures and times at which a sample could be incubated without
dramatically inactivatingfB-galactosidase, temperature inactivation kinetic experiments
were performed. Samples were incubated at different temperatures in a solution of
PBSMA and the residual enzymatic activity, as determined through bulk culture
assays employing the chromogenic substrate o-nitrophenyl-/f-D-galactoside (ONPG),
was followed over time (figure 3.9A).
The shape of these curves suggested that at each temperature, the inactivation
of f-galactosidase obeyed a first order (in -galactosidase) kinetic process. Previous
researchers have also reported that P-galactosidase inactivation obey first order
kinetics (Cohn and Monod 1951). The fact that the residual activity curves appear
as straight lines on a semi-log plot support this assumption (figure 3.9B). The slopes
of these lines give the values of the first order rate constants (Levenspiel 1972).
The rate constants determined from the plots presented in figure 3.9B were
used to generate
-galactosidase deactivation contours aimed at identifying the
incubation times, at set incubation temperatures, over which a certain percentage of
the original activity could be maintained. These results are presented in figure 3.10
56
where the area beneath each curve, and above the X-axis, represents the set of
temperatures and time during which a sample may be incubated while maintaining
the indicated percentage of its original activity.
3.7.3 Constant Temperature Staining
Using the activity contours presented in figure 3.10 as a guide, a series of
constant temperature staining experiments was performed. Two sets of samples
consisting of f-galactosidase
positive cells and
-galactosidase negative cells were
stained under constant temperature conditions in the presence of the internal PKH26
background control.
Both sets of samples were incubated in a 33,1M C 12 -FDG
solution, in PBSMA, for times determined by the 80% activity contour shown in
figure 3.10.
The time course increase in fluorescence of all populations was
monitored and the average rate of fluorescence increase over this time frame
determined (figure 3.11). These results indicate that signal generation due to B-
galactosidase activity, leakage and spontaneous substrate hydrolysis are highly
temperature dependent.
At temperatures above 370C, increases in P-galactosidase
specific signal generation are accompanied by increases in spurious signal generation,
as illustrated by the top two curves. However below 37°C, very little fluorescence is
generated-by any of the aforementioned processes. These results suggests that below
the physiologicalgrowth temperature, 37°C, the membrane freezes and a transport
problem is encountered. Hence it was concluded that constant temperature staining
is not optimal.
57
3.7.4 The Two-Step Temperature-Time Pulse Matrix
The rate data presented in figure 3.11 indicate that two staining regions exist
over the range of temperatures studied. Above 370C, relatively high rates of specific
and background staining occurred. Below 37°C, the rates of specific and spurious
staining are both low. Therefore in order to take advantage of the high rate of
specific staining, while minimizing background, a two-step staining process was
devised. Cells to be stained were subjected to a brief high temperature pulse
followed by incubation on ice. It was hypothesized that the high temperature pulse
would act to load the cells with substrate and the low temperature would permit the
substrate to be cleaved and retained. It was found that the high temperature pulse,
in the presence of sodium azide, not only served to load the cells with substrate, but
also served to permeabilize the cells. On ice, the substrate continued to enter the
cell and the fluorescent product slowlyaccumulated to detectable levels.
To test the feasibility of this scheme, 50/50 mixtures of P-galactosidase
expressing and non-expressing cells were subjected to high temperature pulses for
different periods of time.
The temperature range was varied from 40°C to 52°C.
Since the level of non-specific staining increased monotonically with temperatures, the
goal was to identify the minimum pulse duration that led to good recovery of the
50/50 mixtures. Following the temperature pulse, the samples were incubated on ice
and analyzed eight to twelve hours later.
A ten minute temperature pulse at 50°Cwas found to be optimal. The results
of the temperature-time pulse experiments are qualitatively summarized in table 3.4.
58
While a number of classes of staining have been qualitatively described, the results
summarized in this table indicate that higher temperatures allow shorter incubation
times to be employed. Representative green fluorescence distributions corresponding
to the three qualitative classes described are presented in figure 3.12. Panel A
represents the distributions exhibiting what has been termed "little staining." Panel
B represents the distributions exhibiting two observable peaks. Panel C represents
the distributions that exhibit what has been termed "excellentpeak resolution." Based
on the distribution presented in panel C, the 50°C ten minute pulse was considered
optimal.
Finally it should be noted that the distributions resulting from these
experiments were found to be stable for at least 24 hours. This is a desirable feature
since often times the researcher will have limited access to a flow cytometer.
3.75 Cntroled Mixtures
The results summarized in the temperature-time pulse matrix indicate that a
ten minute 500C temperature pulse led to good staining with minimal background.
Therefore a detailed series of controlled mixture experiments was performed to
identify the resolution with which known positive populations could be recovered.
Controlled mixtures comprised of 5% to 90% -galactosidase expressing cells were
stained according to this strategy. Additionally, the PKH26 internal control was
added to each sample. Representative C12-fluorescein fluorescence distribution (i.e.
due to fluorescein) obtained from these experiments are presented in figure 3.13.
59
Panel A was obtained from a 100% lacZ- population. The fluorescence distribution
obtained from this sample represents the amount of background that must be
overcome in order to detect -galactosidase in a positive cell population. The
distributions presented in panels B through F were obtained from samples containing
known positive populations. The bimodal nature of these distributions indicate that
the two cell populations are recoverable from each mixture. One population exhibits
fluorescence signals equal to that of background (panel A). The second population
is able to accumulate a fluorescence signal above background.
The sizes of the various populations represented by the distributions presented
in figure 3.13 correlate with the fraction of fi-galactosidase positives known to be
present in the mixture. The relative proportion of the populations exhibiting high
and low fluorescence was quantitated using two methods. The first method involved
setting a fluorescence gate based on the green fluorescence exhibited by the internal
control (figure 3.14). The second method involved the use of a non-linear regression
package that could be used to fit two normal fluorescence distributions through the
experimentally obtained composite distribution.
A summary of these results is
presented in figure 3.15. This figure indicates that the fraction of f-galactosidase
positives added to the mixture can be accurately recovered using either method.
These data suggests that this procedure can be successfullyapplied to detected figalactosidase populations in heterogeneous mixtures. Furthermore, the excellent
recovery achieved indicates that loading of the substrate into the cells is relatively
uniform under the employed conditions.
60
3.8 Stin
Ces with 5-Octanminofluorescein di--D-galacpranosidc
3.&l Approach
When the staining procedures developed in the previous sections were applied
to stationary phase cultures of B. subtiis grown in nutrient media without glucose,
very little staining resulted. Since bulk culture assays revealed that P-galactosidase
was present in these cells, this lack of staining reflected a permeability barrier.
In
general it was found that stationary phase cells were considerably less permeable that
growing cells. None of the Cu-FDG series of substrate were able to penetrate these
cells using the two step staining procedure developed in the previous sections.
To obtain information on the distribution of
-galactosidase in these
populations, the protocol had to be modified in order to overcome this permeability
barrier.
Three modifications were found to be necessary; 1) the method of cell
permeabilization was altered, 2) C-FDG was used in place of C12 -FDG and 3) the
staining was performed at room temperature.
3.&2 CFDG
is Optimal
To insure that the fluorogenic substrate had free access to intracellular fBgalactosidase, the cells to be analyzed were permeabilized by vortexing for at least
ten seconds, at maximum speed, in a 1% toluene suspension in PBSMA
Toluene
was chosen over other organic solvents since toluene-permeabilized cells are routinely
used in bulk biochemical assays (Miller 1972, Fisher et a. 1975). When cells were
vortexed for longer periods of time, little if any difference was observed in the ability
61
of the cell to uptake the substrate.
As shown in section 3.8.4, assays performed on
sorted populations indicate that this permeabilization procedure allows C8-FDG to
readily penetrate cells. The solution was then diluted 1/100 by the addition of a
33MuMsolution of C-FDG in PBSMA. The reason for this dilution was to facilitate
the removal of toluene prior to analysis by flow cytometry.
C-FDG was found to have the best penetration and retention properties of
all substrates in the C.-FDG family,where n=2,4,8,12,16. Equal numbers of toluene
permeabilized lacZ positive cellswere incubated with each of the Cn-FDG substrates,
at room temperature, and the substrate penetration ability was assessed by
determining the rate of fluorescence increase of each sample using a
spectrofluorimeter. Figure 3.16A summarizes these results where the relative rates
of fluorescence increase are presented for each substrate for comparative purposes.
These results indicate that C2-, C4- and C-FDG are transported into the cells at
approximately equal rates. Toluene permeabilized cells still managed to exclude C12and C16 -FDG.
The ability of cells to retain the fluorescent product was assessed using the
average single cell fluorescence obtained from each sample using flow cytometry.
This is a useful criterion because flow cytometers measure the fluorochromes level
within or associated with the cell and not the extracellular fluorochrome
concentration. Equal numbers of toluene permeabilized cells, from the same batch
of cells used in the previously described experiments, were incubated for thirty
minutes at room temperature in a 33MMsolution of each substrate. The thirty
62
minute time point was chosen since this represented the last time point analyzed
during the fluorimeter based experiments. Additionally, as will be shown in the next
section, steady state fluorescence distribution had result by thirty minutes. The
results of these experiments are summarized in figure 3.16B.
Presented is the
average single cell fluorescence obtained using each substrate. These data indicate
that C8 -fluorescein is best retained by the cell.
3.&3 Staining Time
In contrast to samples stained with C12 -FDG, samples stained with C8 -FDG
more rapidly reached a stable time invariant fluorescence distribution.
This is
illustrated in figure 3.17 where the time course increase in the average fluorescence
of a sample stained with C8-FDG is presented. After a rapid initial increase in the
average single cell fluorescence, a steady state is reached.
However prolonged
incubation eventually led to the accumulation of considerable levels of free C-
fluorescein as noted by the development of a faint green color in samples incubated
overnight. Based on these data, samples were analyzed approximately thirty minutes
after staining was initiated. Subsequent analysis of stained samples, one or two hours
later, revealed that the thirty minute fluorescence distribution was stably maintained
over a one to two hour time period.
3.84 Heterogeneous Expression in Stationary Phase Samples
The fluorescence distribution obtained from a stationary phase sample of B.
63
subtilis, expressing spoVG-directed fl-galactosidase is heterogeneous (figure 3.18A).
This data was obtained by staining stationary phase cells of MB284 according to the
conditions outlined above (see table 3.1), in the presence of the internal background
control. The results suggest that the culture sample is comprised of two population.
One population expresses high levels of f-galactosidase and the second population
expresses little or no fB-galactosidase. Based on the green fluorescence distribution
of the internal control, the relative sizes of both population was determined (figure
3.18B).
The observed fluorescence distribution reflect differences in single cell
f-
galactosidase content. To insure that the observed signals reflected differences in B-
galactosidase and were not artifacts of the permeabilization procedure, cells from the
high and low fluorescent populations, and the total population, were recovered via
cell sorting and their respective enzymatic activities determined (figure 3.19). The
relative enzymatic activities reported were obtained by assaying 50,000 sorted cells
using the fluorogenic substrate 4-methylumbelliferyl
-D-galactopyranoside (MUG).
These values have been normalized such that the activity of the total population is
unity. MUG was used since the increased sensitivityof this substrate, relative to the
more commonly used chromogenic substrate ONPG, permitted fewer cells to be
assayed in a shorter period of time (Chapter 9).
The bulk culture activity,reflects the activity of the high expressing population.
Using estimates of the sizes of the high and low expressing populations, the
contributions of each population to the total sample activity was determined.
64
Whether the size estimates were determined using a fluorescence gate (67%), set by
the green fluorescence distribution of the internal PKH26 negative control (figure
3.18B), or by curve fitting two distributions through the composite distribution (72%,
see section 9.4.3), the percentage of the total sample activity residing in the high
expressing population is estimated to be greater than 95% (section 4.4).
3.8.5 Contrled
Mixtures
Controlled mixture experiments were also performed with stationary phase
cells expressing P-galactosidase.
Cells expressing spoVG-directed
P-galactosidase
(MB284) were mixed with acZ' cells and the PKH26 internal control and stained
according to the procedures described (table 3.1). Representative green fluorescence
distribution obtained from these experiments are presented in figure 3.20. Similar
to the controlled mixtures presented earlier (figure 3.13), panel A represents the
distribution obtained from a 100% lacZ' population. The other panels correspond
to various mixtures (see legend). These results indicate that the two populations can
be recovered from the controlled mixtures. When the relative proportions of each
of these populations is estimated based on the green fluorescence distribution of the
internal PKH26 negative control, good recovery of the added populations results
(figure 3.21). Because spoVG-lacZ expression was found to be heterogeneous, the
results presented in figure 3.21 account for this heterogeneity. The low or nonexpressing population shown in figure 3.18 was considered to be part of the negative
population.
65
3.86
Correlation Between Fluorescence and Average Enzyme Activity
A correlation was found to exist between the steady state population average
fluorescence
obtained by flow cytometry, using C-FDG,
and the specific /B-
galactosidase activity determined from bulk culture assays. This is illustrated in figure
3.22 where the time course expression of spolIA-lacZ, as determined by bulk culture
assays, is plotted along with the time course average relative fluorescence as
determined using flow cytometry. The units of fluorescence intensity were normalized
by the maximum value, exhibited at the time of peak bulk culture expression. Since
the histograms shown throughout
this chapter were obtained with logarithmic
amplifiers, the fluorescence values reported were obtained after the logarithmic
histograms were linearized (section 8.4.4). Similar results were obtained for other
spo-lacZ fusions (chapter 5). These results are consistent with the theoretical work
of Wittrup and Bailey (1990) where a relationship between enzymatic activity and
single cell fluorescence was derived. Their model also suggests that the steady state
fluorescence distributions obtained (figure 3.17) result from a balance between the
rate of substrate hydrolysis,or product formation, and the rate of product loss.
3.9 Discusion
The principal conclusions derived from the results presented in this chapter
are: 1) that low temperature staining, whether on ice or at room temperature,
alleviates many of the background problems associated with the use of the C-FDG
family of fluorogenic substrates, 2) the PKH26 background control concept provides
66
a means of assessing the background problems that may be encountered during
protocol development, 3) the PKH26 background control provides the means of
controlling for spurious fluorescence when "real" samples are to be analyzed, 4) the
adaptable nature of bacterial cells make it unlikelythat a single staining protocol will
be optimal under all conditions and 5) that the average steady state fluorescence was
found to correlate with enzyme activities determined via bulk culture assays.
A number of workers have found that low temperature staining can be used
to minimize background fluorescence, particularly when larger eukaryotic cells are to
be analyzed (Nolan et al. 1988, Wittrup and Bailey 1988). Since the completion of
this work, Russo-Marie and coworkers (1993) were able to develop a FDG based
technique for staining the Gram-negative bacterium Myxococcus xanthus. Again, this
technique relied on low temperature staining. The commonly provided explanation
behind the success of low temperatures staining is that the cell membrane freezes,
or becomes less fluid, and this serves to prevent the fluorescing product from leaving
the cell. This explanation also appears to apply to cells of B. subtilis stained with Cr2-
FDG. When the Cl2 -fluoresceingenerated fluorescencewas determined before and
after cells were washed with cold and room temperature PBSM, cells washed in cold
PBSM retained more of their original fluorescence.
Given that low temperature staining is essential to minimizing background
fluorescence due to free fluorochromes,
it is likely that any of a number of
permeabilization procedures may be used. In this work, temperature and toluene
were found to be sufficient. Yet a number of permeabilization methods have been
67
successfully used to analyze the attributes of single cells. Isoamyl alcohol (Maloney
and Rotman 1973), triton X-100 (Wittrup and Bailey 1988), and toluene (Rotman
1961) have been used to permeabilize E coil and Saccharomyces cerevisiae. Clearly
the choice will depend on the goals and specifics of the system of interest.
The best feature of the protocols developed in this work is the use of the
PKH26 negative control. Given that low signal to noise ratios can be expected when
bacteria are to be analyzed using flow cytometry, due to their small size, this control
allows the results to be embraced with a certain degree of confidence. However the
ability of the PKH26 positive cells to accurately track background fluorescence relies
on the use of cells that are similar in size to the cells to be analyzed. This reflects
the nature of the tracking process. Since spurious signals result from the association
of free fluorochrome with the surface of the cells care must be taken in order to
minimize size variations that exist between the control cells and the sample cells.
This was accomplished by using stationary phase control cells to track stationary
phase sample cells. It would not be advisable to use exponentially growing cells of
B. subtiis, which exhibit filamentous behavior, to track the accumulation of spurious
fluorescence signals by stationary phase cells, which exist as single cells.
The principal objective of this work was to develop procedures that would
permit flow cytometry to be used to assay the activity of various promoters within a
population of B. subtilis using commercially available equipment. Given that many
of the promoters of interest were active during different stages of a batch culture, it
was necessary to overcome the problems associated with weak signals and high
68
background while maintaining a flexible means of adapting the procedure based of
differences in the cell physiology. The great diversity that exists between bacterial
genera, in terms of cell size, morphology and membrane properties, and the
numerous cultivation conditions and media employed, will most likely require some
modification of the staining procedures described.
However given the relative
scarcity of such information, the conclusions obtained from these studies should
greatly facilitate future developments.
69
Table 3.1 Optimized C.-FDG Staining Conditions
Conditions
Substrate
concentration
C 2-FDG
(M)
C-FDG
33
33
Sample concentration (cell/ml)
106
106
PKH26 lacZ- concentration (cell/ml) l
106
106
500C
(10 min)
1% toluene
(10 sec)
0
25
Permeabilization procedurez 3
Staining temperature (C)
Staining time
8 hours
30 minutes
'These cells represent the internal PKH26 negative control population that was added
to all samples analyzed (see text).
2The heat treatment was performed in the presence of 0.4gn/ sodium azide.
3 Following
the 1% toluene treatment, the cells were diluted 1/100 into the staining
solution. This results in a 0.01 % toluene concentration during sample staining.
70
Table 3.2 PKH26 Staining Conditions
Substrate concentration (M)
Reaction volume (l Diluent C)'
5
200
Cell concentration (cell/ml)
107
Staining temperature (C)
25
Staining time (minute) 2
5
Reaction quencher (g/l BSA solution) 3
20
1 Diluent C is the hydrophobic buffer supplied by the manufacturers of PKH26.
2
During sample staining, the reaction mixture was gently mixed.
3
The reaction mixture was diluted 1/10 in a solution of 20g/l BSA in PBSM.
71
Table 3.3 Spectral properties of fluorochromes commonly used
in fi-Galactosidase fluorogenic substrates
Fluorescent
Excitation'
Emission'
Extinction'
Quantum2
Product
Maximum
(nm)
Maximum
(nm)
Coefficient
(cm-lM-')
Yield
fluorescein
490
514
90,000
0.99
resorufin
571
595
55,000
f-methylumbelliferone
360
450
15,000
B-phycoerythrin 3
540
575
2,410,000
0.98
Obtained from Handbook of fluorescentprobes and researcherchemicals,
Haugland, R., Molecular Probes, Inc., 1992, pp 77,81.
2 Mathies,
3
R.A. and Stryer, L (1986).
Belongs to the phycobiliprotein class of fluorochromes.
P-galactosidase fluorogenic substrate.
72
Not available as part of a
Table 3.4 Staining of 50/50 acZ+/lacZ Mixtures
(Temperature-Time Pulse Loading Matrix)1
Loading Temperature (C)
40
43
45
48
50
52
22
-
-
-
-
-
-
5
-
+-
+
+
+
10
-
+-
+
+
++
++
20
-
+-
--
ND
ND
ND
--
ND
ND
ND
ND
Loading
Time
(min)
40
(-)
(-)
(+-)
(+)
Little staining
High non-specific staining
Two peaks identified
Good staining
(++) Excellent peak separation
(ND) Not determined
Following the heat treatment, the mixtures were incubated on ice overnight then
analyzed by flow cytometry (see text).
73
Positive
Promoter X
lacZ
_
I
C)NA
Beta-Gal
Specific Hydrolysis
Loading
Cn-DG<
Cn-FDG
Non-specific
"<
Product_<
Fluorescent
Hvdrolvsis
. . _.
Negative
Figre
3.1
Problems associated with staining cells with a fluorogenic
-galactosidase
substrate. In addition to the specific signals generated by substrate hydrolysis,spurious
fluorescence can result from free fluorochrome present in solution.
74
AIA
OH
Mn
H(
H
OH
OH
fn.
U'.
He
< 'O
H
tfOHO
OH
0
lpgurs 3.2 Comparison between the molecular structures of FDG (top) and C 2 -FDG
(bottom). The C12 tail provides the fluorescing product with a higher affinity for the cell,
thus increasing the specific signal associated with P-galactosidase positive cells.
75
A
-
1.0
0
0
1.O
a:ID
o
0.5
0
500
540
580
Wavelength
620
(nm)
Fguare 3.3 Emission spectra of fluorescein and PKH26 in ethanol. Excitation was performed
at 488 nm. The differences in these spectra permit both fluorescence signals to be
independently monitored.
76
Positive
C8-FDG
or C12-FDG
Fluorescent Product
Non-specific
Hvdrnlvsin
Non-Specific Staining
PKH26 Negative Control
Negative
Fige 3.4 Schematic illustration of the application of the PKH26 internal negative control.
Cells stained with PKH26 can be used to independently monitor the spurious increase in
green fluorescence due to fluorescein related products free in the staining mixture.
77
Positive
Cn-FDG
0-<
Spontaneous
Fluorescing
Product
Fgure 3.
Schematic illustration of the experiment used to validate the use of the PKH26
negative control. Cell populations, both f-galactosidase positive and negative, were staining
with different concentrations of PKH26 in order to independently monitor the increase in
green fluorescence of each population.
78
&A
s-r
I
141
···
· r······l·l···
· ·
c)
0)
0
r·
'
· · ··
· · · · · · ·
· ·
r II · r r
· ·
· ·
r r·rirYhC···r·
· · · · · ·
r··i
· · r·i···
·
· · ·
· ·
· · · · · ·
· · · · · · · ·
· ·
·····
· · · ·r·· · ·
·
·r
1II
LI·
··
b·
· · I1C·ra··-------· · ·
rrr·r··y-·r-----I
LI L1
· rC ry·
· ·
· · ·
IY·Y··YLIYI···
r·r··
·YYII·
__
· · · ·
· · · ··
f63r··r
L
· · · ·
···I·
r·i·
r·
·
L
··
· · ·
· ··
,· ·
· r I·
_r··
r·
i
·
u·
rr·
· ·
·
I
· · · · ·
· · · · ·
·
· ·
· ·
· · mrYIIYP··llr
··
r··leeLYI·
r·
ri""'
·r··r
··
·· ·· ·r·r· · ·
IICAI-LC
· ·
··L···I·I······
· · ·
· · · ·
··
-C
· ·
· ····
·
ru···· ·r
-- · ·
··
Fluorescence (fluorescein)
Fxge
3.6 Two-color contour plot used representing the populations described in figure 3.5.
The fluorescence increase of the two lacZ- populations, which contain low PKH26
fluorescence (i.e. bottom two populations), was monitored. Since the mixture contained fBgalactosidase positive cells, the fluorescence increase of the bottom two populations resulted
from both dye leakage and spontaneous substrate hydrolysis.
