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Journal of Bacteriology, October 2000, p. 5925-5930, Vol. 182, No. 20
0021-9193/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
On the Architecture of the Gram-Negative Bacterial Murein
Sacculus
David
Pink,1,*
Jeremy
Moeller,1
Bonnie
Quinn,1
Manfred
Jericho,2 and
Terry
Beveridge3
TPI, Physics Department, St. Francis Xavier
University, Antigonish, Nova Scotia, Canada B2G
2W5,1 Department of Microbiology,
College of Biological Science, University of Guelph, Guelph, Ontario,
Canada N1G 2W1,3 and Physics
Department, Dalhousie University, Halifax, Nova Scotia, Canada B3H
4J12
Received 27 March 2000/Accepted 18 July 2000
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ABSTRACT |
The peptidoglycan network of the murein sacculus must be porous so
that nutrients, waste products, and secreted proteins can pass through.
Using Escherichia coli and Pseudomonas
aeruginosa as a baseline for gram-negative sacculi, the
hole size distribution in the peptidoglycan network has been modeled by
computer simulation to deduce the network's properties. By
requiring that the distribution of glycan chain lengths predicted by
the model be in accord with the distribution observed, we conclude that
the holes are slits running essentially perpendicular to the local
axis of the glycan chains (i.e., the slits run along the long axis of
the cell). This result is in accord with previous permeability
measurements of Beveridge and Jack and Demchik and Koch. We outline
possible advantages that might accrue to the bacterium via this
architecture and suggest ways in which such defect structures might be
detected. Certainly, large molecules do penetrate the peptidoglycan
layer of gram-negative bacteria, and the small slits that we suggest might be made larger by the bacterium.
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TEXT |
Bearing in mind that a thorough
knowledge of structure can lead to an understanding of function, it is
important to elucidate the detailed molecular structures of bacterial
surfaces. There have been few recent studies of the physical properties
of murein sacculi (12, 18, 20), and yet this cell wall
component is of paramount importance to the integrity of the cell. It
must have high tensile strength and at the same time be
highly permeable to nutrients, waste products, and secreted
proteins. Even large macromolecules such as DNA (in genetic
exchange, such as transformation) and S-layer proteins can pass
through (1, 17). The murein of a gram-negative bacterium is
composed of one to three layers of peptidoglycan (12, 19)
and is bonded via lipoprotein moieties to the outer membrane; these can
be either covalently or electrostatically attached to the peptidoglycan
(1). Our recent work on the elasticity and thickness of
Escherichia coli and Pseudomonas aeruginosa
murein sacculi has helped define several physical properties of this intriguing cell wall fabric (20), but the work did not
elucidate the network's porosity. Since 35 to 50% of the
peptide stems of peptidoglycan are cross-linked at a given
time in the cell's growth cycle (5, 7), this bonding
between neighboring glycan strands should be important not only for the
fabric's ability to withstand physical strain but also for its
porosity. Indeed, it could even be possible for this limited
cross-linking in combination with the finite length of each glycan
strand to form peptidoglycan arrangements such that holes of various
kinds occur within the network. Because of the dynamic nature of cell
growth and peptidoglycan turnover, these holes could be transient and
difficult to find, especially since they would be at nanoscale
dimensions. Yet, the question of holes in these networks is of
fundamental importance, and here we resort to computer modeling
to predict their existence and distribution. A knowledge of the shape
and size distributions of such holes, as well as the variances in these
distributions, can be relevant for understanding the passage of
molecules through the peptidoglycan layer, the elasticity of the layer,
and, possibly, aspects of the mechanics of cell division. In this note,
we show the expected distribution of holes deduced from the
distribution of glycan strand lengths (5, 7, 9) and discuss
their possible consequences for the viability of the bacterium.
Peptidoglycan chemical structure and initial discussion of holes in
a monomolecular thick peptidoglycan network.
