Signature Assignment Grading Guide QNT/561 Version 9 2 Signature Assignment Grading Guide QNT/561 Version 9 Applied Business Research and Statistics Copyright Copyright © 2017, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008 by University of Phoenix. All rights reserved. University of Phoenix® is a registered trademark of Apollo Group, Inc. in the United States and/or other countries. Microsoft®, Windows®, and Windows NT® are registered trademarks of Microsoft Corporation in the United States and/or other countries. All other company and product names are trademarks or registered trademarks of their respective companies. Use of these marks is not intended to imply endorsement, sponsorship, or affiliation. Edited in accordance with University of Phoenix® editorial standards and practices.Individual Assignment: Signature Assignment Purpose of Assignment The purpose of this assignment is for students to synthesize the concepts learned throughout the course. Provide students an opportunity to build critical thinking skills, develop businesses and organizations, and solve problems that require data. Resources Required Microsoft Excel® Signature Assignment Databases Signature Assignment Options Part 3: Inferential Statistics Grading Guide Content Met Partially Met Not Met Comments: Scenario: Upon successful completion of the MBA program, say you work in the analytics department for a consulting company. Your assignment is to analyze ONE of the following databases: · Manufacturing · Hospital · Consumer Food · Financial Select one of the databases based on the information in the Signature Assignment Options. Provide a 1,600-word detailed, statistical report including the following: · Explain the context of the case · Provide a research foundation for the topic · Present graphs · Explain outliers · Prepare calculations · Conduct hypotheses tests · Discuss inferences you have made from the results This assignment is broken down into four parts: · Part 1 - Preliminary Analysis · Part 2 - Examination of Descriptive Statistics · Part 3 - Examination of Inferential Statistics · Part 4 - Conclusion/Recommendations Part 1 – Preliminary Analysis (3 – 4 paragraphs) Generally, as a statistics consultant, you will be given a problem and data. At times, you may have to gather additional data. For this assignment, assume all the data is already gathered for you. · State the objective. · What are the questions you are trying to address? · Clearly and in sufficient detail, describe the population in the study. · What is the sample? · Discuss the types of data and variables. Are the data quantitative or qualitative? · What are levels of measurement for the data? Part 2 – Descriptive Statistics (3 – 4 paragraphs) · Examine the given data. · Present the descriptive statistics (mean, median, mode, range, standard deviation, variance, CV, and five-number summary). · Identify any outliers in t.

1. Signature Assignment
Grading Guide
QNT/561 Version 9 2
Signature Assignment Grading Guide
QNT/561 Version 9
Applied Business Research and Statistics

2. Copyright
Copyright © 2017, 2015, 2014, 2013, 2012, 2011, 2010, 2009,
2008 by University of Phoenix. All rights reserved.
University of Phoenix® is a registered trademark of Apollo
Group, Inc. in the United States and/or other countries.
Microsoft®, Windows®, and Windows NT® are registered
trademarks of Microsoft Corporation in the United States and/or
other countries. All other company and product names are
trademarks or registered trademarks of their respective
companies. Use of these marks is not intended to imply
endorsement, sponsorship, or affiliation.
Edited in accordance with University of Phoenix® editorial
standards and practices.Individual Assignment: Signature
Assignment
Purpose of Assignment
The purpose of this assignment is for students to synthesize the
concepts learned throughout the course. Provide students an
opportunity to build critical thinking skills, develop businesses
and organizations, and solve problems that require data.