79
4
C
I,
00
0I.
U
3
L.
0
2
0
i0
0
Cr.
aD
1
(R
0
0
1200
2400
3600
Time (sec)
Figure 3.7 Time course fluorescence increase of the lacZ- populations shown in figure 3.6.
80
50
la
0
L
r
40
0
at
0
(4
.
30
4
O
0
a.
a-
(pJ
4-.
C
e0
IX.
20
10
L.
0
CL
0
0
10
20
30
40
50
Percent PKH26 Positives Added
Figur 38 Recovery of PKH26 positive cells from mixtures containing PKH26 positive and
negative populations. The abilityto recover the knownPKH26 positivepopulations indicates
that the dye is stably maintained by cells.
81
100
75
,.
25
O
0
20
40
60
80
100
120
20
40
60
80
100
120
100
4.
10
<10
0
Time (min)
FIge
3.9 Time course decreases in l-galactosidase activity following incubation at various
temperatures (A). The fact that the decay curves exhibit straight lines when plotted on a
semi-log scale (B) indicates that the decay process is first order in //-galactosidase.
82
300
C
E
0
-
2000%X
E
-
80%
.0
100 90%
C
0 I
45
_
I
i
.
i
50
.
·
55
Incubation Temperature (C)
Figue 3.10 Deactivation contours generated from the decay kinetics presented in figure
3.9B. The area bounded by the X-axis and each line delineates the temperature and time
domain where at activity can be maintained at least above the value indicated (i.e. the
percentage of the original activity indicated by the various lines).
83
80
60
C
E
U
IC
S
U
a,
40
0L
o
m:
20
0
0
10
20
30
40
Incubation Temperature
50
60
(C)
Figum 3.11 Average rates of fluorescence accumulation obtained from a series of constant
temperature experiments performed with C 12 -FDG. -galactosidase positive and negative
populations were individuallyincubated in the presence of the PKH26 control population and
the average rate of fluorescence increase for all four populations determined.
84
0so
A
40
20
0
0
C,)
4-'
C-
200
400
600
600
1000
200
4000
0 0
800
1000
400
600
800
1000
60
B
a)
LLJ
40
0
20
E
z
0
DU
0
C
40
20
0
0
200
Channel Number
Figuir 3.12
Green fluorescence distributions resulting from 50/50 fi-galactosidase
positive/negative mixtures stained according to the two-step staining procedure (see text).
Panel A illustrates the case where little staining resulted. Panel B illustrates the case where
two populations are somewhat detectable. Panel C illustrates the case where the two
populations can easily be identified.
85
75
A
60
45
i
0
15
..
0
200
400
600
600
_
4-
1000 0
. .
"1
a)
LU
0
200
400
600
200
400
600
200
400
600
S00
1000
D
60
45
a)
-o
E
zZL
15
0
0
200
400
600
500
1000 0
o00
1000
75
F
60
30
15
0
0
200
400
600
00
1000 0
Channel Number
Figure 3.13
Representative
600
1000
Channel Number
green fluorescence distributions obtained from a series of
controlled mixtures stained with C 2-FDG under optimized conditions in the presence of the
PKH26 negative control. The -galactosidase positive population added to each sample is
0% (A; i.e. 100% lac,),
5% (B), 25% (C), 50% (D), 75% (E) and 100% (F).
percentages do not include the PKH26 background control population.
86
These
CA
cMj
4
I-r
···
··
· · · r·r
·
r
C.
..
· ··
· ··
· ·)·.
ICII
r·
=i;
32
..
c=
· r·rr
C)
·
I······ ·
o
0
· ·
·
·
· II· · ·.. I
··
.··.
· · · ·
·
*·
16
·
·.·
·······..·
·
·
·
· ··
I,
.·
II
r··
·
I· ..
· · .·.·.
· · ·
· · ·
·
··
·
·....
11
r
r
·
,·
·
· ·
r··
· · r····,
· · ,··r·
r······
· r
· · · ·
·
· ·
·
·
· ·
·
· ·
·
· ·
r
· I
· I
·
·
·
I·
······
· · · · · r····
·. · · · r·r·
·. ·
111·..
III····I··
· r···i·I
r·····
r·
·
·.
· ·
·
r
·
·
· ·
· ·
· ·
· r
·
· r
I·
··
,
·
r
.
·
····
· ·
· · ·
· ·
·
· · · I.
..· · · · ·
r
r
·
I·
··
·
·
· I
· · · ·
·
·. ·
·
·· ·
.r·
· ·
rr
.ri·
·
· II
r I
r
·
· · · r·
r··rii
r
· · ri
·
· ·
· · · ·. · ·
·
·
.····,··
·
·
.
·
·
· · · ·
·
I
16
r
r
r
fluorescein)
Fluorescence (fluorescein)
Figure 3.14 The two color contour plot representing the 50/50 mixture
shown in figure
3.13D. The top population represents the PKH26 negative control. The
bottom two
populations
resulted from the 50/50 controlled mixture.
87
1.0
L-
O
O
0.8
00
a:
C
0.6
0
0
I.
0.4
0
0
0.2
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Positive Fraction Added
Figure 3.15 Quantitative summary of the populations recovered from controlled mixture
experiments. The distributions presented in figure 3.13 were analyzed either by the use of
fluorescence cutoffs, set by the green fluorescence distribution of the PKH26 negative control
or by curve fitting (see chapter 8). Both methods yield approximately equilvalent results.
88
1.2
I
-
A
1.0 -
U
ab nlA
L-
I
0.6-
O
.
0.4-
.I
,
0.2An IIV_.
I
I
I
I
C2
C4
C8
C12
C16
I
I
I
C2
C4
C8
V
A
.
%q
I.Z -
B
VI1.0I
o
0.80.6-
0.4 ._
-
Ot
0.2-
nn
!l
IJ
L
--
M
~--r-I
C12
I
C16
FDG Hydrocarbon Tail
Figure 3.16 Summary of C.-FDG screening experiments used to identify C-FDG as the
optimal substrate for staining sporulating cultures of B. subtiis. Panel A represents the rate
C.-FDG hydrolysis obtained using a spectrofluorimeter. Panel B represents the average
steady state fluorescence obtained using flow cytometry.
89
AA
UV
C
60
I
I,
I
0
IIL
40
.__
c: 20
0
0
5
10
15
20
25
Time (min)
Fgwe 3.17 Time course increase in the average fluorescence, due to Cs-FDG hydrolysis,
obtained using flow cytometry.
90
80
A
67X Above
C
Background
60
LU
0 40
.0
E
z
20
0
0
200
400
600
800
1000
Channel Number
..
W.V
B
67% Above
Background
4S
" ..
i·
· ,11
·
......
e'
.
. :
·:
.,
%. · ·~~~~
.:I,.
· · oil··· ·
o
"..
'-
....
...
....
..
......
...
e
.
.·
·
....
.
,w
.......
x
·
^YI~
............ @........
.....
...........
.....
..
~
.-
·-*· i-,-
..
:
ii
'.
is
*.---:;
· ·
-
@
^
-
*s__-@el
@@@@@@
·· ·
e@
@
.,.r
··· ·
_
....
:11
_.......
ii......
.....
@-e·-@@
@
... 4b
@@*_
e
@
r···
@
o
YT@@
@
*
@@
@--w
@
@
_··
__
__
I.
___
,U
I
l,
Fluorescence (fluorescein)
gue 3.18 C-fluorescein generated fluorescence distribution obtained from cells containing
a spoVG-IacZ fusion (A) in the presence of the PKH26 negative control. The PKH26
negative control population is shown in the corresponding two color contour plot (B).
91
75
A
a44C
0
w
Bottom 26%
:W 50 L
Top 54 I;
sorted
sorted
'6-
0
-
e
25 -
E
z
0
l
-
200
0
400
-
-
800
600
1000
Channel Number
1.5
B
1.0
-
2:a3
0
0.5 -
'U
-i
0.0
-~~~
-
-
Total
High Expressing
Low Expressing
Cell Populations
Fgure 3.19 Summary of the cell sorting results obtained from the sample presented in figure
3.18.
92
100
U
4-
C
0
w
A
60
4-
0
0
40
-0
E
z
20
0
0
200
400
600
600
1000 0 '
200
400
200
400
600
800
1000
an~~~~~~~~'
.
D
C
0
w
0-
40
30
0
20
E
10
0
z
0
0
200
400
600
00
1000 0
Channel Number
Figure 3.20
Representative
600
600
1000
Channel Number
green fluorescence distributions obtained from a series of
controlled mixtures stained with Cs-FDG under optimized conditions in the presence of the
PKH26 negative control. Thef-galactosidase positive populations added to each sample are
0% (A; i.e. 100% lacZ, 21% (B), 49% (C) and 67% (D). These percentages do not include
the PKH26 negative control population.
93
75
l,
0
60
0
45
0
0a,
0a
30
C
0
L.
15
0
0
15
30
45
60
75
Percent Positives Added
Figure 321 Quantitative summary of the populations recovered from the controlled mixture
experiments.
These results were obtained from the distributions presented in figure 3.20
using a fluorescence cutoff determined by the green fluorescence distribution of the PKH26
negative control (3.18B).
94
30
·
P
1.0
N
4.'
:W
a,M
C)
0
20
Co
.-
1.0 <
o-n
a.
co
a
£0
o
(n
0
o 10
0.5
a
l
0£0
'U
0
a)
4,
I,
nn
0
0
2
4
6
8
10
Time (hr)
FIgurm3.22 Time course correlation between the P-galactosidase specific activityobtained
from bulk culture assays and the culture average fluorescence obtained by flow cytometry.
,8-Galactosidase activity was driven from the spolIA locus (KI938; chapter 9).
95
Chapter 4: Caraterization
of Stationary Phase Populations of R subtiis
4.1 Introduction
The hypothesis advanced in this work is that cell population heterogeneity
plays a significant role in determining culture behavior. If this is indeed the case, it
should be of particular concern as the culture enters the stationary phase. The
reasons for this assertion is that upon entering the stationary phase, cells initiate a
number of adaptive response mechanisms in order to adjust to the changing
environmental condition of nutrient limitation. Included in these responses is the
induction of a number of industrially important degradative enzymes, as well as those
genes needed for spore formation.
In this chapter the techniques developed and described in Chapter 3 are used
to characterize the populations associated with stationary phase cultures of B. subtilis.
Experimental evidence is presented that demonstrates that culture heterogeneity is
established very early during the stationary phase. Additionally, though unobservable
by bulk average measurements, this heterogeneity plays a significant role in explaining
culture behavior. These conclusions follow from the finding that expression of some
of the earliest sporulation or stationary phase marker genes is restricted to a
subpopulation of cells. The simplest interpretation of these results is that two distinct
cells types are generated. Furthermore, once these differences are established, each
cell type adopts a different developmental program. Evidence in support of these
statements was obtained by analyzing single cell gene expression in cultures of B.
96
subtilis using spo-lacZ fusions, the fluorogenic substrate C8-FDG, and flow cytometry.
The results of these experiments indicate that the expression of early sporulation
genes is cell-type specific.
These finding suggests that induction
into the
developmental pathway of spore formation is and all-or-none phenomenon. Such
behavior is consistent with the existence of threshold-like molecular mechanisms that
control the cell's decision to initiate the developmental process.
4.2 Stationary Phase Protein Expression
Great advances in our understanding of the molecular interactions that control
stationary phase protein expression have been made over the past few years
(Burbulys et a. 1991, Grossman 1991).
While much still needs to be clarified
regarding the sensing mechanisms used by the cell (i.e very upstream elements), it is
now known that the environmental and physiological signals needed to activate
stationary phase gene expression are transmitted via the transfer of inorganic
phosphate through a multicomponent phosphorelay (Burbulys et al. 1991). When the
appropriate
conditions are satisfied, which include nutritional,
cell cycle and
intercellular factors, the principal role of the phosphorelay appears to be the
activation, vWaphosphorylation, of the important transcription factor encoded by
spoOA
Once activated, SpoOA-P plays a major role in coordinating the cell's
transition from exponential growth to the stationary phase.
One of the earliest functions of SpoOA-P, is to indirectly activate the
transcription of spoVG and spoOH,among other genes, by repressing abrB (Perego
97
et al. 1988, Strauch et al. 1990). AbrB is a constitutively expressed DNA binding
protein that represses spoVG and spoOH(Perego et al. 1988, Strauch et al. 1990),
among other genes. Although the biological role of the spo VG product has not been
determined, its pattern of expression has made it an excellent marker for monitoring
the initial events associated with the developmental process. In contrast, spoOH
encodes for the sigma factor, ao , which is absolutely required for spore formation
(Weir et al. 1984, Yamashita et al. 1986, Dubnau et al 1987, Weir et al. 1991).
Following the activation of spoVG transcription, SpoOA-P directly activate
transcription from the spolIA locus (Trach et al. 1991). Transcription from this locus
is directed by the RNA polymerase complex containing aH. Since both phospho-
SpoOA and a
are required for spollA transcription, the pattern of spolIA
transcription can be used to mark developmental events occuring after the initiation
of spoVG transcription. The spolIA locus encodes, among other proteins, the
sporulation specific sigma factor, uF, which is needed to establish differential gene
expression in the mother cell and the forespore (Losick and Stragier 1992). This
temporal
sequence is illustrated in figure 4.1.
In the following sections, the
expression of spoVG-lacZ and spoIIA-lacZ are monitored in detail.
4.3 Single Cen Ge
Exprsion
apVG-lcZ
Due to the central role that SpoOA plays in regulating the transition from
exponential growth to the stationary phase, and its absolute requirement for spore
formation, I sought to identify how the products of genes under SpoOAcontrol were
98
distributed throughout the cell population.
Such information was considered
important since the transcription of genes under SpoOAcontrol represent some of the
earliest stationary phase phenomena. Therefore such knowledge could help to
localize the stage, in the sequence of developmental events, where physiological
differences between sporulating and non-sporulating cells might be established. This
was accomplished
using spo-lacZ fusions in conjunction with the cell staining
techniques developed and described in the previous chapter.
spoVG-lacZ expression is induced early during the stationary phase. Figure
4.2 presents the growth and P-galactosidase specific activity profiles obtained from a
batch culture of B. subtilis(MB284; spoVG'-'lacZ) grown in nutrient broth (Schaeffer
1965). Cell growth was monitored using turbidity measurements at 660nm. The Pgalactosidase
specific
activity
reflects
the
amount
of
o-nitrophenyl
-D-
galactopyranoside (ONPG) hydrolyzed by the "average" cell in the culture (i.e. it
reflects a culture average measurement; see Chapter 9 for an explicit definition). galactosidase expression was directed from the spoVG promoter.
In order to
eliminate ambiguities associated with the fermentation time, it is customary to define
a time scale relative to the point at which the culture departs from exponential
growth. This convention was used in all experiments.
The time course profile of
spoVG-lacZ expression, presented in figure 4.2, indicates that 1) spoVG is expressed
at low levels during exponential growth, 2) spoVG-IacZ expression increases rapidly
soon after the culture enters the stationary phase and 3) spoVG-lacZ expression is
transient, as noted by the decline in specific activity curve. Such results are consistent
99
with those obtained by other researchers (Zuber and Losick 1983, Mueller and
Sonenshein 1992).
Variations in bulk culture specific activity measurements reflect variations in
the population demographics (figure 4.3).
In addition to bulk culture activity
measurements, samples at the indicated intervals were stained with C8 -FDG
according to the protocol described in the previous chapter. The purpose behind this
measurement was to determine whether the specific activity, as measured using bulk
average assays, reflected the behavior of all cells or of a subpopulation of cells. The
parallel profiles exhibited by the curves representing the size of the expressing
population and the bulk culture specific activity indicate that bulk culture variations
in the specific activity reflect variations in the size of the expressing population.
Percent positive refers to the percentage of cellsthat were expressingf-galactosidase
as judged by green fluorescence above background. Since the total cell numbers
change little, if any, during the first several hours of the stationary phase in nutrient
broth media, the percentage of the population staining positive reflects the absolute
size of the positive population.
Bulk culture measurements of spoVG-lacZ expression represent averages
taken over a very heterogeneous population. The green fluorescence distributions,
originating from C8 -fluorescein, obtained from samples withdrawn at different times
during the stationary phase are presented in figure 4.4. Panel A represents the
fluorescence distribution obtained from a sample harvested at time zero or To. This
distribution indicates that heterogeneity is manifested very early during the stationary
100
phase. Also shown are the distributions obtained from samples harvested two and
four hours into the stationary phase as denoted by T2 (B) and T4(C). Panel D shows
the background fluorescence distribution associated with the T2 samples (i.e. the
PKH26 negative control). The bimodal nature of these distributions indicate that the
culture is comprised of two populations. One populations expresses relatively large
amounts of f-galactosidase and the other expresses little if any.
The distributions shown in figure 4.4 indicate that /-galactosidase is-unstable
in stationary phase cells of B. subtilis grown in nutrient broth. Initially, between To
and T2, the size of the expressing population increases.
This is reflected by the
increase in the area under the peaks at high fluorescence. Since the total cell
numbers remained essentially constant from T 2 to T 4, gains in the size of the non-
expressing population must have occurred at the expense of the expressing
population. The degradation of f-galactosidase in sporulating cultures has also been
found by other workers (Errington and Mandelstam 1986). Yet, the technique
employed in these experiments provides a non-intrusive means of acquiring this
information.
Previous studies have relied on the use of protein synthesis inhibitors
such as chloramphenicol.
4.4 spoVGO-acZce Sorting
The fluorescence distributions obtained via flow cytometry reflect true
differences in the level of intracellular P-galactosidase activity. The experiment
described in the previous section was repeated in order to identify the level of P-
101
galactosidase present in the low expressing population. The T2 sample, which
corresponded to the peak in bulk culture
-galactosidase expression was stained,
analyzed and sorted by flow cytometry. Similar to the results described in the
previous section, a bimodal distribution resulted (figure 4.5). The high expressing
population was estimated to comprise 67% of the total population, using a
fluorescence cutoff set by the green fluorescence distribution of the PKH26 negative
control (figure 3.18). Cells from both populations were sorted, according to the
fluorescence gates shown, and the recovered populations were assayed for Bgalactosidase using the fluorogenic substrate 4-methylumbelliferyl
-D-galactoside
(MUG; see Chapter 9). Due to the well separated emission spectra exhibited by
MUG and fluorescein (the emission maxima exhibited by these compounds being
450nm and 514nm respectively), use of this assay was possible without interference
due to fluorescein. The greater sensitivity afforded by this assay (Harwood and
Cutting 1990, Fiering et al. 1991) permitted as few as 5000 cells to be analyzed.
Sorting analysis revealed that bulk culture specific activity measurements,
obtained using the chromogenic substrate o-nitrophenyl-f-D-galactoside (ONPG)
reflected the B-galactosidasecontent in the high expressing population. MUG assays
revealed that the high expressing population expressed greater than twenty fold more
f-galactosidase than the low expressing population on a per cell basis (inset to figure
4.5). The relative activities reported were obtained from the rate of fluorescence
increase of each population as determined using a spectrofluorimeter, due to MUG
hydrolysis, on a per cell basis (see Chapter 9 for more details). Each measurement
102
was obtained by assaying 50,000 cells. Normalized activities are reported relative to
the specific activity of the total cell population, which was arbitrarily assigned a value
of one. Based on estimates of the relative sizes of each population, 67% percent
high expressing versus 33% low expressing, the contributions of each population to
the total sample activity was determined (figure 4.6). These results allowed the
conclusion that greater than 95% of the total culture activity resided in the high
expressing population.
Inherent in this analysis is the assumption that the specific
activity of the top 54% of the sample population (i.e. the population that was actually
sorted) reflected the specific activity of the top 67% of the population, as estimated
using a fluorescence cutoff.
45 Sinl CellGeneE
ixpress
spoli-lacZ
The results presented in the previous sections indicate that physiologically
distinct cell populations appear very early during the stationary phase.
If the
heterogeneity observed in cultures expressing spoVG was sporulation specific, then
heterogeneous expression of spoIIA-lacZ would be expected. This follows from the
fact that transcription from this locus is known to occur after spoVG transcription.
To investigate this possibility, experiments similar to those described in the previous
section were performed on cells containing a spoIIA-lacZ fusions (MB296, spoIAlacZ::pPP81; see Chapter 9).
Cultures expressing spoIIA-lacZ were found to be heterogeneous being
comprised of high and low expressing populations. The time course profiles of the
103
bulk average specific activity (ONPG assays) and the proportion of the population
expressing f-galactosidase, as directed from the spoIIA locus, are presented in figure
4.7. The observation that the size of the expressing population, at the point of
maximal bulk culture expression of spolA-lacZ, is similar to the size of the
expressing population obtained from cultures expressing spoVG-lacZ suggests the
cells that earlier expressed high levels of spoVG-lacZ correspond to the population
that later expressed high levels of spoIIA-lacZ. Together, these results suggest that
the observed heterogeneity is a sporulation specific phenomenon.
As in section 4.3,
it is possible to conclude: 1) variations in bulk culture activity reflect variations in the
size of the high expressing population, 2) bulk culture activity measurements
principally reflect the behavior of a cell subpopulation
and 3) spoIIA-acZ
is
transiently expressed.
It is interesting to note that in contrast to spo VG-lacZ expression (figure 4.3),
the decay time associated with the size of the population expressing spoIIA-lacZ is
longer. This indicates that spoIIA-lacZ expression continues over a longer period of
time, relative to spoVG-lacZ expression.
4.6 Correlation Between Spores and the Expressing Cell Populations
Since the spoVG and spolA gene products are needed for spore formation,
the previous results suggests that the cells expressing high levels of f-galactosidase,
as directed from these loci, are the same as those that proceed to form spores.
Therefore,
a series of experiments
aimed at investigating this hypothesis was
104
performed.
If this hypothesis were true, a correlation between the size of the
populations expressing various spo-lacZ fusions and the spore population would be
expected.
A correlation exists between the number of cells expressing spoVG-lacZ and
spollA-lacZ and the number of spores. Figure 4.8 presents the time course profiles
of the number of cells expressingfi-galactosidase as directed from the spoVG(A) and
spoILA (B) loci and the total number of spores. Initially, the size of both positive
populations attains a maximum, then eventually decreases. This was to be expected
since these loci were found to be transiently expressed (sections 4.3 and 4.5).