There can be two
types of cross-linkages occurring between adjacent glycan strands in a
peptidoglycan network: cross-links that occur somewhere in the middle
region of a glycan strand and those that occur at the terminus of a
strand. Since each peptidoglycan strand possesses a screw axis, the
peptide stems are helically arranged so that they protrude at ~90°
to one another (7, 11). Accordingly, only a maximum of 50%
of the stems can be cross-linked in a horizontal plane. At that point,
the network exhibits maximum cross-linking. Frequently, each
cross-link involves a covalent bond between a 3-amino-acid stem
and a 4-amino-acid stem
(L-Gla-D-Glu-m-A2pm-D-Ala---m-A2pm-D-Glu-L-Ala) or between two 4-amino-acid stems
(L-Ala-D-Glu-m-A2pm-D-Ala---m-A2pm [D-Ala]-D-Glu-L-Ala), each
emanating from adjacent N-acetylmuramyl moieties on
neighboring glycan strands (i.e., a septapeptide or an octapeptide
cross-link). Whenever a cross-link involves a 4-amino-acid stem
(L-Ala-D-Glu-m-A2pm-D-Ala)
and a 5-amino-acid stem
(L-Ala-D-Glu-m-A2pm-D-Ala-D-Ala; i.e., the terminal D-Ala has not been cleaved by the
D,D-carboxypeptidase), the linkage is referred
to as a nonapeptide cross-link. The parts of the peptide stems
that proceed beyond each cross-link are not stressed by the
bacterium's turgor pressure. If all glycan strands were much longer
than the typical septapeptide, octapeptide, or nonapeptide cross-links
and if the maximum possible number of these cross-links had been
produced in a peptidoglycan network, i.e., if 50% of the peptide
stems are cross-linked, then all the holes in the network would be
essentially the same size. This size is the area bounded by two
successive cross-links and the two intervening sections of glycan
strands, as shown in Fig. 1A. Figure 1B
shows a representation of a network which is maximally cross-linked,
thus possessing the smallest possible holes. We have not shown the
possibility that these smallest holes become approximately hexagonal
when the network is stretched (10)
we are not immediately
concerned with what shape the holes adopt in a bacterium, since it is
possible that the cell might exert local forces, thereby additionally
distorting its peptidoglycan network (following Koch, we call
these smallest holes tesserae [i.e., units having four corners]).
In reality, however, the glycan strands are not very much longer
than the cross-links but range from 2 to over 30 disaccharides long
(5, 7, 9, 14), so it is possible that one of the intervening
sections of a glycan strand bounding a tessera appears broken. Instead
of one continuous glycan strand, the network at this point is composed
of the ends of two different glycan strands. In this case, two
adjacent tesserae are actually connected, thereby
creating an aggregate (of tesserae) formed by two connected tesserae
(Fig. 1C shows four such connected tesserae). An assumption will
be made that there are minimal gaps between the ends of two abutting
glycan strands, as shown in Fig. 1C. This assumption is justified by
the observation that, in E. coli and P. aeruginosa, 50% of the pentapeptide strands form
cross-links. If gaps existed between the ends of abutting
glycan chains, then some unlinked peptapeptide chains lying in
the plane of the network would lack another pentapeptide partner from
an adjacent strand with which to form a cross-link. Finally, Fig. 1D
and E show how the networks of Fig. 1B and C might behave at higher
temperatures, where high-energy thermal oscillations give rise to
distortions of the network. The network containing the
aggregate (Fig. 1C) undergoes even larger distortions than that
containing only tesserae because of the proximity of many glycan strand
ends, which lead to the opening of a large hole in the lattice. It will
be shown below that, in a monomolecular thick peptidoglycan
network, it is possible to deduce the general form of the distribution
of the holes, their general shape, and their alignment to one
another from a knowledge of the distribution of glycan strand lengths.

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FIG. 1.