3. Resources Required
Microsoft Excel®
Signature Assignment Databases
Signature Assignment Options
Part 3: Inferential Statistics
Grading Guide
Content
Met
Partially Met
Not Met
Comments:
Scenario: Upon successful completion of the MBA program, say
you work in the analytics department for a consulting company.
Your assignment is to analyze ONE of the following databases:
· Manufacturing
· Hospital
· Consumer Food
· Financial
Select one of the databases based on the information in the
Signature Assignment Options.
Provide a 1,600-word detailed, statistical report including the
following:
· Explain the context of the case
· Provide a research foundation for the topic
· Present graphs
· Explain outliers

4. · Prepare calculations
· Conduct hypotheses tests
· Discuss inferences you have made from the results
This assignment is broken down into four parts:
· Part 1 - Preliminary Analysis
· Part 2 - Examination of Descriptive Statistics
· Part 3 - Examination of Inferential Statistics
· Part 4 - Conclusion/Recommendations
Part 1 – Preliminary Analysis (3 – 4 paragraphs)
Generally, as a statistics consultant, you will be given a
problem and data. At times, you may have to gather additional
data. For this assignment, assume all the data is already
gathered for you.
· State the objective.
· What are the questions you are trying to address?
· Clearly and in sufficient detail, describe the population in the
study.
· What is the sample?
· Discuss the types of data and variables. Are the data
quantitative or qualitative?
· What are levels of measurement for the data?
Part 2 – Descriptive Statistics (3 – 4 paragraphs)
· Examine the given data.
· Present the descriptive statistics (mean, median, mode, range,
standard deviation, variance, CV, and five-number summary).
· Identify any outliers in the data.
· Present any graphs or charts you think are appropriate for the
data.
Note: Ideally, we want to assess the conditions of normality too.
However, for the purpose of this exercise, assume data is drawn
from normal populations.

5. Part 3 – Inferential Statistics (2 – 3 paragraphs)
Use the Part 3: Inferential Statistics document.
· Create (formulate) hypotheses
· Run formal hypothesis tests
· Make decisions. Your decisions should be stated in non-
technical terms.
Hint: A final conclusion saying “reject the null hypothesis” by
itself without explanation is basically worthless to those who
hired you. Similarly, stating the conclusion is false or rejected
is not sufficient.
Part 4 – Conclusion and Recommendations (1 – 2 paragraphs)
· What are your conclusions?
· What do you infer from the statistical analysis?
· State the interpretations in non-technical terms. What
information might lead to a different conclusion?
· Are there any variables missing?
· What additional information would be valuable to help draw a
more certain conclusion?
Total Available
Total Earned
13
#/13

6. Writing Guidelines
Met
Partially Met
Not Met
Comments:
The paper—including tables and graphs, headings, title page,
and reference page—is consistent with APA formatting
guidelines and meets course-level requirements.
Intellectual property is recognized with in-text citations and a
reference page.
Paragraph and sentence transitions are present, logical, and
maintain the flow throughout the paper.
Sentences are complete, clear, and concise.
Rules of grammar and usage are followed including spelling and
punctuation.

7. Total Available
Total Earned
3
#/3
Assignment Total
#
16
#/16
Additional comments:
Wall Teichoic Acid Function, Biosynthesis, and Inhibition
Jonathan G. Swoboda, Jennifer Campbell, Timothy C. Meredith,
and Suzanne Walker

8. Department of Microbiology and Molecular Genetics Harvard
Medical School, 200 Longwood
Avenue Armenise 633, Boston, MA 02115 (USA) Fax: (+ 1)
617-738-7664
Keywords
antibiotics; biosynthesis; conditionally essential enzymes;
Gram-positive bacteria; wall teichoic acid
(WTA)
Introduction
One of the major differences between Gram-negative and Gram-
positive organisms is the
presence or absence of an outer membrane (Figure 1). In Gram-
negative organisms, the outer
membrane protects the organism from the environment. It filters
out toxic molecules and
establishes a compartment, the periplasm, which retains
extracytoplasmic enzymes required
for cell-wall growth and degradation. It also serves as a scaffold
to which proteins and
polysaccharides that mediate interactions between the organism
and its environment are
anchored.[1] In addition, in ways that are not completely
understood, the outer membrane
functions along with a thin layer of peptidoglycan to help
stabilize the inner membrane so that
it can withstand the high osmotic pressures within the cell.[2]
Gram-positive organisms, in contrast, lack an outer membrane
and a distinct periplasm (Figure
1). The peptidoglycan layers are consequently very thick
compared to those in Gram-negative
organisms.[4] These thick layers of peptidoglycan stabilize the
cell membrane and also provide
many sites to which other molecules can be attached. Gram-