However, the fact that the decline in the number of cells expressing fi-galactosidase
mirrors the later appearance of spores, strongly suggeststhat the cells that developed
into spores earlier expressed these sporulation genes. It is interesting to note that
when the sporulation frequency is compared to the fractionof the population that
earlier expressed spoIIA-lacZ and spoVG-lacZ a correlation was also found (figure
4.9).
A correlation between the fraction of the population developing into spores
and the fraction of the population that earlier expressed a variety of spo-lacZ fusions
was also found. Using different genetic backgrounds and different spo-lacZ fusions
(figure 4.10), single time point comparisons were made between the sporulation
frequency (at T12 for SMY strains or T18 for JH642 strains) and the proportion of the
culture that earlier expressed f-galactosidase. Each datum shown in figure 4.10 was
obtained from an independent experiment performed using one of four genetic
105
backgrounds (SMY, JH642, JH642 kinA::Tn917 and JH642 spoOKAC) and one of
four spo-lacZ fusions. Plotted against the sporulation frequency is the percentage of
the culture that earlier expressed P-galactosidasefrom the indicated spo-lacZ fusion.
The time point at which the various samples were analyzed using flow cytometry
coincided with the point of maximal bulk culture expression. Depending on the spolacZ fusion, this varied between T 2 and T4. To obtain cell lines that exhibited low
sporulation frequencies, oligosporogenic mutants (i.e. cell lines exhibiting reduced
sporulation compared to wild type) were employed. This included both kinA and
spoOKmutants. Despite the use of a variety of strains and spo-lacZ fusions, and the
fact that single time points, separated by well over ten hours, were compared, this
correlation appears quite good.
4.7 Dicussion
The immediate conclusions that can be drawn from these studies are: 1) that
bulk measurements of gene expression, among other variables, in cultures of B.
subtiis reflect averages taken over heterogeneous populations, 2) that heterogeneity
results from the inability of certain cells in the population
to initiate early
developmental gene expression 3) that the subpopulation of cells that express various
spo-lacZ fusions appear to be responsible for the appearance of spores and 4) that
incomplete sporulation results from the inability of cells to express some of the
earliest genes associated with the developmental process. The implications of these
results, in terms of the processes involved in initiating development are further
106
discussed in Chapter 5.
These conclusions suggest that entrance into the sporulation process is an all
or none process.
This is precisely what would be expected if threshold-like
mechanisms control the initiation process. Furthermore, the decision made by the
cell to initiate development occurs very early during the stationary phase. Such a
conclusion is in direct contrast with the view that all cells express early sporulation
genes and incomplete
sporulation
results from abortive processes
along the
developmental pathway. As alluded to in Chapter 2, the actual situation probably
involves elements of both views. It will be shown in the next chapter that the ability
of the cell to induce expression of one of the earliest observed sporulation genes,
spoVG, is necessary but not sufficient for the cell to develop a spore. Such a finding
is in agreement with the hypothesis that multiple threshold barriers must be
overcome by the cell in order to successfullycomplete endospore formation.
This chapter is concluded with a speculative observation regarding the inability
of all cells in the culture to initiate spore formation. A explanation as to why high
levels of P-galactosidase, as directed from different spo loci, are induced in only a
fraction of the culture could involve cell-cycledependent factors (Mandelstam and
Higgs 1974, Dunn et al. 1978, Ireton and Grossman 1992). Mandelstam and Higgs
(1974) found that there exists an optimal time within the chromosomal replication
cycle during which cells can be induced to sporulate. About fifteen minutes into the
cycle, induction into the sporulation pathway was found to be optimal. Since cells in
the culture would be expected to be at different stages of the chromosomal
107
replication cycle, uniform induction might not be expected.
Theoretical analysis performed by Dunn and coworkers (1978), based on the
concept of an optimal induction time, allowed the prediction that a maximum of 66%
of the population could be induced to sporulate at any one time. To arrive at this
number, Dunn and coworkers (1978) assumed: 1) that each cell contained multiple
replication forks and hence chromosomes, according to the Cooper-Helmstetter
model of chromosomal replication (Cooper and Helmstetter 1968), 2) that cells
whose chromosomes had been replicated past the critical point were unable to
sporulate, unless a new round of replication could be initiated and 3) that the
chromosomal replication and the cell doubling times are 55 and 40 minutes
respectively (for growth on hydrolyzed casein at 37C). Applying the age distribution
theorem of Sueoka and Yoshikawa (1965), the 66% number was calculated. It is
interesting to point out that when P-galactosidase, as driven from both the spoVG
and spoIlA loci, was detected in single cells of the highly proficient sporulating cell
line SMY, the maximum fraction of the population staining positive ranged from 6070% (figures 4.3, 4.5, 4.7, 4.9).
108
SpoOA
Stationary Phase ---------
(Transcription Factor)
-
Signals
Nutritional
Cell Cycle
SpoOA-P
Intercelular
abrB
-I-
-
spollA
spoOH
l
I spoVG|
+
)I.-
Activation
Repression
Figure 41 Schematic illustration of a subset of the events set in motion followingSpoOA
activation. As illustrated, both spoVG and spolE4 are under SpoOAcontrol.
109
10
0l
200 o
0
CA
E
To
C
0
(0
ff
(0
a:
1
0uC,
4-1
n
CD
C
'j)
0
100 o_.
0.1
0
0
t
4-'
0
0.01
-4
,<
I
I
I
I
I
-2
0
2
4
6
0
_
8
Time (hr)
F'gure 4.2 Time course profiles of growth (;OD60) and -galactosidase expression (0) as
directed from the spoVG locus.
110
100
I
L)
75
a
CD
0
0
U)
CD
100 o
a_
O
0o"
25
O
-2
O0
_.
I
I
I
I
0
2
4
6
0_,.,8
Time (hr)
Figure 43 Time course profiles of the culture average gene expression (spoVG-lacZ) and
the percentage of the population staining positive for L-galactosidase.
111
40
60
B
A
38% Positive
73% Positive
40
0
20
4-
20
0
0
60
200
400
600
0
800
1000 0
200
400
1000
D
4% Positive
0)
w
0
800
40
C
47% Positive
C
600
40
20
L
o
.0
E 20
z
0
0
200
400
Charnel
600
800
0
1000 0
~Naber
200
400
600
800
1000
Charnel Nunber
Figue 4.4 Green fluorescence distributions obtained from culture samples harvested at To
(A), T2 (B) and T4 (C) (figure 4.3). Panel D represents the green fluorescence distribution
of the PKH26 negative control present in the T2 sample.
112
100
Specific Activity
0
C 75
4..m
1
Total Sort
1.4
High sort
0.07
Low sort
I.-
26 Low Sort
50
0
L.
-54X
High Sort
t.-o
E
z
25
0
0
200
400
600
800
1000
Channel Number
Figure4S Green fluorescence distribution obtained at T2 from a culture expressing spoVGlacZ Also shown are the fluorescence gates used during cell sorting and the relative specific
activity of each sorted cell population.
113
100-
7
101'
U
75 E
(0
50so
0
25 -
0
-l
L
I- - -
-
____1
Total
High
Expressing
I
r
-
t
Low
Expressing
Figure 4.6 Summary of the relative contributions of the sorted cell populations to the total
culture average specific activity.
114
100
30
C)
I
75
0
Z0
.,m
0
0
2o
0
50
0
To
Q.
10e
_,.
10
25
.
v
0
-2
0
2
4
6
8
Time (hr)
Figure 4.7 Time course profiles of the culture average gene expression (spoIIA-lacZ) and
the percentage of the population staining positive for P-galactosidase.
115
600
co
I
w 400
0
E
0
E
200
0
0
5
10
15
20
0
5
10
15
20
600
to
I
w 400
O
L.
a
E
200
z
0
Time .(hr)
Figum 4.8 Time course profiles of the P-galactosidase positive () and the spore (O)
populations obtained from cultures expressing spoVG-lacZ (A) and spoIlA-lacZ (B).
116
100
0
Lo
75
C)
Cn
la
0
ct
O)
50
0
.a,
0
cl
14
A_
25
)Q
._
0
0
5
10
15
20
0
5
10
15
20
100
0
L
o
75
la
C,
0
0
enr
c
50
0.
25
0
N_
0
Time (hr)
Fgure 4.9 Time course profiles of percent f-galactosidase positives (0) and spores (O)
(spoVG-lacZ A and spoIA-lacZ; B).
117
ad
9
IUU
10
0
o0.
)
.1
1
.1
.1
1
10
100
% Positive
Figure 410 Plot of the percentage of the population expressing -galactosidase, from a
number of spo-lacZ fusions, versus the sporulation frequency. The proportion of the
population expressing -galactosidasewas evaluated between T2 and T4 near the peak bulk
culture expression. The sporulation frequency was determined at least twelve hours after
exponential growth ceased.
118
Chapter 5: Genetic Analysis of Threshold Phenomena
5.1 Introduction
In Chapter 4, the techniques developed in Chapter 3 were used to characterize
the cell populations present in stationary phase cultures of B. subtilis. One of the
major conclusions reached is that culture heterogeneity, associated with the
expression of early sporulation marker genes under SpoOA control, is established
soon after the culture enters the stationary phase. These results imply that processes
involved in activating SpoOA, or processes acting further upstream, are responsible
for the observed heterogeneity.
In this chapter, single cell measurements of gene expression are combined with
genetic perturbations to explore how processes involved in generating phosphoSpoOA affect culture heterogeneity. The approach employed involves identifying how
null mutations or deletions in stationary phase signal transduction genes, known to
interact with the phosphorelay, alter the distribution of P-galactosidase as expressed
from genes known to be under SpoOA control. The signal transduction genes
involved are kinA and spoOK Since these mutations reduce the level of activated
SpoOA in cells, the observed effects will allow us to infer how changes in the level of
activated SpoOA affect gene expression.
The characterization of the kinA (previously spoIIJ) gene product has recently
led to a revival of the threshold hypothesis (Antoniewski et a 1990). kinA null
mutations and spoOKC deletions cause partial defects in sporulation and reduced
expression of early sporulation genes (Antoniewski et al. 1990, Perego et al. 1991,
Rudner et al. 1991, Mueller and Sonenshein 1992). The reduced expression of these
genes could be due to a decrease in expression in all cells and/or a decrease in the
number of cells expressing these genes. The results presented in this chapter indicate
that mutations in kinA and spoOK reduce the size of the cell subpopulations
expressing certain early sporulation genes. Furthermore, the resulting subpopulations
express at wild type (spolIG) or near wild type (spoIIA) levels, as determined using
flow cytometry.
These results support the hypothesis that SpoOA-P, or some
component of the phosphorelay, must reach a critical threshold in order to activate
early sporulation gene expression. Since the size of the populations expressing early
sporulation genes correlates with the proportion of cells developing into spores
(figure 4.10 and this chapter), this threshold appears to represent one of the major
hurdles that cells must overcome for successful spore development.
5.2 Stationay Phase Signal Tansduction and Gene Expression
When certain cell-density,nutritional and cell-cycle conditions are met, cells
of the Gram-positive bacterium B. subtilis can differentiate into heat-resistant
endospores. One of the key events controlling the initiation of sporulation is the
activation, via phosphorylation, of the developmental transcription factor encoded by
spoOA Phosphorylation of SpoOA involves the sequential transfer of phosphate
through a phosphorelay comprised of the products of kinA, spoOF and spoOB(top
of figure 5.1; Burbulys et al. 1991, Grossman 1991). KinA autophosphorylates and
120
donates phosphate to SpoOF. The phosphate is then transferred to SpoOB, and
finally to the SpoOAtranscription factor. In addition to kinA, there appears to exist
at least one other protein kinase, KinB, that serves as a source of phosphate for
SpoOF (Burbulys et al. 1991). KinA is the best characterized (Antoniewski et al.
1990, Perego et al. 1989).
The phosphorelay's involvement in the sporulation process appears to be the
generation and possibly amplification of phospho-SpoOA. In contrast to other
components of the phosphorelay, sporulation is absolutely dependent upon the
SpoOAgene product. Double mutations that bypass the sporulation defects exhibited
by kinA, spoOB and spoOF mutation have been identified (Shoji et al. 1988,
Kawamura and Saito 1983). Such mutations have been shown to be alleles of spoOA
that lead to alterations in the N-terminus of SpoOA. Additionally,mutations in spoOB
and spoOF can be partially overcome through kinA over expression (Perego et al.
1989). These results support the notion that the kinA, spoOB and spoOF gene
products act upstream of SpoOAand play a major role in regulating its intracellular
concentration.
Recent evidence suggests that multiple environmental and physiological signals
are integrated through the phosphorelay network (Burbulys et aL 1991, Grossman
1991, Ireton et al. 1993, Bird et aL 1993). Nutritional, cell density and cell cycle
signals have been found to be integrated through the activation of the SpoOA
transcription factor (Ireton et al. 1993, Olmeda et al 1990). Through the isolation
of spoOA mutations, resulting in various deletions or alterations in the N-terminal
121
region of SpoOA, transcriptional activation of spollA and spollG can bypass the
normally required nutritional and cell density signals.
Additionally, an altered
function mutation in spoOA, rvtAll, can bypass the dependence of early sporulation
gene expression on cell-cycle or DNA replication signals (Sharrock et al. 1984).
The signal integrating function of the phosphorelay is further supported by the
finding that the oligopeptide permease transport system encoded by the spoOK
operon also provides signal inputs into the phosphorelay (Rudner et al 1991,Perego
et al 1992). While the specific role that this operon plays has yet to be identified,
mutations in spoOK have been found to result in partial sporulation defects.
Additionally, over-expression of kinA can partially overcome mutations in spoOK
(Rudner
et al. 1991).
The model presented
in figure 5.1 summarizes these
observation.
Once activated, SpoOA serves to coordinate the cell's entrance into the
sporulation pathway by setting in motion a series of developmental events that
eventually result in the cell's "commitment" to the developmental pathway.
The
genes activated by SpoOA-Phave been discussed in great detail in Chapter 2 and are
summarized in figure 5.1. The bottom part of this figure illustrates that upon SpoOA
activation, a sequence of well regulated and temporally separated events are set in
motion.
The temporal sequence includes: 1) abrB repression, 2) spoVG
transcription, 3) spolIG and spolIA transcription, 3) spolID transcription and 4)
eventual spore formation. In the followingsections, single cell measurements of gene
expression are used to track cells along this developmental sequence.
122
5.3 kinA Null Mutations: Culture Average Behavior
5.3.1 spaVG-lacZ
Recent results indicate that null mutations in the kinA loci lead to an
oligosporogenic,or reduced sporulation, phenotypes (Antoniewski et at 1990,Mueller
and Sonenshein 1992). Such mutations have also been shown to reduce expression
of certain early sporulation genes (Antoniewski et aL 1990, Mueller and Sonenshein
1992). The results presented in figure 5.2 illustrate that the expression of spoVGlacZ is unaffected by the presence of the kinA null mutation. These results, which
are consistent with those obtained by Mueller and Sonenshein (1992), indicate that
the reduced level of phospho-SpoOA resulting from kinA null mutations is still
sufficient to repress abrB.
5.3.2 pllGacZ
and poIA-lacZ
In contrast to the pattern of spoVG-lacZ expression, transcription from the
early sporulation genes spoIIA and spollG, as determined through lacZ fusions, is
attenuated approximately one order of magnitude by kinA null mutations (figure 5.3
and 5.4). These operons encode, among other proteins, sporulation sigma factors
that are needed to establish cell type specific gene expression in the mother cell and
the forespore (Losick and Stragier 1992). Since transcription of these genes is
directly activated by phosphorylated SpoOA, these results suggests that the level of
phospho-SpoOA in kinA mutants cannot initiate transcription from these genes at
wild-type levels.
123
5.3.3 spaxlDacZ
Transcription from the spollD locus is known to require the gene products of
the spollA and spoIIG operons (Rong et al. 1986, Clarke and Mandelstam 1987).
The sporulation sigma factor ae, encoded by the second gene in the spoIIG operon
(spollGB), is known to direct transcription from this locus (Clarke et al. 1986,Clarke
and Mandelstam 1987, Errington 1993). The post transcriptional processing required
for oB activation has been shown to involve the spollIA gene products (Errington
1993).
Based on the requirements for spolID transcription, the pattern of spoIIDlacZ gene expression exhibited by wild-type and kinA cells lines would be expected
to be similar to patterns exhibited by spoIIA-lacZ and spoIIG-lacZ expression. The
time course profiles of B-galactosidase as directed from a spoIID-lacZ transcriptional
fusion are presented in figure 5.5. These results, indicate that the adverse affects that
kinA null mutations have on spolIA-lacZ and spoIIG-lacZexpression are propagated
through to spoIID-lacZ expression.
5.4 InA Null Mutations: Population Demographics
5.4.1 p-alactosidase
Expressing Populations
The results presented in the previous section indicate that early sporulation
gene expression, as determined from bulk culture assays, is reduced by kinA null
mutations and this affect is propagated into later development. The question that
arose was whether the observed reduction in gene expression was due to a small
124
population of cells expressing at wild type levels or whether gene expression was
reduced in all cells. If the expression of SpoOA target genes, in kinA null mutants,
was the result of a small cell population expressing at wild-type levels, a threshold
level of activated SpoOA, or some component of the phosphorelay, would be
implicated as a critical determinant in initiating spore formation.
Alternatively, if
kinA null mutations reduced SpoOA controlled gene expression in all cells, the
appearance of spores might simply reflect the ability of a small population of cells to
"escape" the effects of reduced levels of necessary sporulation gene products.
Mutations in kinA cause a decrease in the proportion of cells that express high
levels of early sporulation genes. Figures 5.6-8 present the time course profiles of the
fraction of the culture expressing -galactosidase as directed from the different
sporulation loci. The results were obtained by staining cell samples, at the indicated
intervals, with C3-FDG according to the procedure outlined in Chapter 3. Since the
total cell numbers change little, if any, during the stationary phase in cultures grown
in nutrient broth, these percentages reflect the number of positive cells. These
results indicate that standard measurements of gene expression actually mask the
behavior of a small population of cells expressing high levels of P-galactosidase.
Fluorescence distributions, reflecting C 8-fluorescein, obtained from samples
harvested at the point of peak wild type expression are presented in figures 5-9-12.
kinA fluorescence distributions were obtained from samples harvested at the same
time point (as wild type). The fluorescence cutoff used to estimate the relative sizes
of the two populations was set by the green fluorescence distribution of the PKH26
125
background control (figure 5.13).
A comparison between the fluorescence
distributions obtained from wild type and kinA cultures, containing spoIIG-lacZ,
spoIlA-lacZ and spolID-lacZ reveals that kinA null mutations act to reduce the size
of the expressing population.
The fluorescence distributions obtained from cells
containing spoVG-IacZ were unaffected, as could be anticipated from the specific igalactosidase activity profiles.
5.4.2 Spores
The appearance of spores in wild-type and kinA null mutants was monitored
during a time course experiment in order to determine whether the pattern of
appearance was qualitatively similar to those exhibited early during development by
the early sporulation marker genes. The time course of appearance of spores
obtained from wild type and kinA::Tn917 cell lines are presented in figure 5.14.
These results confirm the observation that kinA null mutations reduce spore
formation approximately ten fold. However, in contrast to reports that kinA
mutations lead to delayed sporulation on solid media (Perego et al 1989), these data
indicate that no delay in spore development occurs in liquid cultures. Prior to the
first datum, at 4.75 hours, no spores were detected.
Furthermore, the similar
population profiles exhibited by spores and positive populations (figure 5.6-8)
provides further evidence in support of the conclusion that the spores evolve from
the high expression populations.
126
5.5
inA Null Mutations: Distributions of spo Gene Products
5.5.1 Fluoreence
Distributions
The specific fluorescence of the samples analyzed by flow cytometry, obtained
by averaging the C 8 -fluorescein fluorescence distributions (see Chapter 8), correlates
with the bulk culture specific activity (figure 5.15). These data were obtained from
the flow cytometer based analysis of the spollG-lacZ (A) and spoIID-lacZ (B)
expression. Also reproduced for comparative purposes are the time course profiles
of fl-galactosidase activity as determined through standard methods (figures 5.3 &
5.5). Similar results were presented in chapter 3 (section 3.8.6) for cells expressing
spoIIA-lacZ.
The positive population present in kinA cultures behaves similarlyto wild type.
Table 5.1 contains specific fluorescence data, obtained from a finer analysis of the
bulk culture data (figure 5.15) via the histograms presented earlier (figures 5.9-12).
Two methods of analysis were employed in the analysis of the fluorescence
distributions. In the first method, the specific fluorescence was obtained by averaging
over all cells (i.e. the approach used to generate figure 5.15). In the second method,
the average was taken solely over the high expressing subpopulation.
A comparison
between the specific fluorescence obtained from each wild type-kinApair reveals that
differences apparent at the total population level essentially disappear (or are
diminished for spoIIA-lacZ) when the average is taken over the high expressing
subpopulation. These results indicate that the high expressing populations in kinA
cultures behave similarly to the high expressing population in wild type cultures.
127
Furthermore, cells that are capable of activating early sporulation gene expression
appear to be responsible for the residual sporulation (table 5.1; spores versus
positives).
5.5.2 spaPD-acZ
Cel Sortin
Enzymatic assays performed on the high expressing subpopulations of both
wild type and kinA cells indicated that both populations behave similarly. Wild type
and kinA cells containing a spollD-lacZ fusion were harvested two hours into the
stationary phase (cultivated in DSM), stained with C8 -FDG, analyzed by flow
cytometry and sorted according to green fluorescence.
The resulting green
fluorescence distributions for wild type and kinA cell populations are presented in
figure 5.16. In contrast to the distributions presented in the earlier figures, the wild
type and kinA distributions are comprised of 100,000and 500,000events respectively.
By increasing the number of events taken from the kinA sample, the bimodal
distribution is more apparent (inset to figure 5.16B). Also shown are the two
parameter contour plots used to define the sort window (the rectangles shown in
figures 5.17A & B). The X-axis represents the green fluorescence signal and the Y-
axis represents the right angle light scatter measurement. That both high fluorescent
populations behave similarly is shown in table 5.2 where in addition to the data
presented in table 5.1, the relative specific activityof the total and the high expressing
populations are shown. These results indicate that the level of spoIID-lacZ
expression in both high expressing populations is essentially identical and thus provide
128
direct support for the contention that the wild type and kinA high expressing
populations behave similarly.