(A) Diagram of a portion of a murein sacculus. The
hexagons represent the N-acetylglucosamine and the
N-acetylmuramic acid (Mur) groups. Two complete cross-links
are shown attached to the latter. One is a nonapeptide link (left),
while the other is an octapeptide link (right). Circles containing or × indicate unlinked pentapeptide groups, attached to Mur
groups and oriented out of or into the plane. (B and D) Diagram of
infinitely long glycan strands (horizontal) maximally cross-linked
via nonapeptide or octapeptide chains. A tessera is shown shaded. The
dots represent the sugar groups along the glycan strands. (D) Effect of
temperature-driven fluctuations in this elastic system. (C and E) The
peptidoglycan network of A with six glycan strand ends shown
(arrows) giving rise to an aggregate of four tesserae
(shaded). The hexagonal structure is not represented here. (E)
Effect of temperature-driven fluctuations in this elastic system.
The arrow points to a small, attached but isolated peptidoglycan
remnant that is relatively free to move. Note the possibility of
opening a large hole in the sacculus.
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Mathematical models and techniques.
To explore the size
distribution of holes in a peptidoglycan network formed from a set of
glycan strands exhibiting a distribution of lengths, we considered
trying to assemble the glycan strands in some manner, possibly random.
This proved difficult, because it became apparent that when choosing a
particular strand and a place to insert it, we had to be aware of the
existing structure of the local network; otherwise, the fabric could be
left with large gaps between the ends of successive strands. The use of Monte Carlo techniques to anneal a structure containing such gaps could
be very time-consuming and would not necessarily solve the problem.
This procedure of inserting glycan strands into a preexisting peptidoglycan network is performed by bacteria, as they (somehow) determine exactly where each piece should go, but we do not know the
rules they are following. Accordingly, we solved this problem by
cutting otherwise very long glycan strands so that the procedure resulted in the experimentally observed glycan strand length
distribution. Cutting the strands of a network is a standard technique
in the simulation of percolation and has been applied in a biological context (references 15 and 16 and
references therein). That application, however, was without any
analogue of the requirement here, i.e., that the resulting glycan chain
length distribution must be in accord with experimental results. In
that application, also, all bonds could be cut, leading to the
possibility that sections of the network could be separated from the
main body (15) (Fig. 1). Our model does not permit this, and
we comment on this below.
We began with a flat plane, with periodic boundary conditions,
composed of glycan strands possessing no breaks, and we
assumed
maximum cross-linking (see above). The glycan strands were laid
down parallel to each other with the maximum number of pentapeptide
chains in register. This allowed the maximum number of cross-links,
formed from pairs of nearest-neighbor pentapeptides, to be created,
yielding a peptidoglycan network that contained only tesserae
(Fig.
2A). We then introduced the concept of an
"aggregate of
size
s," defined as a set of
s
tesserae which are connected by
having the ends of glycan strands in
common. Each aggregate of
size
s was created by a rule, B,
which defined a sequence of breaks
in the glycan strands that permitted
s tesserae to be connected.
Such an aggregate can be created
in a variety of ways, but with
one constraint: the breaks in the glycan
strands may not isolate
a portion of the peptidoglycan network so that
it becomes disconnected
from the remainder of the network. There is no
experimental reason
for this constraint except that, should a section
of the peptidoglycan
network become disconnected from the remainder of
the network,
the structure would be weakened. There appears to be no
obvious
advantage for a bacterium in adopting such a structure. In
order
to actually construct a set of aggregates, it is clearly
necessary
that a size distribution,
Ds(
s), which yields the fraction of
aggregates composed of
s tesserae be specified.

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FIG. 2.
(A) Diagram of infinitely long glycan strands
(horizontal) maximally cross-linked via nonapeptide or octapeptide
chains. A tessera is shown shaded. (B) The peptidoglycan system of
panel A. The locations where the glycan strands may be cut are
indicated by ×s. (C) Construction of an aggregate according to rule
B1. The glycan chain has been cut in two places (dark arrows), and the
three tesserae thus connected are shaded. (D) Illustration of rule B2.