9. positive peptidoglycan is heavily
modified with carbohydrate-based anionic polymers that play an
important role in membrane
integrity.[5] These anionic polymers appear to perform some of
the same functions as the outer
membrane: they influence membrane permeability, mediate
extracellular interactions, provide
additional stability to the plasma membrane, and, along with
peptidoglycan, act as scaffolds
for extracytoplasmic enzymes required for cell-wall growth and
degradation.
A major class of these cell surface glycopolymers are the
teichoic acids (TAs), which are
phosphate-rich molecules found in a wide range of Gram-
positive bacteria, pathogens and
nonpathogens alike. There are two types of TAs: the lipo-TAs
(LTAs), which are anchored to
the plasma membrane and extend from the cell surface into the
peptidoglycan layer;[6] and the
wall TAs (WTAs), which are covalently attached to
peptidoglycan and extend through and
beyond the cell wall (Figure 1).[7] Together, LTAs and WTAs
create what has been aptly
described as a “continuum of negative charge” that extends
from the bacterial cell surface
beyond the outermost layers of peptidoglycan.[5] Neuhaus and
Baddiley comprehensively
reviewed both LTAs and WTAs in 2003.[5] Since then,
however, new functions for WTAs in
pathogenesis have been uncovered and it has been suggested
that the biosynthetic enzymes
that make these polymers are targets for novel antibacterial
agents.[8,9] Indeed, the first WTA-
© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

10. [email protected]
NIH Public Access
Author Manuscript
Chembiochem. Author manuscript; available in PMC 2010
January 4.
Published in final edited form as:
Chembiochem. 2010 January 4; 11(1): 35–45.
doi:10.1002/cbic.200900557.
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active antibiotic has just been reported.[10] This review will
focus primarily on recent
developments in the study of WTAs in Bacillus subtilis and
Staphylococcus aureus, and will
include a discussion of strategies for the discovery of WTA
inhibitors and prospects for these
inhibitors as antibiotics.
Wall Teichoic Acid Structure
WTAs are anionic glycopolymers that are covalently attached to
peptidoglycan via a
phosphodiester linkage to the C6 hydroxyl of the N-acetyl
muramic acid sugars.[5] They can
account for as much as 60 % of the total cell wall mass in
Gram-positive organisms. The
chemical structures of WTAs vary among organisms, as
described in detail by Neuhaus and
Baddiley,[5] but the most common structures are composed of a
ManNAc(β1→4)GlcNAc
disaccharide with one to three glycerol phosphates attached to
the C4 hydroxyl of the ManNAc
residue (the “linkage unit”) followed by a much longer chain of
glycerol- or ribitol phosphate
repeats (the “main chain”; Figure 2).[11–18] B. subtilis, the
Gram-positive model organism,
makes poly(glycerol phosphate) or poly(ribitol phosphate)
WTAs depending on the strain,
[19] while S. aureus strains primarily make poly(ribitol

12. phosphate) WTAs.[20–23] The
hydroxyls on the glycerol- or ribitol phosphate repeats are
tailored with cationic D-alanine esters
and monosaccharides, such as glucose or N-
acetylglucosamine.[24,25] The presence of WTAs
and the particular tailoring modifications that are found on them
have profound effects on the
physiology of Gram-positive organisms, and impact everything
from cation homeostasis to
antibiotic susceptibility to survival in a host.
Functions of Teichoic Acids in Bacterial Physiology
The functions of TAs in bacterial physiology are incompletely
understood, but evidence for
their importance is overwhelming. B. subtilis and S. aureus
mutants deficient in LTA
biosynthesis can be obtained but only if grown under a narrow
range of conditions; they are
temperature sensitive and exhibit severe growth defects.[26,27]
Mutants deficient in WTA
biosynthesis are also compromised and manifest increased
sensitivity to temperature and
certain buffer components, including citrate; they also tend to
aggregate in culture.[26–31] In
addition, B. subtilis strains that do not express WTAs show
profound morphological
aberrations. Bacterial strains in which both LTA and WTA
expression are prevented are not
viable, an observation suggesting that these polymers have
overlapping functions and can
partially compensate for one another.[26,27] Indeed, this might
be expected for some functions
since both polymers contain phosphate-linked repeat units with
similar tailoring modifications.
One of the tailoring modifications, D-alanylation, is
accomplished by the same machinery, so