5.6 Observations on spaA
Gene Expression
The effect of the kinA mutation was to reduce the fraction of cells expressing
spoIIA-lacZ (table 5.1). However, for spoIA,
the average fluorescence per cell in
the expressing population was found to be lower in kinA mutants relative to wild type
(table 5.1). This difference could reflect differences in the binding affinity of SpoOAP for the spolIA regulatory region. This difference might also reflect the fact that
transcription of spollA is directly controlled by aH (Wu et al. 1989, Wu et al. 1991),
the spoOH gene product, while transcription of spollG is controlled by aA (Kenney
et al. 1989). Yet, since the size of the expressing population generally reflects the
proportion of cells eventually developing into spores, it is concluded that the level of
spoIL4 gene products in cells containing kinA null mutations is still sufficient to carry
out its in vivo roles in spore formation.
5.7 The Effects of spOKAC Mutations
The spoOK operon has also been found to play a role in the signal
transduction processes leading to the activation of SpoOA(Rudner et al. 1991, Perego
et al. 1991; figure 5.1). Similar to kinA null mutations, mutations in this operon lead
to an oligosporogenic phenotype. This operon encodes five proteins that exhibit
significanthomologywith the oligopeptide permease (opp) operon of the Salmonella
129
typhimurium (Perego et al. 1991,Rudner et al. 1991) and has been postulated to play
a role in sensing and importing extracellular signaling peptides (Grossman and Losick
1988) needed for efficient spore formation. While the exact role that this operon
plays in transducing early stationary phase signals remains unknown, its involvement
in these processes has been deduced by the finding that overexpression of the kinA
gene product can partially overcome spoOK derived sporulation defects.
Previous experiments indicate that deletions in the spoOKC, the third gene in
the operon, lead to an oligosporogenic phenotype (Rudner et al. 1991). Deletions
in spoOKC also cause a decrease in bulk culture measurements of P-galactosidase as
directed from the spoIIG locus (figure 5.18). Given its postulated role in sensing and
transmitting stationary phase signals, it was of interest to identify if the pattern of
single cell gene expression was similar to that resulting from kinA mutations.
Null mutations in spoOKCcause a decrease in the fraction of cells that express
high levels of spoIIG-lacZZ. The time course experiments presented in figure 5.18
suggests that little or no p-galactosidase is expressed in a spoOK mutant. However,
analysis of single cell gene expression indicates that the bulk culture profiles conceal
a small population of cells expressing high levels of spoIIG-lacZ (figure 5.19; table
5.3). These distributions were obtained from the Tz75 samples shown in figure 5.18.
As with the kinA mutations, a comparison between the size of the population
expressing high levels of spoIIG-lacZ and the proportion of cells that developed into
spores (table 5.3) reveals a correlation. These results provide additional support for
the conclusions drawn in the previous section and further reinforce the postulated
130
role that the spoOK operon plays early during sporulation.
5.8 Discussion
5.&1 Threshold Activation of Sporulation
The principal conclusion obtained from the results presented in this chapter
is that threshold mechanisms control entrance into the developmental pathway of
spore formation. Based on these results, the results presented in Chapter 4 and,
what is known about early gene expression in B. subtilis, it is concluded that the
threshold is associated with the phosphorelay. This conclusion is illustrated in figure
5.20 where the effect that a kinA mutation has on the concentration distribution of
a phosphorelay component or the level of signal input into the phosphorelay is
schematically illustrated. Similar to the distributions presented throughout this work,
the abscissa represents the amount of the "threshold" molecule and the ordinate
represents the number of cells. The effect that kinA, or spoOK,mutations have on
the level of this threshold molecule is schematically represented
reduction throughout the cell population.
by a uniform
Therefore, for a fixed threshold
concentration as set by the cell, such mutations act to reduce the number of cells
above the critical threshold so that fewer cells are capable of entering the
developmental pathway. Cells that are able to overcome this threshold barrier
initiate stage II gene expression and eventually develop into spores.
While it can be inferred with a great deal of certainty that the level of
phospho-SpoOA is reduced, on the average, in cells containing the kinA or spoOK
131
mutations, the results presented in this chapter are not able to discriminate how the
level of phospho-SpoOA is distributed throughout the population. If the level of
signal inputs into the phosphorelay must reach a threshold, then one might anticipate
that the distribution of phospho-SpoOAwithin the population would be high or low.
In contrast, if the level of some phosphorelay component must need to reach a
threshold level, then it is likely that the level of phospho-SpoOA may need to reach
a threshold. This statement followsfrom the assumption that the components of the
phosphorelay; KinA, SpoOF, SpoOB and SpoOA, and their phosphorylated
counterparts are close to equilibrium so that changes in the level of each component
leads to equilibrium driven changes in all other components.
In Chapter 6, a
threshold model is developed in order to provide an unambiguous framework in
which to analyze these possibilities. This model will also be shown to be consistent
with the results presented in this thesis.
5.82 Multiple Thresholds
If one accepts the operational definition of a threshold, namely; the
mechanism by which small or continuous changes in a parameter or variable are
dramatically amplified so as to lead to very different outcomes, then it is likely that
multiple thresholds exist. This follows from the molecular mechanisms known to
regulate early stationary gene expression in B. subtilis.
The first developmental switch appears to be associated with the AbrB
pathway (figures 2.3A & 5.1) which regulates spoVG expression. AbrB exists as a
132
hexamer in vivo and it is the hexameric form that is responsible for repressing
transcription from a number of stationary phase genes (Strauch et a. 1989).
Additionally,AbrB has been found to repress transcription, from certain loci, through
a cooperative binding process (Strauch et al. 1989B). Therefore small decreases in
the in vivo level of AbrB, resulting from both degradation and decreased transcription
via repression by phospho-SpoOA, can be expected to have an amplified effect.
These observations, along with the pattern of spoVG expression in kinA mutants
suggests that only a small amount of phospho-SpoOA is required to overcome this
threshold.
The second threshold appears to involve the repression of stage II genes via
SinR and possibly GsiA (Mueller and Sonenshein 1992, Bai et al. 1993). Mutations
in sinR or gsiA have been found to bypass the partial sporulation and gene
expression defects exhibited by kinA mutants (Louie et al. 1992, Mandic-Mulec et aL
1992, Mueller and Sonenshein 1992). Also, mutations in sinR result in increased
sporulation and stage II gene expression while over-expression has the opposite effect
(Mandic-Mulec et al. 1992). It also appears that SinR represses spoOA expression
(AD. Grossman, personal communication).
While very little is known about the
mechanisms by which phospho-SpoOA inactivates GsiA, it is known that SinR is
inactivated by the developmentallyexpressed sinI gene product. The functional role
that SinR plays in repressing certain target genes appears to be inactivated by
protein-protein interactions with the sinIgene product (Bai et al 1993). Since SinR
appears to perform its role as a tetrameric complex, the mechanism by which cells
133
relieve this repression might very well mimic the philosophy employed by cells to
inactivate the lac repressor, which also functions as a tetramer
(Barkley and
Bourgeois 1980). Finally, because sinI expression relies on intact spoOA and spoOH
genes (Bai et al. 1993), it is likely that phospho-SpoOA is involved in countering the
effects of SinR. Based on the results presented in this work, this threshold appears
to require higher levels of phospho-SpoOA.
In Chapter 6, a model is developed in order to demonstrate that these
threshold arguments are consistent with what is known about early stationary phase
gene regulation in cultures of B. subtilis. This model is used to argue that the AbrB
and the SinR checkpoints are associated with the need of the cell to accumulate a
threshold signal input into the phosphorelay and to subsequently amplify this signal
via the phosphorelay.
In the context of this model, the spoOK gene products are
hypothesized to be associated with the signal input threshold. The kinA gene product
is hypothesized to be associated with the need for signal amplification.
5.83
Implications of a Threshold Mechanism
The existence of threshold mechanisms in biological systems has several
important
implications: 1) systems controlled by threshold mechanisms can be
expected to be sensitive to initial conditions and to slight variations in cultivation
conditions and 2) stochastic and deterministic processes will appear to govern such
systems.
The first implication is a natural consequence of the definition of a
threshold. Namely that small changes in the concentration of a particular molecule
134
can lead to dramatics changes in the behavior of the system. Because there is no way
of knowing a pnon which "small" changes are important, system variability must be
accepted as the rule, not the exception. It is therefore not surprising that complex
biological systems, particularly those involving differentiating systems such as cultures
of B. subtilis, often exhibit day-to-day variability.
Despite the deterministic nature of the molecular mechanisms that lead to
threshold behavior, the ability of cells to initiate the developmental process of spore
formation also involves stochastic elements. The existence of random factors that
control the initiation into sporulation was demonstrated by the early work of
Schaeffer (1965) and Dawes and Mandelstam (1970). They found that there exists
a finite probability that a cell would initiate spore formation under a given set of
cultivation conditions.
More recently, the results reported by a number of
researchers (Weinrauch et al. 1990, Rudner et al. 1991) permit a similar conclusion.
These researchers determined the sporulation probabilities (i.e. frequencies) in
various signal transduction mutants involvingsingle mutations (in kinA, spoOK and
come and double mutations (kinA spoOKand kinA comP). All of these genes are
believed to be involved in regulating the initiation process. These researchers noted
that the sporulation probabilities observed in the double mutants were more severe
than in any of the single mutants. However, further analysis of their data indicates
that the sporulation probabilities observed in the double mutants simply reflected the
multiplicative probability of the individual mutants. This is illustrated in figure 5.21
where the calculated probabilities, as determined from the individual mutants, are
135
plotted versus the experimentally determined double mutant sporulation probability.
These data indicate that stochastic elements are involved in determining which cells
in the population accumulate enough activated SpoOA to initiate spore formation.
136
Table 5.1 Sporulation and P-Galactosidase Population Data
Fluorescence
%
%
all
positive
spores'
positive 2
cells3
cells4
26
30
22
64
strain
fusion
kinA
KI1179
K11398
spoVG-JacZ
+
spoVG-lacZ
-
0.5
31
23
69
SIK122
AG1350
spoIIG-lacZ
spoIIG-lacZ
+
-
32
2
23
4
11
2
71
62
KI1183
KI1397
spoIID-lacZ
spoIID-lacZ
+
-
22
1
25
5
16
3
85
64
KI938
spoIIA-lacZ
+
21
33
41
109
KI1001
spoIIA-lacZ
-
4
2
5
57
l Percent spores were determined between 12 and 18 hours after the end of
exponential growth as described in chapter 9.
Percent positive refers to the percentage of cells that were expressing
galactosidase as judged by green fluorescence above background.
2
/B-
3 Average relative fluorescence (all cells) is the average linear fluorescence above
background per cell. In all experiments > 10,000 events were counted.
Average relative fluorescence (positive cells) is the average fluorescence above
background per cell in the positive or expressing subpopulation.
4
137
Table 5.2 Sporulation and spoIID-lacZ Expression Data
genotype
wild type
kinA
%
spores'
%
positive2
Specific Activity3
all
positive
cells
cells
Fluorescence 4
all
positive
cells cells
17
19
0.21
1
0.25
1
0.5
2.6
0.06
0.93
0.09
1.2
1 Percent spores were determined 18 hours after the end of exponential growth as
described in chapter 9.
2
Percent positive refers to the percentage of cells that were expressing -
galactosidase as judged by green fluorescence above background.
3
Specific activity was determined by assays 44,000 cells from each population (i.e.
total population sorts and positive population sorts) using the MUG assay. The wild
type positive population has been normalized to unity for comparison purposes.
4
Same as in Table 5.1 except that the data has been normalized with the wild type
positive population set to unity.
138
Table 5.3 Sporulation and spollG-lacZ Expression Data
in Oligosporogenous Mutants
strain
genotype
SIK122
wild type
AG1350
1 Percent
%
spores'
%
positive2
Fluorescence
all
positive
3
cells
cells4
31
20
16
120
kinA::Tn917
3
6
4
140
spoOKAC
1
4
2
90
spores were determined 18 hours after the end of exponential growth as
described in chapter 9.
2
Percent positive refers to the percentage of cells that were expressing/f-
galactosidase as judged by green fluorescence above background.
3
Average relative fluorescence (all cells) is the average linear fluorescence above
background per cell. In all experiments 2 10,000events were counted.
4
Average relative fluorescence (positive cells) is the average fluorescence above
background per cell in the positive or expressing subpopulation.
139
Stationary
Phase
Signals
KnA
Signal Transduction
(Phosphorelay)
*
SpoOK
OF
wKinA-P
OB
.Kin-P
f
OF-P
Kt
Kin
+
Activation
sp oIIG
OB-P
OA
A-P1
1 spollA
soabrB
spollA
spoVG
I- Repression
Transcriptional
Activation
spollD
spore
Figure5.1 Summary of stationary phase signal transduction via the phosphorelay and
transcriptional activation va phospho-SpoOA. Upon entrance into the stationary
phase, the the spoOKgene products provide input signals to the phosphorelay (KinA,
SpoOF, SpoOB and SpoOA)
140
150
4-0
O
O
100
._
C/)
CD
co
m
50
ar
O
0
I
n
-1
0
1
2
3
4
Time (hr)
Figure 5.2 Time course profiles of culture average P-galactosidase expression as
directed from the spoVG locus in wild type ()
141
and kinA null mutants (O).
60
4._
4:0
O
C,
.
40
0
0O
20
la
0
0
e4.
I
Ca
0
-1
I
0
1
2
3
4
Time (hr)
Figre
5.3 Time course profiles of culture average B-galactosidase expression as
directed from the spollIG locus in wild type ()
142
and kinA null mutants (O).
100
.l
4.
0 75
o
cY
0a,
0 50
-o
0
0
0t0o 25
I
cm
0
-1
0
1
2
3
4
Time. (hr)
Figure 5.4 Time course profiles of culture average Bf-galactosidase expression as
directed from the spollA locus in wild type ()
143
and kinA null mutants ().
40
a.
O
O
30
0
5.
co
20
to
C,
10
I
0
0
1
2
3
4
5
Time (hr)
Fiure
5.5 Time course profiles of culture average P-galactosidase expression as
directed from the spolID locus in wild type (0) and kinA null mutants (O).
144
30
0
20
,_
H
13.
eQ
10
0
-1
0
1
2
3
4
Time (hr)
Figure 5.6 Time course profiles of the proportion of the population expressing galactosidase as directed from the spoIIG locus in wild type ()
mutants (0).
145
and kinA null
A
4U
30
0O
CL
10
0
m
1
0
1
2
3
4
Time (hr)
Figure 5.7 Time course profiles of the proportion of the population expressing igalactosidase as directed from the spoILA locus in wild type ()
mutants (O).
146
and kinA null
30
020
.
10
0
0
1
2
3
4
5
Time (hr)
Figure 5.8 Time course profiles of the proportion of the population expressing fBgalactosidase as directed from the spoIID locus in wild type () and kinA null
mutants ().
147
feI n
ou
(D40
LU
0
-0
E
z
20
0
0
200
400
600
800
1000
0
200
400
600
800
1000
r,,^
ou
,)
c-
a)
40
LL
0
4o
0)
E
20
0
Channel Number
Fisure 5.9 Green fluorescence distributions obtained from cells expressing spoVGlacZ in wild type (A) and kinA null mutants (B). The samples analyzed were
harvested at T 2 (figure 5.2).
148
r,
TOU
C')
C-
() 40
LUJ
0
L)
()
E
20
0
0
200
400
600
800
1000
0
200
400
600
800
1000
ou
n
_
U)
Cn
(i
40
LU
0
,,)
E
20
0
Channel Number
Figure 5.10 Green fluorescence distributions obtained from cells expressing spoIIGlacZ in wild type (A) and kinA null mutants (B).
harvested at T2S (figure 5.3).
149
The samples analyzed were
OU
(I
C-
a)
40
LU
a)
E
20
0
0
200
400
600
800
1000
0
200
400
600
800
1000
f:'
ou
,n
a)
I1
40
L
a)
-
20
0
Channel Number
Figure5.11 Green fluorescence distributions obtained from cells expressing spollAlacZ in wild type (A) and kinA null mutants (B). The samples analyzed were
harvested at T2 (figure 5.4).
150
r' rX
ou
LI
0
a)
.0
E
20
0
0
200
400
600
800
1000
0
200
400
600
800
1000
f"n
ou
U)
)
40
LU
0
()
E
z
20
TO
Channel Number
Figure 5.12 Green fluorescence distributions obtained from cells expressing spolDlacZ in wild type (A) and kinA null mutants (B). The samples analyzed were
harvested at T3 (figure 5.5).
151
60
a,
40
< 0.1% Positive
Hij
N-
o0
E 20
z
0
0
200
400
600
800
1000
Channel Number
Figure 5.13 Green fluorescence distributions obtained from the PKH26 internal
negative control present in the wild type spoIIG-lacZ sample (figure 5.10A).
152
25
20
u)
15
QI
en
10
5
0
0
5
10
Time
15
20
(hr)
Figure 5.14 Time course profiles of the sporulation frequency in wild type (0) and
kinA null mutants ().
153
-d
-
13
-n
0(D
C
60
(D
E3
o
c,
40
5c
C,
(y)
-
Q
0
0
D
(D
0
0
1
2
3
--
4
4U
-I
,U
(D
O
'0 30
(I
15
0
D
(D
10
20
,cI
C,
C-)
°
10
0
(D
0
0-
0
1
2
3
4
5
Time (hr)
Figure 5.15 Correlation between the culture average specific activity, obtained from
bulk culture assays, and the culture average fluorescence, obtained from flow
cytometry. Panel A represents spoIIG-lacZ expression (figure 5.3) and panel B
represents spoIID-lacZ expression (figure 5.5).
154
400
A
C
0
300
0
200
E
100
z
19% Positive
0
200
400
B
2000
)O
w
0
L-
e
.0
1000
E
z
0
0
200
400
600
800
1000
Channel Number
Figure 5.16 Fluorescence cutoffs used to sort and quantitate wild type (A) and kinA
(B; inset) cell populations expressing high levels of spoIID-lacZ
155
230EC06
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CHUNC
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-
Figue 5.17 Two parameter, right angle light scatter (Y-axis) and green fluorescence
(X-axis), dot plots displaying the windows used to sort wild type (top) and kinA
(bottom) cell populations expressing high levels of spoIID-lacZ
156
U
150
0 100
0
0
44
.'
(U)
f,
la
.z
50
(9C
1J
ea
0
-1
0
1
2
3
4
Time (hr)
Figure 5.18 Time course profiles of spoIIG-lacZ expression in various isogenic cell
lines.
157
4M
IW
100
A
B
C,
a)
75
20% Positive
75
6% Positive
w
I4-
O
50
50
25
25
I-
c~
E
Z
0
0
200
400
600
800
100
0
1000 0
400
600
800
1000
60
C
C
u)
200
D
75
4% Positive
40
1.6% Positive
4-
50
D
E
Z
20
25
0
0
0
200
400
600
800
1000
Channel Number
0
200
400
600
800
1000
Channel Number
Figure 5.19 Green fluorescence distributions obtained from cells expressing spolIGJacZ in wild type (A), kinA (B) and spoOKAC mutants. The samples analyzed were
harvested at T27 5 (figure 5.16). Also presented is the green fluorescence distribution
of the PKH26 negative control present in the wild type sample.
158
IThreshold
Initiation
Mechanism
I
Wild Type
Onto
Spores
V-
0
kinA
\
I
.0
E
I1
z
,
' I
ID
Ir
I
I
II
I
I
I
II
Threshold
Phosphorelay Component or Signal Inputs
Figure 5.20 Proposed threshold initiation mechanism used by cells of B. subtilis to
control early developmental gene expression.
159
10-2
0
V
10- 3
-0
0
0
10-4
C
· - kinA spoOK
v - kinA spoOK141
Q
U
10-s
* - kinA comP
L_
* - kinA spoOK
L
* - kinA spoOK141
10
/ I,,,,,,1
110-6
Ig
10-5
I
wlW1
I
I
I
IW~g1
I .
10 - 4
I
I I
10-3
10-2
Sporulation Frequency (Experimental)
Figure 5.21 Comparison between the sporulation frequencies exhibited by various
double mutants. The ordinate represents the sporulation frequencies calculated from
the single mutant sporulation frequencies assuming the mutations acted
independently. The abscissa represents the experimentally determined double mutant
sporulation frequencies (the spoOK data was obtained from Rudner et. al. 1991 and
the comP data was obtained from Weinrauch et. al. 1990).
160
Chapter 6: Quantitative Analysis of Threshold Phenomena
6.1 Introduction
In the previous chapters the concept of a biologicalthreshold was introduced
in order to explain how genetically identical cells, in the same environment, could
adopt dramatically different developmental fates. In this sense, a threshold is meant
to describe how small changes in a system variable or parameter
can elicit
"catastrophic" changes in the system behavior (Arnold 1986). Experimental results
were also presented, suggestingthat threshold-like mechanisms control entrance into
the sporulation pathway. The purpose of this chapter is to show that threshold
mechanisms can be predicted theoretically in a manner that is fully consistent with
what is known about gene regulation in a number of biological systems.
The generality of threshold mechanisms is argued for by showing they control
adaptive gene expression in two paradigm biological systems, phage A and the lac
operon.
Since both systems are well characterized, both qualitatively and
quantitatively, a theoretical analysis of how the biological processes that regulate
these systems is possible.
The principal goal of this analysis is to identify the
biological control structures responsible for the existence of threshold behavior. By
restricting the analysis to the experimentally determined parameter space found in
the literature, the identification process can proceed without the ambiguities that
would be associated with less characterized biological systems.
Quantitative analysis will be used to show that threshold-like mechanisms are
161
initiated when the actively maintained balanced growth state of the cell is disrupted,
by environmental and physiological forces, beyond the cells ability to compensate.
Single cell material balances on key transcriptional activators are used to study the
relationships that exist between the various rate processes that determine the
intracellular concentration of these transcriptional activators, and hence the
transcriptional state of the cell
Consistent with what is known about microbial
growth, it is shown that the cell stably maintains a homeostasis or balanced growth
state, by balancing these rate processes. However, when encountered with extreme
and prolonged disturbances, the capacity of the cell to maintain its original state is
exceeded and adaptation becomes the only course of action available to the cell.
Adaptation is shown to coincide with the crossing of a threshold and this ultimately
results in the cells acquisition of a different transcriptional state.
Beneath the obvious differences that exist between the lac, the A and the B.
subtilis sporulation systems, a common control "structure" is shown to regulate
adaptive gene expression in all cases. This structure and its implications were derived
from quantitative studies on both the ac and the A systems. So long as the
intracellular concentration of the transcriptional activators responsible for initiating
adaptive gene expression is regulated by a positive feedback loop, a non-linear
generation rate, of the cooperative type, and a concentration dependent removal rate,
threshold potential exists. This finding has allowed a simple conceptual framework
to be put forth that is capable of succinctlyorganizing the plethora of information
available on early stationary phase gene expression in B. subtilis. This includes, and
162
is not limited to, the data presented in the previous chapters. The pervasiveness with
which this control "structure" can be found in nature suggests that it represents a
conserved biological mechanism used by microbes to efficiently cope with changing
environmental conditions.