With the glycan chain cut at the dark arrow, the two ×s on either side
of the cut (light arrows) have been removed to show that those sites
may not be cut. The solid circles represent sugar groups along the
glycan strands.
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This set of aggregates, represented by the distribution
Ds(
s), was distributed, according to
a rule H, on the plane of the
peptidoglycan network. Bearing
in mind that aggregates introduce
breaks in the glycan chains, the
network was now composed of a
distribution of finite-length glycan
chains. The length distribution,
DL(
L), yielding the fraction of
glycan strands of length
L, could
then be calculated and
compared to the results of the experiment.
In what follows, we present
the rationale for different choices
of
Ds(
s) and rule H and the
results.
Creation of holes.
The following procedure was used to create
a set of aggregates possessing a size distribution
Ds(s). First, we selected the total
number of aggregates we wanted produced and then selected a size,
s, in accord with the probability,
Ds(s), that such a size would occur
in the distribution. An aggregate of size s was then created
as follows. We assumed, for simplicity, that the two glycan strands
bordering a tessera could be cut at four sites, and their locations are
identified in Fig. 2B. We selected one of these sites randomly but in
accord with rules B1 and B2 (see below) and, by cutting there, attached
a second tessera to the first tessera. The two-tessera aggregate thus
created possessed five sites at which it was possible to cut. We then
randomly chose one of the two tessera as well as a site on one of its
glycan strands at which to cut. By cutting at that site, a third
tessera was attached to the growing aggregate. This procedure is shown in Fig. 2C, where the two cuts that create the three-tessera
aggregate are indicated. The procedure terminated when sufficient
cuts had been performed to create an s-tessera
aggregate. The resulting structure was then placed anywhere convenient
on the maximally cross-linked peptidoglycan network.
(i) Rules B1 and B2.
Rules B1 and B2 dealt with the procedures
used to select sites on the glycan chains at which to cut. We began by
imposing no conditions on which sites (Fig. 2B) we could cut at (rule
B1) but with the condition that, as the aggregates were constructed, they were not cut in such a way as to cause a section of the network to
become separated from the remainder of the network. For reasons to be
described below, we also introduced rule B2. This states that, if a cut
was made at a site, then cuts could not be made at the sites nearest to
it along the same glycan strand and belonging to the same tessera.
This is shown in Fig. 2D, where the cut is indicated and the two sites
where cuts were not permitted in any future operation are also shown.
The set of aggregates of various shapes, exhibiting a size
distribution
Ds(
s), now distributed
on the peptidoglycan network,
resulted in a distribution of glycan
strand lengths
DL(
L). We
then moved
the aggregates around, according to rule H (see below),
until
DL(
L) had achieved a steady-state
distribution.
(ii) Rule H.
There are many ways to mimic what a bacterium
might be doing without suggesting that this is the method that it
actually uses. In the mathematical model used here, we moved the
aggregates on the plane of the network subject to an interaction,
V, which determined their average spatial distribution. We
are unaware of how a bacterium actually controls the distribution of
glycan strand ends. Our intent was simply to discover whether it does
so, and we achieved this by introducing the effective interaction,
V, which was parameterized by an interaction strength,
V0, between pairs of aggregates. If V0 was positive (negative), then the interaction
between pairs of aggregates was repulsive (attractive). Choosing a
V0 equal to 0 would result in a random
distribution, as long as the density of aggregates was not too high
(i.e., as long as the aggregates could find sufficient room between the
other aggregates to move around). It should be clear that the
aggregates do not rotate
they undergo only lateral movement. Since the
bacterium probably does not create glycan chain lengths by using our
mathematical procedure, our interaction, V, might not be
easily identified with parameters of the procedure actually used by the
bacterium. The analytical form used for V is given in the
Appendix.