6.2 Mathematical Conceptualization
Figure 6.1 illustrates how the biological systems analyzed are conceptualized
both physically and mathematically.
It is assumed that physiologically distinct cell
types represent cells with distinct transcriptional states.
The focus is placed on
identifying and understanding the processes and mechanisms that control the
transition between states. Material balances on single cells are then used to translate
what is physically known about these systems into a mathematical model.
The
physical states are assumed to be representable by stable mathematical states
identified by the governing equations. Insight into the physical origins of threshold
phenomena are obtained by identifying and analyzing the mathematical conditions
required for the existence of threshold behavior. The consistency of these results is
then checked against experimental observations.
63 PhageA
6.3.1 Bacgound
Under the appropriate conditions, lysogens of E. coli may opt to initiate lytic
development (figure 6.2, Jacob and Monod 1961, Bailone et al. 1979, Ptashne 1987).
163
Once induced, a distinct pattern of gene expression is initiated. After some time, this
primitive developmental process culminates in the maturation of phage particles and
the subsequent lysis of the host. However it has also been found that depending on
the induction conditions, not all phages initiate this developmental program (Bailone
et al. 1979). Such a situation results in a heterogeneous culture comprised of both
lysogens and phages undergoing lytic development.
Since dramatically different
phage types coexist under nearly identical environmental conditions, this suggests that
mechanisms leading to threshold-like behavior are involved.
It is now known that the decision made by lysogens is controlled by the
intracellular concentration of the CI transcription factor, or JArepressor (Ptashne
1987). Repressor is a DNA binding protein that is actively expressed in lysogens.
Repressor functions as a dimer and its principal role is to maintain lysogeny by
repressing cro, a lytic specific gene. Figure 6.3 illustrates how CI dimer represses cro.
cI and cro are adjacent on the Achromosome and transcription from these loci occur
in opposite directions via the PRMand PR promoters, respectively. Segments of both
promoters comprise, in part, the OR operator and it is the state of this operator that
determines which promoter is utilized. In the lysogenic state, repressor binds to OR
near cro and this effectively prevents cro from being transcribed. At the same time,
this binding configuration allows cI to be transcribed from PRm. Conversely, during
lytic development the state of OR is such that cro is transcribed and cI is repressed
by Cro (figure 6.3B). Hence the transcriptional states of OR directly determine the
fate of A.
164
In the following sections, the processes that control the transition from
lysogeny to lysis are analyzed quantitatively. The goal is to quantitatively identify the
key processes responsible for this switch in order to better appreciated what might
be involved in less characterized systems. In so doing, precise arguments will be used
to define commonly used terms associated with adaptive gene expression which
include "threshold," "trigger" and "switch."
6.3.2 CQhracteristicsof Transcriptional Regulation
The lysogenic transcriptional
state of OR is stably maintained
by the
intracellular concentration of repressor. Figure 6.4 reveals that OR is comprised of
three adjacent sites OR
, 1 OR2 and OR3 upon which CI dimer may bind. The relative
binding affinities of CI dimer for these sites are such that OR1 is first filled, followed
by OR2 then OR3 . At low repressor concentrations, transcription of cro is blocked
due to the binding of dimer to OR1 (figure 6.3A and 6.4A). At higher repressor
concentrations,
OR2 is filled.
By binding to OR2 , dimer serves to activate
transcription from c. At the same time, the transcriptional block of cro is further
reinforced. Finally as the repressor concentration is further increased, OR3 is filled
and this results in a shut down of cI transcription. This autoregulatory function (i.e.
self repression at high dimer concentrations) prevents the excessive build up of
repressor within the cell.
165
6.3.3 The Rate Law for CI Generation
The regulatory interactions described above indicate that the level of CI dimer
is crucial to maintaining the state of lysogeny while preventing lytic development.
Therefore the model developed in this and the next few sections is specifically aimed
at summarizing the processes that determine the intracellular concentration of CI.
In this section, a rate law describing CI generation is derived. In the next section, this
rate law is incorporated into a single cell material balance on CI. Finally the model
is studied in order to identify the features responsible for threshold behavior.
To greatly simplify the algebraic manipulations involved, it is assumed that CI
dimer binds to OR only in the manner described (figure 6.4). It is also assumed that
transcription is initiated from PRmonly after both OR1 and OR2 are occupied. These
assumptions are reasonable since thermodynamic considerations indicate that these
binding states account for greater than 92% of the occupied configurations (Ackers
et al. 1982). Finally, the rate of CI generation is assumed to be directly proportional
to the rate of transcription of cI. In other words, transcription, or the initiation of
transcription, is assumed to represent the rate limiting step in the series of steps
leading to the generation of CI. Under these assumptions, the rate of generation of
CI is given by:
RGEN=k
[o (CI) 2(CI) 2]
(1)
[O(CI)2(CI)
2 J represents the concentration of the doubly occupied operator. [(CI)2]
represents the intracellular concentration of CI dimer. k represents the lumped first
166
order rate constant relating the rate of transcription to the rate of generation of CI.
An alternate interpretation of equation (1) is that the rate of generation of CI is
proportional to the probability that both OR1 and OR2 are filled.
Equation (1) is made useful by replacing [O(CI)(CI) 2 ] with variables and
parameters
that may be determined
experimentally.
This is accomplished by
assuming that the interactions between the operator sites (i.e. OR1 etc.) and CI dimer
are close to equilibrium.
The reactions and their corresponding equilibria
relationships are given by:
o] +[ CI)o()]=
(CI)
[o(CI)2] + (cI) 2 ] =o (CI)
[o(CI) 2 (CI) 2] +[ (CI) 2 ] =[o(CI)
(2)
2]
2
(CI) 2]
2(cI)2
(cI)2].
(3)
(4)
with
K 1=
[o (cI)
C[o [ (CI)
]2
(5)
2]
[o((CI)2 (c)) 2
2K2
[o((CI)][ (CI2)
[o(CI)2 (CI) 2 (CI)]
Here and
C
~~~~~~~~(6)
2
represent the intracellular
concentration of the(7)
Here [O(CI)21and [O(CI)2(CI)CI)z ] represent the intracellular concentration of the
167
singly and triply occupied operator respectively. The final relationship needed, the
OR site balance, is given by:
[OT]=O0] +[O(CI)2] + O(CI)2(CI)2] +[O(CI)2 (CI)2(CI)2
]
*
(8)
[Or] and [0] represent the total and the unbound operator concentrations
respectively.
When equations (2-8) are solved for the doubly occupied operator
complex, and the result substituted into equation (1), one obtains:
klKl[OT ] [ (CI)2]
RGEN
2
1+Kl[ (CI) 2 +K1K2[(CI)
2)
3
+K1 K2 K3 (CI)
2
(9 )
]
This equation relates the rate of generation of CI to: 1) the intracellular
concentration of CI dimer, 2) the equilibrium binding parameters and 3) the kinetic
parameter kl. With the exception of kl, estimates of all parameters can be found in
the literature. A compilation of these parameter values, along with the source, is
presented in table 6.1. Using these values, it is possible to study how this equation
varies as a function of [CI] (figure 6.5). To reflect the fact that k, is unknown, curves
for different values of kl are presented. The relationship used to convert monomer
concentration, [CI], to that of dimer concentration, [(CI)2 ], is presented in the next
section.
Several important features can be derived from figure 6.5. The first involves
the qualitative form of these curves. At low repressor concentrations, the generation
rate depends non-linearly on [(CI)2 , accelerating with increasing repressor.
168
This
"activation" region reflects the fact that the doubly occupied complex promotes
transcription (figure 6.3B). At very large [CI], the generation rate decreases with
increasing [CI]. Physically this corresponds to the autoregulation, or self-repression,
exhibited by CI dimer on c transcription (figure 6.3C).
The second important
feature is that the activation and repression regions only depend on the equilibrium
constants associated with the binding of CI dimer to the different OR sites.
Variations in the value of kl only affect the amplitude of the generation curves.
6.3.4 The Single Cell Model
To examine the in vivo ramifications of equation (9), molar balances on the
intracellular levels of CI monomer and dimer are derived. The latter is needed
because equation (9) is a function of the intracellular dimer concentration. Assuming
the cell is a well mixed variable volume reactor and that repressor does not cross the
system boundaries, the general molar balances on monomer and dimer are given by:
d( [CI Vc) ( RGMI-RDERj)
dt
d(
[
RoENand
RDEGR represent
(CI)Vc]
dt
(R
= (RaN
R,
-RDWal)
V
V
(10 )
(1)
VC
the molar rates of generation and removal, respectively,
on a per volume basis. Vc represents the cell volume. Contributions to the various
summation terms present in equations (10) and (11) are assumed to be limited to
169
equation (9) for the generation of CI and the forward and reverse reactions
associated with the dimerization reaction:
2 [CI] = (CI) 2]
(12)
The equilibrium constant associated with this dimerization reaction is written in terms
of the forward and reverse reactions rates as:
Kakf ((C)]
kr
[CI]
(13)
( 3)
Substituting the contributions from the dimerization reaction into the generation and
degradation summations in equations (10) and (11) yields:
dt ] ) =RG+k, [ (CI)
2]
-kf [CI] 2 -k2 [CI]
dw(
[ (CI)]) =_ k [ (CI) 2] +knt[CI] 2-k3 [ (CI) 2]
dt
2
2
(14)
(s )
where the forward and reverse kinetic constants, k and k, are based on moles of
monomer. The last term, on the right hand side of both equations, represents the
dilution effect due to the expanding cell volume. Under balanced growth conditions,
they are given by (i=2,3):
170
k
dlnV _ n2
dt
d
(16)
where d represents the doubling time of the cell in minutes.
In the derivation of equations (14) and (15), mechanisms that lead to the
specific degradation of monomer and dimer have been ignored. When these
processes cannot be ignored, first order kinetic mechanisms can be assumed and the
values of k2 and k3 are altered accordingly. This approach is demonstrated in section
6.3.6 where mechanisms leading to the specific degradation of CI monomer are
incorporated into the material balances.
63.5 Steady State or Balanced Growth Solutions
The steady state solutions are obtained by setting the time derivatives in
equations (14) and (15) to zero. When this is performed, equation (15) yields:
[(CI) 2] = [CI] 2 KA.
(17)
with
KKa1
A
1 +1
k,
(18)
KA can be considered to be the apparent equilibrium constant. Since KA is
smaller than K,, the [(Cl)2 ] level under balanced growth will be smaller than expected
in the absence of kinetic effects (i.e. under equilibrium conditions). The difference
reflects the dilution of dimer as the cell volume increases. In the analysis to follow,
171
KA will assume the reported value of K,. This assumption is justified since the
dissociation of dimer occurs rapidly (Pirrotta et al. 1970, Chadwick et al. 1970).
Therefore the specific rate constants associated with dimer dissociation, k, and cell
growth, k2 or k3, are such that k, > > k3 . Combining equations (17) and (18), with
the steady state form of equation (14), one obtains:
kKlK O ]
2 k3KA[CI]
klK2
1 +K,
A[
cI ] +KK2
T
2 C
]
(19)
[C]
4
CI
(9)
KK2K3 K [ CI ]
Equations (17) and (19) along with the associated parameters (table 6.1)
determine the intracellular concentrations of CI monomer and dimer under steady
state or balanced growth conditions. The simplest interpretation of this equation is
that at steady state the rate of repressor generation (right hand side) must be
balanced by the rate of repressor removal (left hand side) in order for the cell to
maintain a balanced growth environment. The right hand side of this equation yields
curves that are identical to those appearing in figure 6.5. The left hand side of this
equation represents the rate of repressor removal due to the expanding cell volume.
The linear and quadratic terms, on the left hand side, result from the dilution of
monomer and dimer respectively.
Solutions of this equation can be determined graphically in a manner similar
to that employed in nonisothermal chemical reaction engineering (Fogler 1992). This
is demonstrated in figure 6.6 where the repressor removal rate is superimposed on
the repressor generation curves presented in figure 6.5. The removal curve was
172
determined assuming repressor loss is solely due to cell growth (a 30 minute cell
doubling time was used). The points of intersection between the generation and
removal curves identify the real solutions of equation (19). Figure 6.6 (A and B)
indicates that three real solutions are possible. Also indicated in figure 6.6A is the
range of intracellular repressor concentrations reported in the literature (Pirrotta et
al. 1970, Ackers et al. 1982). The lower and upper boundaries that delimit this
region, as indicated by the dashed vertical lines, correspond to total repressor
concentrations of 1X10-7Mand 2X10-7Mrespectively. This corresponds to 100-200
-'
molecules of repressor per cell, assuming a cell volume of approximately 1X10
5
1.
Since intersections between the generation and the removal curves represent
solutions to equation (19), intersections in the range of reported repressor
concentrations identify "realistic" solutions. Realistic solutions are possible when the
specific generation rate constant, kl, ranges between 4 min-' and 8 min-' (see legend
to figure 6.6). In subsequent sections, a k, of 4 min - is shown to lead to system
behavior that is consistent with other experimental observations.
The physical significance of solutions to equation (19) can be explained with
reference to figure 6.3. The lysogenicstate corresponds to the stable steady state at
high concentrations of CI (figure 6.6A). When CI is present in sufficient quantities,
the phage stably maintains lysogenyby blocking transcription of cro. The lytic state
corresponds to the trivial steady state where no repressor is present (figure 6.6B).
With repressor absent or present in small quantities, cro is expressed and the
transcriptional
switch is thrown.
Between these physical states there exists an
173
unstable steady state that is not experimentally realizable. The physical significance
of this unattainable state is discussed in section 6.3.7.
6.3.6 Initiating the Switch
In the laboratory, lytic development is induced by subjecting cells to stimuli
that activate the proteolytic action of RecA (Bailone et al. 1979, Ptashne 1987).
RecA is a protein that is normallyinvolved in catalyzingrecombination between DNA
molecules (Ptashne 1987). When DNA is damaged, RecA exhibits proteolytic activity
and acts to degrade the monomeric form of A repressor. It has little or no effect on
the dimer. Assuming the degradation of CI monomer obeys a first order kinetic
process, the action of RecA may be described mathematically by increasing the value
of k 2.
Figure 6.7 illustrates what happens to the steady state solution "structure"
associated with equation (9) as the value of k 2 is increased. Small increases in k 2 lead
to a small leftward shift in the removal curve. Because the generation and removal
curves still intersect, at high repressor concentrations, the phage is able to maintain
the state of lysogenydespite such perturbations in k2. However as the severity of the
perturbation is increased, as reflected by a larger value of k2, the capacity of the
phage to maintain the lysogenic state is exceeded.
When this occurs, the only
transcriptional state available to the phage is associated with the lytic state.
This
imbalance, between the generation and removal rates, directs the phage towards lytic
development as defined by a zero repressor concentration state.
174
k2 represents a threshold or bifurcation parameter. As demonstrated in figure
6.7, as the value of k2 is varied slightly around the neighborhood of seven times the
growth rate, dramatic changes in the system behavior are effected. Since k2 reflects
the degradative action of RecA, this indicates that the concentration or the specific
activity of RecA must reach a threshold level in order to initiate lytic development.
6.3.7 The Dynamics Associated with the Transition
In this section, the dynamics associated with the transition
to lytic
development, once the threshold barrier has been overcome, are investigated. The
goal of this section is to demonstrate that initiation into the developmental pathway
requires that several conditions are met.
In addition to achieving a threshold
concentration of RecA or equivalently the value of k2, this threshold level must be
maintained for a certain period of time. Unless both conditions are met, the phage
will maintain its state of lysogeny.
Figure 6.8A illustrates the time course profile of CI, monomer and dimer,
following a step increase in k2 above the critical threshold.
These curves were
generated using equation (14) and the steady state form of (15). The steady state
form of equation (15) was used in order to eliminate the terms involving kf and k,
from equation (14). Since the dynamicsof dimer dissociationare rapid, the dynamics
of equation (15) can also be expected to be rapid. As the concentration of monomer
decreases, due to the action of RecA, the concentration of dimer can be expected to
adjust rapidly according to the steady state form of equation (15).
175
The profiles shown in figure 6.8A represent the dynamics associated with the
transition from lysogeny to lytic development. Following the perturbation, the rate
of monomer removal exceeds its rate of generation. Therefore the intracellular level
of repressor is driven to zero. The time duration of this transition is of the order of
30 minutes. A similar time constant, determined experimentally, has been reported
by Bailone and coworkers (1979). Because the time associated with this transition
depends on the value of k2, the value chosen for k2 represents a fit. Yet this "fit"
seems reasonable since it corresponds to a k2 slightly higher than the threshold value
(see legend to figure). Since lytic development is not initiated by all phages, a value
of k2 close to the threshold appears reasonable.
Followinga transient perturbation in k2, the eventual fate of the phage, or cell,
is decided by the repressor concentration at the unstable steady state. Figure 6.9A
presents the time course response curves, of [CI], following two different pulse
perturbations in k2 The duration of these pulses differ by only one minute (figure
6.9B). These pulses can be viewed as simple approximations to the action of RecA,
which has been found to be transient in nature (Bailone et aL.1979). As indicated
in figure 6.9A, the fate of the phage depends very much on the length of time the
perturbation persists. When the cell is subjected to a 30 minute pulse, the phage
maintains its state of lysogeny. This is possible because [CI] is driven to a value just
above the unstable steady state concentration, as indicated by the dashed horizontal
line. Therefore following the perturbation, the phage remains within the attractive
region of the lysogenic state. In contrast, a 31 minute perturbation is sufficient to
176
commit the phage to lytic development (dotted curve). In this case, [CI] is driven to
a value just below the unstable steady state monomer concentration so that once the
original phase portrait is reestablished, the phage resides in the attractive region of
the lytic state. These results are consistent with the finding that lysogenymay or may
not be maintained when lysogens are transiently induced by thymine starvation
(Bailone et a 1979). The repressor concentration at the unstable steady state
indicates that approximately 85% of the monomer, prior to the perturbation, must
be degraded in order to commit phages to the lytic pathway. This value is in good
agreement with the 90% value reported by Bailone and coworkers (1979).
6.3.8 Static versus Dynamic Thresholds
The monomer concentration at the unstable steady state represents a dynamic
threshold. Since the fate of the phage, and hence the cell, depends on whether its
intracellular monomer concentration falls above or below the unstable steady state
concentration, slight changes in this value can lead to dramatic changes in the system
behavior. The term dynamic is used to explicitly indicate that time dependent factors
are involved in determining the state adopted by the phage. This was graphically
illustrated in figure 6.9 and it will resurface in section 6.5.3. In contrast, the term
static threshold can be used to described the barrier that must be overcome in order
to alter the number of steady states available and does not involve time. This was
illustrated in figure 6.7 were the activity of RecA, via the paramater k2, was required
to reach a critical level before lytic development was possible.
177
Both types of
thresholds have been alluded to in the literature (Novick and Wiener 1959, Lewis et
a). 1977). The arguments presented in this section allow the subtleties associated with
both thresholds to be appreciated.
6.4 Tme R c
ac Operon
6.4bA1Bacground
It has long been known that when the E coli lac operon is induced under
suboptimal conditions, it is possible to generate and maintain physiologically distinct
cells types (Benzer 1953, Novick and Weiner 1957, 1959, Cohen and Horibata 1959).
This heterogeneity is associated with the transcriptional state of the cells.
One
population is induced for expression of the lac operon while the other remains
uninduced. The explanation for this phenomenon, provided by Novick and Weiner
(1957) is presented in figure 6.10. They hypothesized that in order for cells to
become induced for expression, the amount of lac permease, encoded by lacY, must
reach a threshold level within a cell. Upon attaining this threshold concentration,
inducer could be imported into the cell in order to inactivate lac repressor.
This
results in the expression of more permease and eventually leads to full induction.
Cells that did not achieve this threshold level remain uninduced.
The autocatalytic activation mechanism described in figure 6.10 cannot explain
the existence of two stable populations.
By the arguments presented above, the
uninduced state would be expected to be unstable. Any permease molecules
associated with the cell would rapidly import inducer and the positive feedback loop
178
would be initiated. Since the time scale associated with cell growth is much larger
than the time scale associated with inducer transport or permease expression, the
amount of permease per cell would be expected to rapidly increase despite the fact
that it is diluted upon each division cycle. Novick and Weiner (1959) realized this
stability problem and accounted for it using hypothetical arguments.
In this section, these issues are studied quantitatively using models derived
from single cell material balances. The stability problem will be addressed and the
conditions required for the existence of multiple transcriptional states identified.
Additionally, the "maintenance" phenomenon (Novick and Wiener 1957, section 6.4.6)
or the dependence that inducibility has on initial conditions will be explained. All
analysiswill be performed in the concentration and parameter ranges reported in the
literature. It will be demonstrated that the information needed to explain these
phenomena is already known.
64.2 Chaeristics
of the lac Operon
When cells of E. coli are cultivated in non-glucose containing media, the trans
acting factor encoded by acI negatively regulates transcription from the lac operon
(see Reznikoff 1992 for recent review). lacI encodes a repressor that blocks
transcription by specifically binding, as a tetrameric complex, to the lac operator.
Since lacI is expressed constituitively, little or no transcription occurs under noninducing conditions. However, when an appropriate inducer is added, molecules of
inducer bind to repressor in such a way as to reduce the affinity of repressor for the
179
operator (figure 6.11B). Once this occurs, the operon is freely transcribed (figure
6.11C).
An important detail involved in inducing the operon arises from the fact that
two inducer molecules, at least, must bind to the tetrameric repressor complex (Boezi
and Cowie 1961, Yagil and Yagil 1971, Barkley and Bourgeois 1980 for rev.).
Standard biology and biochemical engineering texts often omit this detail.
This
omission is of little consequence when the operon is fully induced. In either case, the
rate of transcription can be expected to saturate with respect to increasing inducer
concentrations. However, under suboptimal induction conditions, failure to account
for this detail will lead to inexplicable system behavior. In other words, the dynamical
system will be dramatically altered (Othmer 1976, Tyson and Othmer 1978). The
ramifications of this detail form the core of the analysis that follows.
The approach used to study the heterogeneity associated with suboptimal
induction of the lac operon, is identical to that employed in the previous sections.
A rate law describing the generation of permease is first derived. This rate law is
then incorporated into a single cell material balance on the lacY gene product. The
resulting model is then used to explain the hysteresis phenomenon noted by Novick
and Weiner (1957) and others (Cohn and Horibata 1959).
6.43 Derivation of the Rate Law
The rate of expression of lac permease is assumed to obey the following
equation:
This equation assumes that the rate of generation is proportional to the concentration
180
RGEklJ10]
(20)
of free operator, [0]. It also assumes that transcription is the limiting step in the
series of steps from transcription of the operon to translocation of the permease to
the cell membrane.
The first order kinetic constant k [min-'] relates the
concentration of free operator to the rate of permease generation. The units
associated with this equation are [M-min-1]. As before, [0] is replaced with variables
and parameters that can be determined experimentally. The approach taken is
essentially identical to that of Yagil and Yagil (1971). The following reactions are
considered to lie close to equilibrium:
2 [I] +[R]=[
2 RR]
(R] +O] =[RO].
(21)
(22)
Here [I], [R], [I2 R] and [RO] represent the concentration of free inducer, free
repressor, inducer-repressor complex and repressor-operator complex respectively.
The associated equilibrium constants are given by:
K
CI2R]
1=
(R][I]2
K =[RO
Theappropriate
site balances are given[R [oby:
The appropriate site balances are given by:
181
(23)
(24)
[OT,]
=[o]
(25)
+[RO]
(26)
[Rr ] =[R] +[I 2 R ].
[RT] and [OT] represent the concentrations of the total repressor and the total
operator respectively. Equation (26) neglects [RO] relative to [R]. This assumption
greatly reduces the algebraic manipulations required and is justified on the basis that
the concentration of repressor is much greater than that of the operator. Solving
equations (21-26) for [0], followed by substitution into equation (20), one obtains:
RGENk IOT ]
1+K1[I]
1+K1 [I]
2
2
+K2[Rr]
(27)
This equation describes the rate of generation of permease as a function of [I].
Figure 6.12 illustrates the shape of this function for different values of k.
All
parameters associated with this equation, except the kinetic parameter kl, can be
found in the literature (table 6.2). The shape of this curve is in good agreement with
the data obtained from Herzenberg (1959). This includes both the S-shape and the
concentration of inducer needed for saturation.
The dominant physics associated with equation (27) are contained in the [I]2
term present in the numerator. This term originates from reaction (21) where the
fact that two inducer molecules are needed to inactivate the repressor is explicitly
accounted for.
Due to this term, the rate of permease generation varies
182
approximately as [I]2 at low inducer concentrations and an S-shaped curve results.
Had only one inducer molecule been sufficient to inactivate repressor, a hyperbolic
rate expression would have resulted. Both rate expressions exhibit saturation kinetics,
however the latter would have depended linearly on [I] at low inducer concentrations.
Therefore considerably different behavior would be expected to result under
suboptimal induction conditions.
6A4 The Single Cel Model
In this section, the rate law given by equation (27) is incorporated into single
cell material balances derived on the species shown in figure 6.10. Equation (26)
represents the repressor balance. The molar balances on permease and inducer are
given by:
d( [
V)
d t -RGVC
dt
=(RACTIVr+RPASSIVE)
VC,
(28)
'
(29)
[YJ and [I] represent the intracellular concentrations of permease and inducer
respectively. In both equations, degradation processes have been ignored.
RGBN
is
given by equation (27). RACTVB
[M-min-'] represents the rate of active transport of
inducer via permease. RPASSVi
represents the transport rate of inducer viapermease
independent processes.
183
The rate of active transport of inducer via permease can be represented by
(Rickenberg et al. 1956, Kepes 1960):
R"z
a I R~ACTzrVz
Y1
I[Iz(30)
P
(30
+
where a [min-'] represents the turnover number of lac permease and
i
[EsM]
represents the permease saturation constant. The turnover number represents the
number of molecules of inducer that can be transported into the cell per unit time
per molecule of permease under saturating conditions.
[IAx] represents the
extracellular concentration of inducer [AM].
RpAsswvis assumed to be given by:
RPASSZV=8( [EX] - []
(31 )
where 6 represents the volumetric mass transfer coefficient [min"]. The form of this
rate expression was chosen to reflect; 1) the concentration dependent nature of this
mode of transport (Herzenberg 1959) and 2) the equilibration of the intra- and the
extracellular inducer concentration under balanced growth conditions in lacy strains
(Kepes 1960, Yagil and Yagil 1971).
When equations (30) and (31) are substituted into equation (29), one obtains:
-[]
Ra (2Y[
dt,Nk
d[Y] _R _k2 [ Y]
d(I
dt
dt pzl+
+ [IM]
8[
(
184
I])-J
k[
I]- I
) -k2 I]
(32)
(32)
(33)
where
k
2
1 dVc_ ln2
v
dt
d
(34)
k2 represents the specific growth rate of the cell. If necessary, degradative processes
can be easily incorporated into these equations by altering the value of k2.
6.4.5 Te Steady States
Substituting the steady state form of equations (33) and (27) into the steady
state form of equation (32), one obtains:
1+K1 [i]2
X, [I] -X 2 -a
1+K [] 2+K2 [RT]
(35)
where
_ kl
k,_[OT] 3 aa
k2
Da*
(36)
(38)
= P
X2-
+ Iz=x]
p
(37)
In the derivation of (35), the dilution of inducer due to cell growth has been
neglected relative to the loss inducer by export out of the cell or that > >k2 (table
185
6.2). Equations (35-37) along with the associated parameters (table 6.2) determine
the intracellular concentration of inducer under balanced growth conditions. Because
[Y] is linearly related to [I], via the steady state form of equation (33), equation (35)
also determines [Y]. The parameter A was introduced in order to group the
unknown parameters given by a, d and kl. With the exception of A, everything else
associated with this equation is known. The approach used to determine A is
presented in section 6.6.1.
Since the right and left hand sides of equation (35) scale linearly with the
generation and removal rates of [Y], respectively, they will be referred to as the such.
This greatly simplifies the comparison to experimental data (i.e. one of the principal
goals behind this analysis). The generation rate is easily interpretable using equation
(27). The removal rate represents the loss of permease due to cell growth.
Its
disguised form results when [Y] is eliminated from equation (32) using the steady
state form of equation (33). This manipulation reduces the dimensionality of the
system and facilitates further analysis. Another advantage of this form of equation
(32) stems from the fact that this equation
controllable variable, to [I].
relates [Idx], an experimentally
When solutions of equation (35) are sought (next
section), the existence of multiple solutions will be shown to depend on [IEJ.
6.4.6 Te
Hysteresis or Maintenance Phenomenon
The significantconclusion derived from the "maintenance"phenomenon is that
the transcriptional state of the lac operon is not a unique function of the
186
environment, as represented by [IBx]. Therefore, two physiologically distinct cell types
can coexist in the same environment.
This phenomenon is described in figure 6.13,
where the transcriptional state of the lac operon is shown to depend on both [IEX]
and the previous historyof the cell. If the environment encountered by an uninduced
cell is adjusted in a manner consistent with path I, (A) -> (B) -> (C), then induction
occurs only after the concentration of inducer reaches a "high"value. Intermediate
values of [Iwdj will not induce an uninduced cell. In contrast, if induced cells are
placed in an environment characterized by intermediate [IEX],they remain induced
(Path II). Taken together, paths I and II form a classic hysteresis loop.
An explanation for the maintenance phenomenon can now be provided by
figure 6.14 where the right and left hand sides of equation (35) are plotted as
functions of the intracellular inducer concentration with [IEX]as a parameter.
The
parameters in table 6.2 were used. Since the generation rate is independent of [IEX],
a single curve results. These figures indicate that the number of real solutions of
equation (35) depends on [IEx]. Path I in figure 6.13 can now be explained by the
series of lines presented in figure 6.14A At low [IEA],only the uninduced state is
available to the cell as noted by the single point of intersection between the removal
and generation curves. This corresponds to a low rate of permease generation and
a low [I].
As [IBx] is continuously increased, three intersections between the
generation and removal curves eventually result. Yet due to the stability of the
uninduced state, the cell will retain its uninduced state. However as [IEx]is further
increased, the removal curve eventually becomes tangent to P1. Any further increase
187
in [IEx]will result in the dissolution of the uninduced state. Because the generation
rate exceeds the removal rate, beyond P1, the cell becomes "attracted" to the induced
state. Consistent with the previous use of the term, [IEx]at P1 defines an induction
threshold.
Path II, in figure 6.13, can be explained by the series of lines shown in
figure 6.14B. Starting from the induced state, when [Iex] is reduced to the point
where the removal rate becomes tangent to the generation rate at P2, further
decreases in [Iex]result in the disappearance of the induced state. Since the induced
state cannot be maintained at this lower [IEx],P2 defines a second threshold with will
be termed the "maintenance threshold". As in the case with RecA, both thresholds
are static in nature since they qualitatively rearrange the phase portrait.
This hysteresis loop is more clearly visualized when the loci of intersections,
as shown in figures 6.14A and 6.14B, are plotted against a continuously varying [IEX]
(figure 6.15). This curve was generated by determining the solutions [I] of equation
(35) as [IEx] was varied from 10 to 1000MuMof TMG (see section 6.9.2 for details).
This curve is partitioned into three branches by P1 and P2. Each branch defines a
loci in the [IBx]-[I]plane that corresponds to a qualitatively different physical solution
(Iooss and Joseph 1990). The branch beginning at low [IEx]and continuing to P1
corresponds to path I in figure 6.13 and represents the uninduced cellular state. The
uninduced state is maintained until the induction threshold is reached (i.e. P1). The
branch beginning at large [IE] and continuing to P2 corresponds to path II of figure
6.13 and represents the induced cellular state. This state is maintained so long as the
value of [IE, remains above the value identified by P2. The branch between P1 and
188
P2 identifies the locus of unstable states.
As in the case with phage A, this locus
identifies the set of dynamic thresholds. Near this locus, small variations in [I], or
equivalently [Y], lead to the adoption of dramatically different cellular states,
depending on which side of this curve branch the perturbation places the cell. This
represents the threshold that was alluded to by Novick and Weiner (1957,1959).
This curve is consistent with several experimental
observations.
The
maintenance or hysteresis phenomenon is explainable simply by tracing the path
sketched out by the arrows. This corresponds identically to the closed path shown
in figure 6.13. Also the induction threshold corresponds to an [IEx] of approximately
100,uM TMG. This is consistent with experimental data since Novick and Weiner
(1957) fully induced cultures using an [IEx] of 250MuM.This curve also describes the
ability of cells to concentrate inducer. Rickenberg and coworkers (1956) and Kepes
(1960) found that cells in the induced state could concentrate inducer to levels
approximately 100 times that of the extracellular medium. This concentration factor
is described over a large range of [IEX]. The agreement between the maintenance
threshold predicted by this curve and that reported by Novick and Wiener is exact
since it represents a "fit" (section 6.6.1).
6.5 Sporlation by R subtil
6.5.1 T
Requirements for Threshold Behavior
Despite the apparent differences between the lac and the A systems, a
common regulatory scheme was found to control the initiation of adaptive gene
189
expression in both systems.
So long as the concentration of the transcriptional
activator responsible for induction is regulated by: 1) a positive feedback loop, 2) a
non-linear generation rate, of the cooperative type and 3) a concentration dependent
removal rate, threshold behavior is possible. These features were found to be
inherent to the molecular mechanisms known to regulate adaptive gene expression
in both systems.
Positive feedback was found to be necessary for an autocatalytic activation
mechanism to exist. This allowed the rate of generation of the transcriptional
activator to be expressed as a function of its intracellular concentration.
In the A
system, positive feedback results from the direct ability of CI dimer to activate its
own transcription. The positive feedback associated lac operon transcription is less
direct. According to figure 6.10, permease must first concentrate inducer. Only after
inducer is present at "high"intracellular concentrations will repressor be inactivated
and permease transcription initiated. The importance of positive feedback becomes
quite apparent when the feedback loop is "opened".
In lacY strains, physiological
heterogeneity, associated with the transcriptional state of the lac operon, is no longer
possible (Herzenberg
1959).
Non-linear generation rate laws, of the cooperative type, were found to be
necessary in order to provide the system with the mathematical ability to exhibit
multiple stable steady states. Such rate laws were found to be a natural consequence
of the need for two molecules of "inducer" to activate transcription, either directly
(phage A) or indirectly (the lac operon). This class of rate laws led to a point of
190
inflection at low inducer concentrations when the generation rate was plotted as a
function of intracellular inducer. When the removal rate curve was superimposed on
the generation rate curve, three intersections were possible. Each stable intersection
was found to correspond to a different transcriptional state.
Given a purely
hyperbolic generation rate law, of the Michaelis-Menten type, this would not have
been possible (Othmer and Tyson 1978).
6.5.2 The Threshold Requirements are Satisfied
The identification of the mathematical "structures" required for threshold
behavior provide the necessary framework in which to discuss the initiation of spore
development.
Based on what is known about the initiation process, all three
conditions appear to be met. The positive feedback loop associated with the B.
subtilis sporulation system is illustrated in figure 6.16. A detailed account of the
interactions implicit in this figure can be found in the manuscript by Strauch and
coworkers (1993). For comparative purposes, the positive feedback loop associated
with the lac system is also shown. In both systems, information on environmental
changes, which originate from "starvation" associated phenomena (sporulation system)
or from the concentration of a well defined inducer (lac system), is relayed to the
transcriptional apparatus of the cell. While the transduction mechanisms associated
with both systems might appear very different, functionally they are identical since
both result in the initial accumulation of intracellular transcriptional activator or
inducer.
In the ac system, this information is relayed through the direct
191
transportation of the inducer into the cell. In the sporulation system, this information
is relayed through the direct transfer of phosphate via the phosphorelay. Following
this initial boost of inducer, if the action of various negative regulatory elements can
be countered, an inducer amplification mechanism is set in motion via the positive
feedback loops. In the lac system, the lacI gene product must be countered. In the
sporulation system, AbrB and SinR must be rendered ineffective. Upon repressor
inactivation, more "transporter" proteins are expressed and eventually the fully
induced state is achieved. In the sporulation system, the transporting proteins
amplified are the phosphorelay gene products encoded by the kinA, spoOF and
spoOA
Full induction eventually results in a high level of phospho-SpoOA and the
subsequent expression of stage II genes (i.e. spollG, spolIE and spollA are
transcribed).
An important question is "Where does the non-linear generation rate, of the
cooperative type, originate ?" While a definitive answer is unavailable, a possible
explanation could involve the sinR gene product. sinR encodes for a developmental
switch protein that has been found to play a major role in determining which of a
number of adaptive responses the cell chooses (Bai et al. 1993). SinR is a repressor
of early sporulation gene expression and recent evidence suggests that it represses
stationary phase induction of spoOA (A.D. Grossman, personal communication).
SinR functions as a tetramer in vivo and its inactivation seems to be mediated
through the binding of multiple molecules of SinI (Bai et al. 1993). Since sinIl
expression relies on the spoOA and spoOH gene products (Bai et al. 1993), if two
192
molecules (more than two will also work) of SinI are required for SinR deactivation,
a nonlinear phospho-SpoOAgeneration rate, of the cooperative type, would result.
Also represented in figure 6.16 are the molecular processes that lead to
concentration dependent inducer removal rates. In the lac system, inducer removal
occurs by growth and inducer export (Kepes 1960). Under suboptimal induction
conditions, the inducer removal rate was found to represent a critical barrier to full
induction. In the sporulation system, the removal rate will depend on which phase
of the culture the cell resides. During the exponential phase, inducer removal might
be expected to occur via cell growth. During the stationary phase, recent evidence
suggests that phospho-SpoOA removal occurs by dephosphorylation
via the spoOE
gene product (Perego and Hoch 1991). spoOE encodes for a phosphatase that acts
to degrade phospho-SpoOA and its expression has been found to be under SpoOA
control. As will be shown in section 6.5.4, under suboptimal induction conditions, the
rate of removal of phospho-SpoOAcan be expected to represent a critical barrier to
full induction.
6.5.3 Threshold Model Descnbing Sporulaftion
The three components required for threshold behavior can be summarized by
the equation:
d[I]
d[
[I ] ) -R
=RGmn(
( [ I])
where [I] represents the intracellular concentration of phospho-SpoOA.
193
(39)
RGEN
and
RREM
represent the generation and removal rates of [I] and the [I] argument is shown
to denote the dependence of these rates on phospho-SpoOA. For simplicity, the
dependence of the generation and removal rates on other factors is not addressed
explicitly. This equation states that when an imbalance between the generation and
removal rates exists, the intracellular concentration of inducer will change. Based on
the arguments presented in the previous section, the functional dependence of RGEN
and RREMon [I] might be expected to be qualitatively similar to the curves drawn in
figure 6.17. The S-shaped generation curves results from the assumption that two
molecules of SinI are required to inactivate SinR, an assumed repressor of spoOA
The linear removal curves follow from the assumption that SpoOA-P is removed
according to a first order kinetic process. The intersections at low and high phosphoSpoOA concentrations would reflect the growth and sporulating states respectively.
Under balanced growth conditions, phospho-SpoOA removal curves similar to
those shown in figure 6.17A might be expected. The slope of these lines represent
the specific growth rate of the cell with faster growth rates leading to larger slopes.
As before, these lines represent the removal of phospho-SpoOAdue to the expanding
cell volume. Without loss of generality, the slope of these lines could also include the
action of any unknown phosphatases.
Since sporulation occurs even under growth
conditions (Schaeffer et al. 1965, Dawes and Mandelstam 1969,1970, Dawes and
Thornley 1970), this possibility is accounted for by the intersection at high phosphoSpoOA concentrations.
This model predicts that increased sporulation would be expected under
194
slower growth conditions. This follows from the observation that the phospho-SpoOA
concentration at the unstable steady state increases with increasing growth rates (i.e.
P1<P2<P3).
Recall that this concentration represents the dynamic concentration
threshold (see section 6.3.8) that separates the growth and sporulation states.
Experimental support for this prediction can be found in the literature (Schaeffer et
al. 1965, Dawes and Mandelstam 1969,1970, Dawes and Thornley 1970).
The ability to initiate stage II gene expression depends on the ability of the
cell to accumulate phospho-SpoOA. When nutrients become limiting and cells enter
the stationary phase, the slope of the removal curve decreases (figure 6.17B; curve
A) because the cell either stops dividing or divides more slowly. Under these
circumstances the rate of phospho-SpoOAgeneration will exceed its rate of removal
and phospho-SpoOA can be expected to accumulate within the cell according to
equation (39). Yet this rate imbalance does not continue indefinitelysince the spoOE
gene product also accumulates during the early stationary phase. The accumulation
of SpoOEwithin the cell can be expected to lead to a time dependent increase in the
rate of phospho-SpoOA removal, as reflected by the series of lines with increasing
slopes (figure 6.17B). Therefore unless the cell can accumulate a level of phospho-
SpoOA above a certain threshold (P) before the rate of phospho-SpoOA removal
balances its rate of generation (curve B), it will be locked in the uninduced state.
Whatever phospho-SpoOAis present eventually decreases since the rate of removal
dominates. In contrast, those cells with levels of phospho-SpoOA above this critical
concentration continue to accumulate phospho-SpoOA and eventually initiate stage
195
II gene expression.
6.5.4
A Mutations
kinA mutations act to decrease the rate of generation of phospho-SpoOA.
Because the kinA gene product is involved in the positive feedback loop needed for
phospho-SpoOA generation (i.e. kinA is a phosphorelay component), the phosphoSpoOA generation curve in kinA mutants will be attenuated. This might be reflected
by the generation curves shown in figure 6.18A. This conclusion was arrived at by
analogy with the ac system. In the ac system, an attenuated feedback loop could be
manifested by a lower permease turnover number, a, or a lower rate specific
permease generation constant, kl. Recall that both parameters directly effect the
amplitude of the generation rate curves shown in figure 6.14 via the parameter A.
The dynamic phospho-SpoOA threshold concentration is greater in kinA
mutants. As discussed previously, upon entrance into the stationary phase a series
of lines with increasing slopes would result due to the accumulation of SpoOE (figure
6.18B).
The removal curves would be unaltered since the AbrB pathway is not
affected by kinA null mutations (Perego et al 1989, Mueller and Sonenshein 1992,
this work). Therefore for a fixed removal rate (figure 6.18A) the critical threshold
will be greater in the kinA mutant relative to wild type as illustrated by points P1 and
P2. The increased threshold makes it more difficult for cells to attain the induced
state so that fewer cells in the population would be expected to initiate stage II gene
expression. The probability that a cell will overcome this increased threshold barrier
196
is further reduced by the lower phospho-SpoOA generation rate in kinA mutants.
According to equation (38), the rate of accumulation of phospho-SpoOA varies
directly with the generation rate. These predictions are in full accordance with the
data presented in chapter 5. This analysis suggests that even if a kinA mutant cell
is able to accumulate phospho-SpoOA above the boundary concentration, P2, it will
always contain less phospho-SpoOA relative to wild type even in the maximally
induced state as defined by P3 and P4. This might explain why the fl-galactosidase
expression in kinA cells containing a spoIA-lacZ
fusion was found to be lower than
in wild type (table 5.1, figure 5.11).
These arguments are all consistent with the effects that SpoOE variations have
on sporulation.
spoOE loss of function mutations have been found to restore
sporulation in kinA mutants to wild type levels (Perego and Hoch 1991). Since the
phospho-SpoOA removal curves would not increase rapidly, if at all, during the
stationary phase in spoOE mutants, phospho-SpoOA could accumulate unchecked.
In contrast, overexpression of spoOEwould lead to a more rapid increase in the rate
of removal of phospho-SpoOA.
This would lead to decreased sporulation since it
would reduce the amount of time available for the cell to accumulate phosphoSpoOA. This prediction is in accordance with experimental observations (Perego and
Hoch 1991).
6.5.5. spoOKMutations
Similar to the effects of the kinA mutant, this model predicts that the rate of
197
phospho-SpoOA generation will be lower in a spoOK mutant.
In contrast to kinA
mutations, the mechanism by which spoOK mutations reduce the rate of phosphoSpoOAgeneration is predicted to be different. Since the spoOK gene products are
presumably involved in mediating signal inputs into the phosphorelay (figure 6.16),
independent of the positive feedback circuitry, their contribution to the inducer
generation rate should not depend on the level of phospho-SpoOA. This is supported
by the finding that the spoOK operon is expressed constitutively (A.D. Grossman,
personnel communications).
Under this assumption, figure 6.18B would reflect the
effect that spoOKmutations have on the inducer generation rate. In contrast to kinA,
the spoOK mutation decreases the entire generation curve in a phospho-SpoOA
independent manner.
This figure suggests that when growth ceases and wild type
cells began to accumulate phospho-SpoOA, few spoOK cells even begin to accumulate
phospho-SpoOA,since the phospho-SpoOAinducer removal rate remains greater than
its generation rate, for phospho-SpoOA concentrations below P.