We used the following two choices for the functional form of
Ds(
s), the distribution aggregate
sizes:
Ds(
s) =
Aexp(

[
s0
s]
2/
2) (1a)
Ds(
s) = 1 if
s =
s0 (1b) = 0 if
s
s0
where
A is an amplitude chosen so that the sum
over
s is equal to unity. In equation 1a, the bacterium
would have distributed
glycan strand lengths leading to a gaussian
(normal) distribution
of aggregate sizes, with an average size
(aggregates composed
of
s0 tesserae each) and a
range of sizes (variance determined
by
2). The
second distribution, equation 1b, would arise if the bacterium
had
distributed glycan strand ends which resulted in all aggregates
being
the same size, each containing
s0 tesserae.
These two possibilities
cover two extremes from which we might learn
about the actual
distribution in gram-negative
bacteria.
In order to move the aggregates on the network, we made use of Monte
Carlo techniques using the energy function (see equation
in the
Appendix). We used the Metropolis algorithm (
4) with
the
magnitudes of
V0 and distance chosen so that
aggregate moves were
achieved for at least 50% of the attempts,
with equilibrium achieved
within a few thousand steps. We chose
V0 to be negative so as
to keep the aggregates
apart. The networks used initially contained
1,600
tesserae.
Results.
Figure 3A shows glycan
strand length distributions with 25% of the network covered by
aggregates and utilizing rule B1 and a gaussian distribution with
s0 equal to 4 and
equal to 2. The graph
shows the distribution of glycan strand lengths,
DL(L), for V0s
of 0, 2, and 5, with an insert showing the distribution of aggregates
for a V0 of 5. These values were chosen with the intention
of obtaining a glycan strand length distribution similar to that
obtained by Obermann and Höltje (14). However,
although DL(L) exhibits a local
maximum at values of L corresponding to those observed by
those authors when V0 is equal to 5, there is a
maximum at the shortest length, an L of 2. The maximum at
the shortest glycan chain lengths is absent in the results of Obermann and Höltje. The reason for this was easily discovered. There are
two ways of creating glycan chain lengths in our system. The first is
controlled by the distribution of aggregates on the network, and this
gives rise to a local maximum at the larger value of L.
However, the large numbers of glycan strands with an L of 2 arise inside aggregates. They are created when cuts are made at adjacent sites, along the same glycan strand, on either side of a
cross-link (Fig. 1C and E and 2C). Although we have no proof that we
will necessarily obtain a maximum in
DL(L) for an L of 2, we
have been unable to find a counterexample that yields a maximum in
accord with the results of Obermann and Höltje without another
maximum at an L of 2. Obermann and Höltje
(14) report that the number of strands of length
(L) 2 is near zero. Because of this, we introduced rule B2
(see above), which states that if a cut was made at a site, then cuts
could not be made at the sites nearest to it along the same glycan
strand and belonging to the same unit (Fig. 2D).

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FIG. 3.
DL(L), the fraction of
glycan strands possessing length L, as a function of glycan
strand length L, for different values of repulsive potential
V0. The dashed lines indicate a
V0 of 0 (short dashes) and a
V0 of 2. The solid lines indicate a
V0 of 5. At an L of 61, the curve
indicates the sum of all values of
DL(L) for an L of >60.
The insets show typical distributions of aggregates for a
V0 of 5. (A) Use of rule B1.
Ds(s) is gaussian, with
s0 equal to 4 and equal to 2. (B to D) Use
of rule B2 to eliminate short glycan strands.
Ds(s) = 1 for
s = s0 and
Ds(s) = 0 for s s0; otherwise all aggregates possess the same
number of tesserae. (B) s0 = 2; (C)
s0 = 3; (D) s0 = 5.