This analysis suggests that processes acting on at least two levels control
entrance into development via the phosphorelay. Figure 6.18B suggests that a signal
input threhold, mediated in part by the spoOK gene products, must be overcome if
the cell is initiate development. Figure 6.18A suggests that even if the signal barrier
is overcome successfully, initiation will also depend on the cell's ability to amplify or
accumulate phospho-SpoOA above a critical threshold. Support for this decoupling
was presented in Chapter 5 (figure 5.18) where the data obtained by several research
groups (Rudner et al. 1991, Weinrauch et al. 1990) was further analyzed.
198
It was
shown that the probability that a spoOK kinA double mutant will successfullyinitiate
spore formation, assumed to be reflected by the sporulation frequency, is given by the
product of the single mutant initiation probabilities.
6.6 Appendi: Fitting Experimental Observations
6.6.1 Determination of A
The parameters gathered from the literature (table 6.2) determined equation
(35) up to the adjustable parameter A. "A" is a lumped parameter comprised of both
reaction and transport terms (equation 36). By noting that an extracellular inducer
concentration of approximately 25M TMG: 1) sustains the induced state in induced
cells, 2) fails to induce uninduced cells and 3) defines the minimum value below
which cells cannot maintain the induced state (Novick and Weiner 1957), A can be
determined according to figure 6.17. The removal line shown represents the right
hand side of (35) for fixed f (table 6.2) and a [Ix] of 25MuM.A is determined by
varying its value until the left hand side of equation (35) becomes tangent to the
removal line. This results in an approximate value of A of 240.
6.6.2 Generation of the Hysteresis Curve
The approach used in to generate figure 6.15 involves the application of
elementary differential geometry. Equation (35) can be viewed as defining a curve
in the [Iy]<-[I] plane.
This curve is traced out as follows.
differentiated with respect to [I] to yield:
199
Equation (23) is
d[I EX]
g 1 +2K1[ I ]g
3g 4+g 2g 4
d[I]
(A+AK
[ 1 I
) +g 2
g2 g 3
g4 -
p
(40)
-
where
gl = -2AK 1
g 2 =K2[]
EX]i III
(41)
2
(42)
+K[I]
g3= [I] -IEx] I
[ IEX]
g4 =1 +P
(43)
(44)
Combining equations (40-44) with the condition: [IEx]=0.1 @ [I] =0.1, figure 6.15 can
be generated.
200
Table 6.1 Parameters used in Phage A Simulations
Parameter
Value'
Source2
[OT]
1 X 10-9 M
Johnson et. a 1981
KA
5 X 107 M-l
Ackers et. a. 1982
AG,
-11.7 Kcal/mol
Ackers et. a. 1982
AG 2
-12.1 Kcal/mol
Ackers et. al 1982
AG 3
-10.1 Kcal/mol
Ackers et. al. 1982
K1
1.77 X 108 M-l
AG,=-RTlnK,
K2
3.40 X 108 M-'
AG 2 =-RTInK 2
K3
1.32 X 108 M-'
AG 3 =-RTlnK 3
k 2,k3
0.023 min'
ki=ln2/rd
' The AG 2 value used includes the cooperative binding free energy.
The AG3 value does not include the cooperative binding free energy,
which results from interactions with CI dimer bound to the OR2
operator, since cooperation does not occur when both OR1 and OR2
are occupied (Ptashne 1987).
2 Physiological
temperature was employed (i.e. 370C).
201
Table 6.2 Parameters used in lac Operon Simulations
Parameter
Value'
Source
[O]1
1 X 10 9 M
Johnson et. al. 1981
[RT]K2
1 X 10S
Bourgeois and Monod 1970
K1
0.012 M- 2
Barkley and Bourgeois 1980
6
0.82 min -'
Kepes 1960
~
4500
IM
k2
0.023 min- '
k2= ln2/rd
A
240
Fit to data (see appendix)
Kepes 1960
' The value of K, shown is the square of the reported value. The
reported value was determined based on the binding of one molecule
of methyl-/-D-galactoside (TMG) to repressor.
202
Physiological Heterogeneity
PhysicalTransitions
Material
Balances
II
A
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
V
!
Single Cell
Models
I
I
Mathematical
State
Simulated Transitions
-----------------------------------
1
Mathematical
State 2
Multiple Stable Steady States
Fgure 6.1 Conceptualization of physiological heterogeneity from a biological and a
mathematical perspective. Distinct cell types are assumed to reflect cells with different
transcriptional states. The processes that determine how the cells acquire different states are
studied using mathematical models that exhibit multiple steady state behavior.
203
Lysogeny
.
Lysogeny
I
:
OR
I
I
T
Mathematical
State 1
-v
Mathematical
State 2
-=
Figure 62 The decision faced by bacteriophage A when integrated into cells of E. col.
Under suboptimal induction conditions, some phages will initiate lytic development while
others maintain a state of lysogeny. The divergent pathways undertaken by phages suggests
that a developmental "threshold"has been achieved by a fraction of the population. The goal
is to develop a mathematical model that can explain this threshold-like behavior.
204
OR Operator
A
I~~~~~~~~~
I
I
!i
L
PRM
PR
-- - - - - - - - - -
- - - - - - - _- _- -_ -
cl
cro
ye
NN
Transcription
Blocked
Polymerase
Lysogeny
B
-
PRM
cl
----------- -----------cro
Transcription
Blocked
RNA
Polymerase
,
.
Lysis
Figure 6.3 The genetic switch controlling initiation into lytic development. The switch,
associated with the OR operator, can be in the lysogenic or the lytic positions. In the
lysogenicposition (A) cI is transcribed and cro, a lytic specific gene is repressed. In the lytic
position (B), cro is transcribed and cI is repressed.
205
OR
Operator
cro block
0R21dime
ORI
IOR3
0
dimer
0
c-
Activated State
r-
cro block
c)
C)
0
C
Ct
Transcript
cl block
Repressed State
cro block
ta-~~~
o
Figure 6.4 Maintaining the lysogenicstate of the ORoperator. Three binding sites comprise
OR and CI dimer binds dominantly in the order shown (adopted from Ptashne 1987).
Positive and negative feedback loops maintain a stable lysogenicstate.
206
o0 8
C
E
6
0
4
.o
0
c
2
0oL
aL_
a:
0
0
2
4
Monomer Concentration
6
8
[M] X 10 8
Figre 6.5 Parametric, in kj, study of equation (9). All parameters are reported in table 6.1.
The curves shown are for kl=2, 4, 6, 8 and 10 min-'.
207
r%
a)
0
6
E
4
+-'
2
'I-
cE
0
0
2
4
6
8
A
U.4
0)
Q0
'
0.3
X
_
E
0.2
'0"
0.0
0.0
0.2
0.4
Monomer Concentration
0.6
[Ml X 108
Figure6.6 Multiple steady state solutions to equation (19). The intersections between the
removal and generation curves (A), identify the solutions of equation (19). The generation
curves shown are for kl=2, 4, 6, 8 and 10 min-'. The removal curve is set by the value of the
specific growth rate. For each generation curve, three solutions exist. Panel B represents
a blow up of the low concentration region where the two solutions at low repressor levels
become obvious. This plot allows a fit of kt to the reported concentration range (A).
208
f
^
U.0
O
0.6
0.4
(O
a: 0.2
nn
Uv.
0
1
2
Monomer Concentration
3
4
[M] X 108
Figure 6.7 Activation of the biological threshold. As the value of k 2 is slowly increased, a
point is reached where the phage (or cell) can no longer adjust to the increased "stress."
This results in the dissolution of the balanced growth state, that characterizes lysogeny, and
induction into lytic development. The degradation of monomer, by RecA, is represented by
an increase in k2, above the value set by the specific growth rate of the cell. The removal
curves shown are for k 2= 0.023 (removal solely due to cell growth), 0.092, 0.162 and 0.254
min-'. k2 =0.162 min-' approximates the position of the threshold.
209
6
a,
X
1o
x
c
4
4-
o
u)
o
2
0aC:
0
co
0
-20
0
20
40
60
-20
0
20
40
60
f%
n,'
U..U
C
1u.
a)
irX
c
0.20
o
'o
aa)
0.10
0
l-
Q
O)
0.00
Time (min)
Figure 6.8 Transient profiles of monomer and dimer (A) following a step increase (B) in k 2
from 0.023 (the growth rate effect) to a value above the critical threshold (i.e. 0.184 min -1).
210
4
0
Go
x
3
0
co
t_
Cu
L.
c
a)
2
o
0
c
o
L.
(D
E
1
0
c
0
oi
0
-2
0
20
40
60
80
S e f%^
U.3U -
B
At=31 min
At=30 nin
M
0.20-
k2=0.184 min
z
o
co
L
8 0.100
0.00
k2=0.023 min- '
0.00
-20
k2=0.023 rrin'l
I
20
0
20
40
60
Time (min)
Figure6.9 Transient response of monomer (A) followingpulse perturbations in k2 (B). The
width or time of the two pulses differ by one minute. When the phage (or cell) is subjected
to a thirty minute perturbation, it is able to reestablish lysogeny. However, lyticdevelopment
is initiated when the duration of the perturbation is increased by one minute.
211
LacY
(Permease)
T
Inducer Import
--
Inducer
Repressor
Inactivation
Repression
Lacl
(Repressor)
Fgure 610 Autocatalytic activation mechanism used to describe culture heterogeneity.
Inducer is transported into the cell by the lacy gene product. Inside the cell, inducer
indirectly activates lacYexpression by inactivating lac repressor (encoded by lacd). Cells that
have attained a threshold level of permease activate this loop and eventually become fully
induced.
212
Promoter
A) Uninduced
Operator
lad
lacZ
lacY
M~
Repressor
Promoter
lad
I
Operator
WlacZ
I
lacY
B) Induction
Promoter
C) Fully Induced
lad
Operator
VII
I
lacZ
lacy
URNS
RA/
I
I
I
Transcript
O
Figue 6.11 Negative regulation of the lac operon. In the uninduced state (A), repressor
blocks transcription. The affinity of the allosteric repressor complex for the operator is
reduced upon binding to two inducer molecules (B and C).
213
6
c)
co
4
0
aQ)
4+J
c-
o
2
C_
cO
0
0
2000
4000
6000
Inducer Concentration
8000
10000
[PaM]
Figure 6.12 Parametric, in kl, study of equation (27). All parameters employed are reported
in table 6.2. The curves shown are for k 1=30, 40, 50 and 60 min'.
214
F
0
0
n
F
w
un
Path
I
II
(dotted)
-_.
V
A
Path
B
C
(solid)
0
Uninduced
+
Induced
Increasing
Extracellular Inducer
gurfte6.13 Hysteresis or maintenance phenomenon associated with induction of the ac
operon. When inducer is added to uninduced cells (Path I) induction occurs only at "large"
inducer concentrations. In contrast, when inducer is partially removed from induced cells,
the induced state is maintained at inducer concentrations that cannot induce uninduced cells
(Path II).
215
8
6
a)
(V
a:
Cn
C,
4
a)
o0
(D
C:
2
E
0
-2
0
100
200
300
400
-1r% 11
)UU
a)
200
o
E 100
0
0
1X10 4
2X10 4
3X10 4
Inducer Concentration [M]
Figure 614 Explanationfor the hysteresis phenomenon in terms of threshold phenomena.
Equation (35) is plotted with different removal rates, as determined by [IEx](see table 6.2
for parameter values). Panel A is a blow-up of the low concentration region. P1 (A)
represents the induction threshold. P2 represents the maintenance or extinction threshold
(see text).
216
4 '5
Il-
2 104
0
L.
0
-
0
c- 105
C
In 1
101
Extracellular Inducer (M
Fgue
10 3
102
TMG)
6.15 The hysteresis curve generated by equation (35). As [Ia] is increased, the
number of intersections between the generation and removal curves (figure 6.14) varies from
one to three to one (excluding the points of tangency). These intersections appear as points
on this curve for each [IEx]. P1 and P2 represent the induction and the extinction thresholds
respectively.
When [IEx] is increased beyond P1, full induction occurs.
When [IEX] is
decreased below P2, cells lose the ability to maintain the induced. For [IEx]between P1 and
P2, the cell can be in either state depending on its past history.
217
Sporulation Signals
SpoOE, Growth
via SpoOK
I
Phosphate
Transport
Phosphorelay
l
Components
Inducer Removal
, Phospho
SpoOA
Inadivation
(direct and indirect
Indirect Repression
of Transporters
Extracellular Signlals
AbrB, SinR
(Repressors)
Export, Growth
via lac Induce3r
Inducer Removal
LacY
1
Transport
I(Permease)
Intracellular
Inducer
II
I nactivation
Repression
of Transporter
Lacl
(Repressor)
Figure 6.16 Positive feedback loops associated with the amplification of phospho-SpoOA
(top) and lac inducer (bottom).
218
a)
r0
E
1)
c-
c
(U
U)
(o
P1P2
P3
(Ur
Fa
0
E
c
co
C
0
c)
(9
P
Phospho-SpoOA Concentration
Fgue 6.17 Threshold model describing initiation into sporulation. Panel A represents how
the cell might maintain a homeostatic balanced growth state by balancing the rates of
phospho-SpoOA generation and removal. Panel B represents how the time dependent
increase in SpoOE eventually balances the unchecked accumulation of phospho-SpoOA during
the early stationary phase.
219
a)
0
CC
O
CE
(o
-O
(c
P1 P2
P3
P4
a)
cr
4-A
0
E
a)
CC
c
0
c
a)
(9
P
Phospho-SpoOA Concentration
Figure 6.18 Model describingthe effects that kinA (A) and spoOK(B) mutations might have
on the rate of phospho-SpoOAgeneration and hence the ability of the cell to accumulate
phospho-SpoOA.
220
300
0
4-,
40
200
CC
'I-
0r0
40
E 100
c
0
0
0.5X1 0 4
104
Inducer Concentration [pM]
Fegu 6.19 Generation rate curve fit to the data of Novick and Wiener (1957). The point
of tangency represents the static or "maintenance threshold." If the slope of the removal rate
curves is slightly increased (i.e. for slightly lower [IEX], see text), the cells are no longer
capable of maintaining the induced transcriptional state.
221
Chapter 7: Summary and Conclusions
7.1 Summary
An investigation aimed at better understanding the origins of physiological
diversity associated with sporulating cultures of B. subtilis was undertaken. The
objective was to better understand how the heterogeneity known to exist when cells
acquire the terminal differentiated state was influenced by events occurring much
earlier in the stationary phase. Single cell measurements of gene expression were
combined with genetic analysis in order to identify how events occurring at the level
of stationary phase signal transduction affected early developmental gene expression
as determined through the use of a number of temporally separated developmental
markers.
Theoretical studies, made possible through the development of
mathematical models, were used to demonstrate that the experimentally derived
conclusions were not only physically possible but consistent with the molecular
processes known to regulate early sporulation.
7.L1 Single Cell Measurements
Gene expression was studied on the single cell level, through the development
of a flow cytometry-based technique capable of estimating the relative sizes of B.
subtiis populations expressing -galactosidase. Phospho-SpoOA controlled gene
expression was then studied, on the single cell level, through the use of
chromosomally integrated spo-lacZ fusions. The technique involved taking samples
222
from stationary phase cultures, staining with a fluorogenic P-galactosidase substrate
and analyzing green fluorescence, due to C8 -fluorescein, by flow cytometry.
The
staining conditions were optimized in order to minimize non-specific fluorescence
while permitting the generation
expressing cell populations.
of detectable signals in known
-galactosidase
The ability to apply this technique experimentally was
assessed based on the ability to recover known cell subpopulations in controlled
mixture experiments and to correlate sample fluorescence with culture average
measurements of f-galactosidase activity.
In all cases when fermentation samples were analyzed, an internal control was
added to the experimental cells in order to control for background fluorescence and
leakage of the fluorescent product out of cells containing -galactosidase. The
internal control was an aliquot of cells that did not contain a lacZ fusion, and that
had been stained with PKH26 to allow the control cells to be distinguished from the
experimental cells using two color flow cytometry.
This insured that the level of
fluorescence background was not problematic and permitted a quantitative estimate
of the size of the cell population exhibiting fluorescence above background.
7.1L2
perImental Analis
of Gene Expresion
The processes associated with early development were studied at the level of
signaltransduction and transcriptional activation viathe phospho-SpoOAtranscription
factor. Single cell measurements were used to estimate the distribution of spo gene
products, using lacZ reporter fusions, within a population. The spo markers analyzed
223
spanned early development ranging from the indirect activation of one of the earliest
genes expressed upon entrance into the stationary phase, spoVG, to the expression
of a gene known to be exclusively expressed in the mother cell, spollD.
Culture average measurements of phospho-SpoOAcontrolled gene expression
were found to reflect the behavior of two cell populations. One population exhibited
high expression with the other exhibiting little or none.
The size of the high
expressing population was found to correlate with the final sporulation frequency.
Well defined genetic mutations, in genes involved in signal transduction, were used
to perturb the level of intracellular phospho-SpoOA and the effects monitored using
single cell measurements. It was found that mutations that reduce the level of
phospho-SpoOA resulted in a redistribution of the relative sizes of these populations
with fewer cells residing in the high expressing population. Again, the size of the high
expressing population was found to correlate with the final sporulation frequency.
7.L3 Quantitative Analysis of Gene Expression
Quantitative
studies were used to demonstrate
the feasibility of the
experimentally derived conclusions and to provide a conceptual framework in which
to study the relationship between the biochemical rate processes that control cell
behavior. Feasibility was demonstrated by the ability to extract mathematical models,
from the accepted state of genetic regulation, capable of explaining how distinct cell
types can coexist in the same experimental environment.
Initiation of adaptive
cellular processes was analyzed in terms of the rate processes that determined the
224
intracellular level of transcriptional activator. The resulting model was shown to be
consistent with experimental results presented in both, this thesis and the literature.
The conceptual framework provided by the model allowed a hypothesis to be
put forth regarding how various processes interact with the phosphorelay. In the
simplest analysis, two states are assigned to the phosphorelay and both act to control
entrance into sporulation. The default state, present during vegetative growth,
regulates entrance into development at the level of signal inputs into the
phosphorelay. This state is characterized by basal levels of certain phosphorelay
components including kinA, spoOF and spoOA and constitutive expression of the
spoOK operon. The induced or the "activated" state of the phosphorelay is attained
once cells overcome the signal threshold barrier.
This state is characterized by
elevated expression of certain phosphorelay components, as made possible by
activation of the AbrB pathway and SinR deactivation. To fully induce stage II gene
expression, the activated state of the phosphorelay state must succeed in amplifying
phospho-SpoOA to a critical level, by accelerating its rate of generation, before
competing processes begin to dominate.
225
7.2 Conclution
1) Threshold mechanisms control entrance into the sporulation pathway and these
threshold are associated with processes that interact with the phosphorelay.
2) The phosphorelay represents a critical bottleneck to early developmental gene
expression and represents one of the principal means by which cells regulate the
concentration of phospho-SpoOA.
3) The intracellular level of phospho-SpoOAdetermines a transcriptional state of a
B. subtilis cell.
4) Culture heterogeneity, associated with incomplete spore formation, results in part
by the inability of cells to expressed some of the earliest genes known to be involved
in the sporulation process.
5) So long as the concentration of the transcriptional activator responsible for
initiating gene expression is control by a positive feedback loop, a non-linear
generation rate of the cooperative type, and a concentration dependent degradation
rate, threshold potential exists.
6) Threshold potential is inherent to the molecular mechanisms known to regulate
adaptive gene expression in a number of genetic systems including; 1) induction of
the lac operon, 2) induction of lytic development by phage 1 and induction into the
sporulation process.
7) Threshold mechanisms provide the cell with a stable means of maintaining
homeostasis while allowing it to initiate adaptive responses when extreme
perturbations exceed its ability to compensate.
226
Chapter & Recommendations and Future Research Areas
81 Biological Studies
81.1 Signal Inputs versus Signal Amplification
The experimental and theoretical results obtained from the analysis of the
signal transduction processes needed to generate phospho-SpoOA, suggests that the
kinA and spoOK gene products act at different levels. Both are involved with
phospho-SpoOAgeneration however it is hypothesized that the spoOKgene products
are involved transmitting the initial signals into the phosphorelay.
In contrast, it is
suggested that the kinA gene product in principally involved in amplifying the level
of signal inputs into the phosphorelay in order to amplify the rate of phospho-SpoOA
generation.
This conceptual framework might provide the vehicle in which to analyze the
finer regulatory features associated with stationary phase signal transduction. A
number of experiments are suggested in order to explore this hypothesis. It would
be interesting to look at spoVG expression in a spoOK kinA double mutant. Since
spoVG expression is normal in a kinA mutant, the multiple threshold hypothesis
would predict that the double mutant would behave similar to a single spoOKmutant.
Because kinA is expressed at low levels during exponential growth, it would also be
interesting to investigate how early gene expression is altered, if at all, by mutations
in the kinA vegetative promoter in wild type and spoOKmutants. Such experiments
could help to sort out whether the initial signals responsible for activating the AbrB
227
pathway, presumably originating from spoOK mediated processes, flow into the
phosphorelay via kinA.
8&2 Adaptive Cellular Processes in Other Biological Systems
The approach
employed in this thesis to study heterogeneity should be
applicable to the analysis of other biological systems. Analysis of competence
development in B. subtilis, mating in S. cerevisiae, chemotaxy and a host of other
adaptive response mechanisms exhibited by cells are in theory amenable to similar
analysis. The conclusions derived from this study could possibly provide a conceptual
framework in which to study signaling processes and adaptive cellular mechanisms in
higher organisms. Such systems are not amenable to the advanced genetic techniques
available to bacterial systems. This includes a number of industrially relevant
biological systems such as stem cell differentiation, neural signal transduction and Tcell development.
Since single cell measurement technologies are quite advanced in
these systems, the convoluting effects that might be introduced by physiological
heterogeneity can easily be taken into account.
&3
Biologica Control Systems Theory
The theoretical studies performed were aimed at identifying important
biological control structures capable of imparting decision making abilities to cells.
Strict emphasis was placed on interpreting these control structures in terms of known
regulatory mechanisms. The generality with which positive feedback and cooperative
228
generation, can be found in biological systems suggests that this simple approach to
analyzing biological systems might prove to be useful in the future. Such an approach
is in direct contrast to the methods currently aimed at describing biochemical
networks in their entirety.
For example, it might be interesting to analyze how the cell regulated its
intracellular level of PEP, by regulating the flow of glucose and related products via
the PTS system and glycolysis in terms of the control structures analyzed in chapter
6.
The PTS system exhibits striking similarities with the phosphorelay as first
observed by Burbulys and coworkers (1991). Furthermore, PEP serves to positively
regulate the flow of reactants into the network and hence indirectly regulates its own
intracellular concentration. The approach taken to analyze such systems (chapter 6)
might be fruitful since emphasis is only placed on the key control structures as
opposed to all processes involved.
&2 Single Cell Measurements
82.1 Single Cell Analsis: Fluorescence Microscopy
The technique used to obtained single cell measurements of gene expression
involved the sole use of flow cytometry. However it might be useful to compare the
results obtained from this technique with fluorescence microscopic observations. The
advantage afford by FACS is that samples may be analyzed less subjectively and
simultaneous multicolor analysis can be used to control for background. This allows
assay conditions to be readily assess and subsequently optimized. In conjunction with
229
FACS optimized assay conditions, fluorescence microscopic observations would
provide reliable visual observations which could be used to calibrate and compare the
sensitivities inherent to each technique.
However the drawbacks with fluorescence
microscopy is that in order to practically count a large number of cells (i.e. several
hundred), the concentration of cells must be relatively high compared to the
concentrations amenable via flow cytometry.
Preliminary work along these lines
indicated that interference could be a problem.
&2.2 The Future: Strain Improvement via Flow Cytometry
Strain improvement and selection via flow cytometry represents a extremely
challenging area of industrial microbiology, which is very much in its infancy, with the
potential for great payback. While much effort is currently being directed towards
pathway modification in order to increase culture productivity, the excellent screening
features provided by FACS should not be forgotten. Presently the limiting steps lie
in the areas of instrument sensitivityand probe development. Yet this is somewhat
understandable since the development of FACS technology has been principally
guided by eukaryotic biology where such issues are less of a problem.
Eukaryotic
biologists have taken advantage of this feature and strain selection in the form of
clone isolation and hyperproducer isolation is routinely employed. Until equivalent
resources are committed to the development of microbiologyspecific instrumentation
and protocol development, bacterial flow cytometry will remain in its infancy.
230
Chapter 9: Materials and Methods
9.1 Strains, Maintenance and Cultivation Conditions
9.L1 Strains
The B. subtilis strains used throughout this work are listed in table 9.1. All
strains derived from JH642 contain the trpC2 and phel mutations (Perego et al.
1988). SMY was obtained from the laboratory of A.L Sonenshein (Mueller and
Sonenshein 1992). The spoVG-lacZ translational fusion, present in both the JH642
and SMY backgrounds, was carried in the specialized transducing phage SPf and has
been described (Zuber and Losick 1983). The spollG-lacZ transcriptional fusion,
present in the JH642 background, was carried in the specialized transducing phage
SPf and has been described (Kenney et al. 1989). The spoIIA-lacZ transcriptional
fusion, present in both the JH642 and the SMY backgrounds, was carried on the
integrative plasmid pPP81 and has been described (Piggot and Curtiss 1987). The
spoIID-lacZ (Stragier et al. 1988) transcriptional fusion was located in the nonessential amyE locus and has been described. The veg-lacZ (Fouet and Sonenshein
1990) transcriptional fusion was carried on the integrative plasmid pAF25 and has
been described. The kinA::Tn917 null mutation (Sandman et a. 1987) and the
spoOK mutation (Rudner et al. 1991) have been described.
9.1.2 Routine Strain Maintenance and Storage
LB medium (10g/l Tryptone, 5g/l Yeast Extract, 10g/l NaC1, pH 7.0, Davis et
231
al. 1986) supplemented with 5g/l glucose was used for routine strain growth and
maintenance.
All strains were stored at -800 C in a 17% glycerol solution. The cell
stocks were grown in Difco sporulation media (8g/1 Difco nutrient broth,
lg/lKCI,
0.25gAlMgSO 4-7H 2 0, mM Ca(NO 3 )4, 0.01mM MnCl2, 0.001mM FeSO 4; Harwood
and Cutting 1990) or LB (5gl glucose) and just prior to the onset of the stationary
phase lml of culture was added to 0.5ml 50% glycerol and the mixture was stored at
-800C.
9.L3 Cultivation
Prior to each experiment, -80°Ccell stocks were reconstituted using LB plates
supplemented
with 5g/l glucose and the appropriate
antibiotic concentrations
(Harwood and Cutting 1990). Following approximately twelve hours of growth at
37oC, single exponentially growing colonies were used to inoculate 25ml of
prewarmed Difco Sporulation Media (DSM), with and without 5gl1glucose, in 250ml
baffled flask without antibiotic selection.
vigorous agitation.
The flasks were cultivated at 37°C with
Genetic stability was verified by replica plating late stationary
phase cultures onto the appropriate antibiotic plates. Single colony inoculations
insured that at least five doublings had occurred before the cells entered the
stationary phase.
SMY::pAF25 (Fouet and Sonenshein 1990) was used as the
-galactosidase
positive control in controlled mixture staining experiments involving C12-FDG. Cells
were grown in DSM supplemented with 5g/l glucose to an OD"o of 3.5-4.5, recovered
232
by centrifugation,
washed
two times with room temperature
PBSM (50mM
phosphate; 100mM NaCI, 10mM MgC12; pH 7.0), resuspended in the original volume,
then placed on ice for further use. The cells used to generate the controlled mixture
distributions shown in figure 3.13 contained an average specific activity of 240 Units
as defined below. The cell number density, corresponded to approximately 1.5x109
cells/ml as determined using a Coulter Counter. This corresponded to approximately
3xlO8 cells/mlper OD660.SMY, which does not contain a acZ fusion was used as the
negative control in all staining experiments performed using the SMY background
and was cultivated under similar conditions.
MB284 (spoVG-lacZ in SMY; Mueller and Sonenshein 1990) was used as the
B-galactosidase positive control in controlled mixture staining experiments involving
C8 -FDG (sections 3.84-3.8.5).
Cells were grown at 370C in 25 ml (DSM) without
glucose and harvested two hours (i.e. at T) into the stationary phase.
This
corresponded to an OD60 of approximately 2.0 and a cell density of approximately
lx109 cells/mi. The culture average specific f-galactosidase activity of samples used
in controlled mixtures (section 3.8.5) was approximately 112 Units as defined below.
9.2 Analytical Techniques
9.21 Specific 4alactosidase
Activity (ONPG)
Cells were recovered by centrifugation, washed twice with phosphate buffered
saline supplemented with magnesium (PBSM; 50mM phosphate; 100 mM NaC1, 10
mM MgCI2; pH 7.0) and either stored at -20°C for later analysis or on ice for
233
immediate analysis. Bf-Galactosidase was assayed in whole cells using the substrate
o-nitrophenyl-f-D-galactoside (ONPG) according to the procedure of Miller (1972).
Cell suspensions were combined with Z-buffer (60mM Na2 HPO4 , 40mM NaH2 PO 4,
10mM KCI, lmM MgSO4, 0.27 % v/v P-mercaptoethanol,
pH 7.0) such that the final
volume was 1 ml. 10ul toluene was added (1% final concentration) and the solution
vortexed vigorouslyfor at least ten seconds. The tubes were then gently shaken at
37oCfor 30-40 minutes to allow the toluene to evaporate. The cell suspensions were
then incubated at 30°C for ten minutes and the reaction was started by the addition
of 0.2ml of prewarmed ONPG (4g1 ONPG in an aqueous solution of 10.5g/1K2HPO4,
4.5 KH2 PO 4, g/l (NH4 )2 SO4 , 0.5g/l sodium citrate-2H 2 0). The reaction was allowed
to proceed until a faint yellow color appeared (at least ten minutes elapsed in all
samples) and the reaction stopped by the addition of 0.5ml 1M Na2CO3. The optical
density was recorded at 420nm and 550nm. Light scatter due to cell debris was
corrected using the formula given by OD42-1.75OD55s (Miller 1972). One unit of
activity is defined as AA420per minute per ml culture per OD660X 1000 at 30°C.
Spectrophotometric
spectrophotometer
measurements were made using a Konitron, Uvikon Model 810
using a 1 cm lightpath.
9.2.2 Relative.Galactosidase
Activities (MUG)
Relative enzymatic activities of sorted populations were determined using the
fluorogenic substrate 4-methylumbelliferyl-f-D-galactopyranoside(MUG) using a
modification of the procedure described by Fiering and coworkers (1991). 5,000 to
234
50,000 sorted cells were added to Z-buffer such that the final volume was
48 0&1. fB-
mercaptoethanol was added so that the final concentration was 2.7 parts to 1000
(Miller 1972). Toluene was added to a concentration of 1% and the mixture was
incubated at 37°C for 10 minutes in to equlibrate the samples.
12 0
1lof a 3mM
MUG solution was added to the equilibrated mixture and the reaction time recorded.
The 3mM solution was obtained by diluting one part of a 30mM MUG stock solution
(stored at -200 C, in N,N-Dimethylformamide) with nine parts Z-buffer without
P-
mercaptoethanol. Samples were incubated at 37°C anywhere from ten minutes to
five hours with periodic monitoring, in order to insure a detectable signal had
developed, and the reaction was stopped by adding 300julof MUG stop solution
(15mM EDTA, 300mM glycine,pH 11.2). A blank consisting of an equal number of
lacZ' cells, incubated for an identical time period, was used to record the fluorescence
background.
Under the cultivation conditions employed, background due to
endogenous P-galactosidase was not detected.
The fluorescence intensity for each sample was determined using a Perkins
Elmer Model LS-5spectrofluorimeter. The excitation and emission monochrometers
were set to 3nm centered about 355nm and 460nm respectively. An arbitrary
enzymatic activity unit was defined, as AFluorescence per minute per cell, in order
to allow comparisons between samples analyzed under identical conditions (i.e. at the
same time).
Since only the relative activities of various subpopulations within a
culture sample were desired, absolute measurements were not needed (section 3.8.4).
The linearity of this assay with respect to time and cell numbers, or equivalently
235
enzyme activity, is shown to figure 9.1.
9.23 Spores
Sporulation frequencies were determined by serially diluting culture samples
in T'-Base (2g/l (NH4) 2 SO4, 14g/l K2HPO4, 6g/l KH2 PO4 , lgfl Na 3 -citrate-2H 2 0)
followed by heating the samples for 15 minutes at 80C.
100zl aliquots were plated
onto LB plates supplemented with 5g/l glucose. After a 12-15hour of incubation, at
37°C, colony counts were performed.
Colony counts were repeated after 24 hours
to account for delayed spore germination.
At least 100 colonies were counted in
order to determine the sporulation frequency. The frequency of sporulation
represents the ratio of the number of colonies obtain with and without the heat
treatment.
9.2.4 Cell Numbers
Cells numbers were determined using a Coulter ZBI Coulter Counter
equipped with a 30Mum
orifice (Coulter Electronics, Hialeah, FL). The electrode
electrolyte was a 4% NaCI solution. Isoton II (Curtin Matheson, Boston MA) was
used as the sample diluent. 1/Ampere was set to 0.354 and 1/Amplification was set
to 1/4. 25ml of Isoton II was dispensed into the sample Acuvettes (Curtin Matheson)
and typically from 5 to 20/zl of cells were added to the solution.
This dilution
resulted in a cell concentration from 1x105 to 3x10s cells/ml so that counts between
10,000-30,000 were obtained per 100l of dilute cell solution. All cell counts were
236
performed in triplicate and the arithmetic means employed. Both, the Isoton II and
the 4% NaCI electrolyte solution were filtered twice using a 0.2tm filters prior to use.
9.3 Cel Staining
9.3.1 PKH26 Staing
lacZ' cells to be stained with the membrane binding dye PKH26 (Zynaxis,
Malvern PA) were harvested according to the procedures described above.
cells, corresponding
to approximately
2x106 cells, were pelleted
2 00
/l of
in a 1.5 ml
microcentrifuge tube, resuspended with 10/ul PBSM then rapidly but mildly mixed
with 1001Alof Diluent C (Zynaxis). This solution was immediately added to a room
temperature solution containing 51AMPKH26 in 100/zl Diluent C (i.e. 2/1 of the 1mM
PKH26 stock was added to 1001 l Diluent C). The solution was gently but thoroughly
mixed then incubated at room temperature for five to seven minutes. The staining
reaction was stopped by diluting the solution 1/10 with room PBSM containing 20g/1l
bovine serum albumin (BSA). The mixture was incubated for one minute then the
cells were recovered by centrifugation. The cells were washed three times with room
temperature PBSM, resuspended in 200/A1PBSM then placed on ice. It was found
to be absolutely essential that all solutions used be filtered.
0.2/Mmlow protein
binding filters were used (Part No. 4192, Gelman Sciences, Ann Arbor MI) in this
work. To insure that the level of PKH26 specific fluorescence was greater than that
of background (detected by the PKH26 optical system; figure 9.2), it was sometimes
necessary to increase the dye concentration to 7.5aM. Alternatively, the number of
237
cells to be stained could be reduced. The precaution was taken because the final
PKH26 fluorescence was found to depend on the amount of total surface area in the
sample to be stained relative to the dye concentration.
9.3.2 c-FDG
Cells taken from late exponential phase cultures containing glucose were
stained with the fluorogenic
-galactosidase substrate C 12 -FDG. 1x106 cells were
added to lml of PBSM containing 33MM C12-FDG, 0.4 g/l sodium azide and x106
lacZ' cells, stained with PKH26. All mixture constituents were contained in Falcon
2054 polystyrene tubes (Becton Dickinson), since these tubes allowed analysis via flow
cytometry without the need to transfer the sample. The solution was incubated at
50°C for 10 minutes then immediately placed on ice. Samples were analyzed by flow
cytometry 8-12 hours later.
The hydrophobic nature of the C12-FDG placed upper limits on the dye
concentration in PBSM. To insure that the substrate was dissolved, the solution was
heated for 5 minutes at 45°C, then filtered through a 0.2/M low protein binding filter
(Gelman Sciences). Filtering was found to remove the fluorescent particles that
tended to accumulate in the substrate stock solution. Low protein binding filters,
along with heat treatment were necessary to prevent the substrate from being
removed from solution upon filtering.
In contrast to the SMY strain, SMY::pAF25 eventually sporulated in DSM
(5gAlglucose). Presumably this was due to an exhaustion of glucose, facilitated by the
238
expression of veg-lacZ Under such conditions, the C 12 -FDG could not be sufficiently
loaded into the cell under the described conditions. This problem was overcome
using the C8-FDG protocol described in the next section. In fact, this observation led
to the identification of CS-FDG as the optimal substrate for stationary phase cells
(section 3.8.2).
9.33 CFDG
Stationary phase samples to be stained with the fluorogenic -galactosidase
substrate Cs-FDG were harvested as described above.
4 50
5 0/p1of
cells were added to
1Ml
of PBSMA containing 1% w/v toluene and 1x106 lacZ cells stained with
PKH26. The mixture was vortexed vigorously for at least ten seconds then diluted
1/100 with a solution of PBSM containing 33/AM C-FDG.
The samples were
incubated for 30 minutes at room temperature to allow a steady state fluorescence
distribution to be reached, then analyzed by flow cytometry. As shown in figure 3.17,
a steady state fluorescence distribution was obtained after a few minutes at room
temperature.
CS-FDGwas purchased from Molecular Probes (Eugene OR) in the form of
a desiccated solid. To reconstitute the substrate, 627;/l of a solution containing lml
H20 and 250/u1dimethyl sulfoxide(DMSO) and added to 5mg C8-FDG. The mixture
was vortexed then stored at -20°C. This stock solution was then used to makeup a
33/aM solution for cell staining. Prior to use, the staining solution was filtered using
a 0.2,um low protein binding filter. In contrast to C12-FDG, the heat treatment was
239
not found to be necessary.
9.4 Flow Cytometr
9.4.1 The Flow Cytometer
The flow cytometer used in this work is a FACStar Plus (Becton Dickinson,
Mountain View CA). A simplified schematic of this instrument is presented in figure
9.2. Cells were hydrodynamically focused in order to pass cells single file through
argon laser outputing 100mW of 488nm light. For routine analysis, the sheath fluid
consisted of Isoton II (Curtin Matheson). Upon illumination, the amount of light
scattered and given off by fluorescing molecules associated with the cells was
monitored. Forward light scatter was detected using standard photodiodes. In order
to detect cells using forward scatter, the forward scatter neutral density filter (a
standard instrument feature) was removed. Fluorescent light was collected and
passed through a 560nm short pass dichroic mirror. The short wavelength light
generated by fluorescein (i.e. the light less than 560nm which is passed by the
dichroic mirror) was then passed through a 530 band pass filter (30nm bandwidth at
half transmission; 525 BP 3 band pass optical filter; Omega Optical, Brattleboro, VT).
The longer wavelength light generated by PKH26, the component reflected by the
560nm short pass dichroic mirror, was then passed through a 575nm band pass filter
(26nm bandwidth at half transmission; 575 BP 3 band pass optical filter; Omega
Optical).
The fluorescing light generated by C.-fluorescein was gathered using a
photomultiplier tube operating between 700-800 volts. The resulting signal was
240
discretized using a four log decade amplifier.
The fluorescing light generated by
PKH26 was gathered using a photomultiplier tube operating between 600-700volts.
This resulting signal was discretized using a five log decade amplifier.
The
photomultiplier outputs transferred to a MicroVax 3000 real-time work station using
a 12-bit ADC.
9.4.2 CeSorting
Sorting experiments were performed using a 70/Amjet in air nozzle and cells
were sorted into cold Z-buffer (Miller 1972) without P-mercaptoethanol. In sorting
experiments, Z-buffer, without fi-mercaptoethanol, was also used as the sheath fluid.
The drop drive was operated at 23,000 Hz and the sheath pressure was set to 10psi.
Due to the noise inherent in the forward scatter (FSC) signal with the drop drive
operating, it was necessary to trigger sorting on the side scatter (SSC) signal.
The fluorescence gate used to sort the high expressing population was set by
the green fluorescence distribution of the PKH26 background control contained in
a sample identical to the sample to be sorted. The actual sample sorted did not
contain an internal control in order to avoid possible contamination of the sorted
populations by the artificiallyintroduced population. The samples to be sorted were
stained according to the procedures described above (C-FDG) then maintained at
4°C, using a refrigerated water bath, during the duration of the sort. To obtain a
certain number of sorted cells, the base assay volume (i.e. 0.5 ml) was scaled linearly
in all components in order to achieve the desired cell numbers.
241
The cell density of the sorted population was set by the capabilities of the flow
cytometer. Based on the number of events sorted and the volume of the sorted
sample, the cell number density of the sorted sampled was determined to be
approximately 3.3x10 cells/ml. This calculation was performed in order to determine
the volume of sorted sample to be used when relative enzyme activities were to be
determined according to the MUG procedure (section 9.2.2).
9.43 Data Acquisition and Analysis
The flow cytometer was interfaced to MicroVAX 3000 operating under the
VMS operating system. ACQ8 was used to acquire data in real-time (Becton
Dickinson).
Off-line one and two color data analysis was performed using DISP4,
DISP2D (Becton Dickinson, San Jose CA) respectively. Parameter conversions were
performed using CALC4 (Dr. Robert Murphy, Carnegie Mellon).
Non-linear
regression used for fluorescence distribution analysis was performed
using the
MODFIT program operating under the Windows environment (Verity Software
House, Topsham ME). This program utilizes the Levenberg-Marquart algorithm
(Press et a. 1990) in order to minimize the chi-squared quantity. An example
histogram is shown in figure 9.3 (this distributions corresponds identically to the
distribution shown in figure 3.16B). MicroVax 3000 based Flow Cytometry Standard
(FCS) files were downloaded to an IBM compatible (Northgate 486) using the
KERMIT file transfer protocol.
The use of logarithmic amplifiers necessitated the conversion of the
242
fluorescence data to a linear format. This was performed using the CALC4 program
(Robert Murphy, Carnegie Mellon). The relative linear fluorescence data presented
throughout this work were obtained by linearizing both the experimental
cell
population and the internal control population, prior to substrate addition and after
the sample obtained a steady state fluorescence distribution.
The reported
fluorescence values were obtained using the formula:
RFL=ARFL(Sampl e) -ARLF(PKH26Control )
(1)
where ARFL(sample) represents the difference in average fluorescence between the
steady state sample distribution and the unstained sample, obtained via CALC4, and
ARLF(PKH26 Control) represents the difference in average fluorescence between
the initial and final PKH26 control distributions, obtained via CALC4.
9.5 Theoretical Computations
Model simulations were performed using a fourth order Runge-Kutta
algorithm with automatic step size adjustment (SIMNON; Engineering Software
Concepts, Palo Alto CA).
243
Table 9.1 Bacillus subtilis Strains
strain
genotype
reference
JH642
SMY
SMY::pAF25
MB284
MB296
KI1179
KI1398
trpC2 phel
Prototrophy
amyEl:(veg-lacZ) cat
SPf::(spoVG-IacZ) cat
spoIIA+::pPP81(spoIIA-lacZ) cat
SPp::(spoVG-lacZ) cat
SPBf::(spoVG-lacZ)kinA::Tn917cat
SIK122
AG1350
SPpB::(spollG-lacZ) cat
SP::(spoIIG-lacZ)kinA::Tn917::neo
KI1183
KI1397
KI938
KI100o
amyE:(spoIID-lacZ) cat
amyE:(spollD-IacZ)kiA::Tn917 cat
spoIIA+::pPP81(spollA-lac cat
spoIIA+::pPP81(spoIIA-lacZ)kiA::Tn917::spc
spolIG-lacZspoOKAC
244
Perego et aL 1988
Mueller and Sonenshein 1992
Fouet and Sonenshein 1990
Mueller and Sonenshein 1992
Mueller and Sonenshein 1992
Zuber and Losick 1983
Zuber and Losick 1983
cat
Kenney et aL 1989
Kenney et aL 1989
Stragier et aL 1988
Stragier et aL 1988
Piggot and Curtis 1987
Piggot and Curtis 1987
Rudner et aL 1991
1;U
3
100
vs
c
o
60
0C
0
o 40
0
IL
20
n
0
2
4
6
8
10
Time (hr)
.
,~
1ZU
I
100
c
0
0o
0
C
60
0
0
3 40
IL 20
0
0.0
30000
60000
Number of Cells
Fgure 9.1 Linearity of the MUG assay with respect to time (A) and total cell number (B).
245
a,
a,
E
C
0
.......P.
:te 00
.0
7C
0rz
@1
0
0
0
EDi
00
0A
t00
-0
Fqure 92 Schematic illustration of the flow cytometer used in this work.
246
L
300--
200--
100--
0-
,i,,1i,,,1I,,,i,,,,i,,l1l1l,i,,,1I,,,,i,,i,111111l
30
60
90
120
150
180
210
240
270
300
Fgure 9.3 Sample output obtained from the MODFIT curve fitting program. The X-axis
scales with the green fluorescence signal and the Y-axis scales with the number of events.
The population exhibiting high fluorescence was determined to be 72% of the total
population.
247
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