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Figure
3B to D shows results, using rule B2, for
DL(
L) with
V0s
of 0, 2, and 5 obtained for the distribution of equation 1b
with
s0s of 2, 3, and 5. The inserts show the
distribution of
the aggregates for a
V0 of 5. We
now see that there is only one
maximum in the chain length distribution
and that there are no
chains where
L is equal to 2. The
consequence of this is that
the aggregates are now all slits running
essentially perpendicular
to the axis of the glycan strands. Although
we cannot identify
a distribution of aggregate sizes, it seems clear
from our results
that the slits are kept apart by the bacterium, as
suggested by
the experimentally observed form of
DL(
L). This is achieved in
our model
by means of an effective aggregate-aggregate repulsive
interaction (see
the
Appendix). The technique by which the bacterium
might achieve this
is not yet clear. Comparing the experimental
results with the
L dependence of
DL(
L)
obtained by us suggests
that
s0 is >5.
Conclusions.
We have modeled the distribution of holes in the
murein sacculus (peptidoglycan network) of gram-negative bacteria as
represented by E. coli and P. aeruginosa. By
requiring that the length distribution of glycan strands be in accord
with the results of others (5, 7, 9, 14), we conclude that
all the holes are slits running essentially perpendicular to the axis
of the glycan strands (Fig. 2D and 3B, C, and D). If the slits are not
too large, then our results are in accord with what has been observed
by Beveridge and Jack (2) and Demchik and Koch
(6) regarding the permeability of bacterial walls. In
addition, there appear to be some possible advantages to a distribution
of slits as described here.
Demchik and Koch (
6) measured the permeability of the wall
fabric of
E. coli and
B. subtilis and concluded
that the effective
hole radius in these walls is slightly more than 2 nm. They pointed
out that this is the characteristic dimension of a
single tessera
in the peptidoglycan network. If the slits are not too
large,
then our results are in accord with this, as illustrated in Fig.
4. There we have shown a network under
tension, with an aggregate
composed of three tesserae. It is clear that
if the network is
allowed to relax (not shown in Fig.
4B), then the
characteristic
dimensions of the slit are a length of ~6 nm and a
width of ~2
nm. The limiting dimension here is the smaller one, so
the passage
of the molecules used by Demchik and Koch would be
inhibited by
slits as described here. This conclusion would not hold if
aggregates
of more general shapes were formed, as in Fig.
1E and
3A.
There
it can be seen that the interiors of larger aggregates, composed
of segments similar to that in Fig.
1E, would have no means of
inhibiting the entry of a molecule with a radius of >2 nm.

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FIG. 4.
(A) Turgor pressure-stretched peptidoglycan network such
that the holes formed by the tesserae become hexagons. The cross-links
at two peptide stem junctions are being cut at the regions marked by
×s to form a slit aggregate comprising three tesserae. (B) The network
of A with the slit shown. It can be seen that although the slit
is ~6 nm long, the lateral dimension will remain ~2 nm.
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Consider a cylinder fabricated out of glycan strands, which adopt the
zigzag conformation in an average direction perpendicular
to the long
axis of the cylinder, viz., the cell axis, with the
octa- and
nonapeptide cross-links oriented along the axis of the
cylinder. In
contrast to the many conformational states accessible
by the
cross-links, the glycan strands possess conformational
freedom only via
rotation around the oxygen linkages. Straightening
the glycan strands
would necessitate carrying out rotations of
the sugar residues around
those bonds. Certainly, gram-negative
rods do not have much expansion
around their girths (
13), but
the existence of slits of the
kind described here distributed
among the peptidoglycan network would
allow more
elasticity.
All peptidoglycan networks must be porous; otherwise, the free
diffusion of nutrients and waste products could not occur.
Furthermore,
large secreted substances (e.g., S-layer proteins
are typically 50 to
120 kDa [
17]) are known to pass through.
Of course, it
is possible that large molecules are (somehow) linearized
so that they
can pass, but many periplasmic enzymes (previous
to export) are
functional, which implies they have correctly folded
and are no longer
completely linear. Slits (arising from rule
B2) increase hole size, and
it is possible that slits in the peptidoglycan
alter the network's
inherent porosity so larger-size molecules
can pass. It is likely also
that the opening and closing of a
slit would be easier for the
bacterium to control against random
fluctuations than some other kind
of aggregate structure. An example
of the latter is shown in Fig.
1E,
where a substantial opening
can be created simply by moving the short
isolated length of glycan
strand (Fig.
1E). These openings could
oscillate like flaps, opening
and closing due to thermal fluctuations
in the periplasm. It may
be possible too that slits could be an easily
recognizable physical
marker for autolysins, so that these enzymes
could machine larger
transient holes for larger
molecules.
It has recently been proposed that the sacculus expands (and the cell
grows) by excising one old peptidoglycan strand and
adding three new
strands (Höltje's three-for-one hypothesis [
7,
8]). This is an attractive idea, but there is a difficulty
in
that a bacterium must (somehow) be aware of the lengths of
the
preexisting strands at the location of the insertion. If,
however, the
ends of the strands at insertion locations are correlated
because they
form the side of a slit, this ready-made site might
be
preferred.
We have chosen not to calculate quantities such as the elasticity of
the system, since there are a number of unknowns, including
the spring
energy of the septapeptide, octapeptide, or nonapeptide
cross-links,
the permissible range of the angles

and

shown
in Fig.
4A, and
the bending energy of the glycan strands. These
questions have been
addressed by Boulbitch (private communication),
who has carried out a
calculation of the elasticity of a peptidoglycan
network without holes
and other defects. This paper is concerned
with deducing the hole
structure that exists in gram-negative
murein sacculi, subject to the
constraint of the glycan strand
length distribution. As described
above, the presence of such
slits could be advantageous for the
bacterium, and this makes
it an attractive model. At the present time
we do not know of
any physical or chemical method to validate our
model. All attempts
to visually detect specifically labeled slits by
transmission
electron microscopy (TEM) and metal decoration have failed
because
of moire effects as a result of superpositioned layers. Neutron
or X-ray sources cannot be used to detect the slits as defects
in a
continuous network, since it is not yet possible to stack
and orient
sacculi with enough of the networks in register for
suitable
diffraction or reflection. It may be possible to supersede
the moire
problems inherent in TEM by using the enhanced imaging
qualities of
TEM-electron spectroscopic imaging (
3), and we
are currently
exploring this
possibility.
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APPENDIX |
Consider two aggregates, which we call a1
and a2, with the set of distances
{rij} being those between the individual
tesserae which make up the two aggregates and with i and
j representing tesserae on a1 and
a2, respectively. Then the two aggregates
interact with each other via the energy
V(a1, a2,
{rij}). We chose the simplest mathematical
form possible that yielded spatial distributions of aggregates which
were easily distinguished from each other. A
"one-over-rij4"
interaction was sufficiently "hard" to achieve this while being "soft" enough to permit the aggregates to move around and relax their distribution on the network. We chose
1/
r 4
ij
If V0 is positive (negative), then the
interaction is repulsive (attractive). Choosing a
V0 equal to 0 will result in a random distribution, as long as the density of aggregates is not too high
(i.e., as long as the aggregates can find sufficient room between the
other aggregates to move around and relax into an equilibrium
distribution). It should be clear that the aggregates do not
rotate
they undergo only lateral movement.
 |
ACKNOWLEDGMENTS |
This work was supported by Natural Sciences and Engineering
Research Council of Canada (NSERC) research grants to D.P., T.B., and
M.J. and by a St. Francis Xavier University Council for Research grant
to D.P. J.M. was supported through an NSERC Summer Research Scholarship. We also appreciate the help of the Canadian Institute for
Advanced Research (CIAR) for support through grants for travel and workshops.
We appreciate the comments of the referees.
 |
FOOTNOTES |
*
Corresponding author. Mailing address: TP1, Physics
Department, St. Francis Xavier University, P.O. Box 5000, Antigonish, Nova Scotia, Canada B2G 2W5. Phone: (902) 837-3987. Fax: (902) 867-2414. E-mail: dpink{at}stfx.ca.
 |
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Journal of Bacteriology, October 2000, p. 5925-5930, Vol. 182, No. 20
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