13. there is even some overlap in the biosynthetic pathways. This
fact makes dissecting the
functions of the individual anionic glycopolymers difficult, but
is consistent with the idea that
LTAs and WTAs are partially redundant. Some of the functions
attributed to WTAs are
described in the following paragraphs. LTAs are beyond the
scope of this review, but will be
mentioned in cases where it is relevant to the discussion of
WTAs. Morath et al. and Rahman
et al. have each written recent reviews on LTA structure and
biosynthesis.[6,32]
Cation binding functions
WTAs form a dense network of negative charges on Gram-
positive cell surfaces. To alleviate
the resulting electrostatic repulsive interactions between
neighboring phosphates, TAs bind
cationic groups, including mono- and divalent metal cations.
Networks of WTA-coordinated
cations affect the overall structure of the polymers, and this in
turn influences the porosity and
rigidity of the cell envelope. WTAs are proposed to be
important for cation homeostasis in
Gram-positive organisms,[33,34] and provide a reservoir of ions
close to the cell surface that
might be required for enzyme activity. In addition, the gradient
of ions could in some way
mitigate the osmotic pressure change between the inside and
outside of the cell. The amount
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of bound cations can be modulated by D-alanylation, a tailoring
modification that introduces

15. positively charged amines.[35] WTAs that lack D-alanyl esters
can bind up to 60 % more
Mg2+ ions than analogous polymers that contain this
modification.[36] The importance of
cation binding is highlighted by the observation that B. subtilis
strains up-regulate their
production of TAs in the presence of low Mg2+ concentrations,
and produce other negatively
charged polymers (teichuronic acid) in the presence of limiting
phosphate concentrations.
[37] Recent structural studies have been focused on elucidating
modes of cation binding by
WTA polymer phosphate groups, and researchers have
suggested that a clear understanding
of the three-dimensional structure of WTAs and their bound
cation groups might provide
insights that facilitate the design of novel antimicrobials.[38]
Scaffolding roles
In addition to providing binding sites for cations, WTAs serve
as scaffolds or receptors for a
wide range of other molecules. In S. aureus, for example, they
function as receptors that are
required for phage infection.[39] Depending on their tailoring
modifications (see below) they
might also promote adhesion by lytic enzymes produced by
neutrophils.[40] They are
additionally thought to serve as scaffolds for endogenously
produced cell wall hydrolases
(autolysins) involved in cell growth and division.[41] In
general, the molecular interactions
between WTAs and other biomolecules are not well understood
but could provide crucial
insights into cell envelope function.
Tailoring modification-dependent functions

16. The main chain hydroxyl groups on both glycerol- and ribitol
phosphate WTA polymers are
subject to further derivatization by tailoring enzymes (Figure
2). There are two classes of
tailoring enzymes: those that catalyze the addition of D-alanyl
esters, and those that append
glycosyl groups. The extent to which these modifications occur
on the TA polymers is strain
dependent and can also be affected by environmental
conditions. Efforts have been made to
understand the role(s) of these modifications in bacterial
physiology, and some of these studies
are highlighted below.
The D-alanylation tailoring modification has been more
extensively investigated than
glycosylation and is far better understood at this point. Perego
et al. were the first to characterize
the genetic pathway responsible for this modification (dlt
operon) in B. subtilis.[42] Briefly,
the biosynthetic pathway begins intracellularly with the
activation of D-alanine to its
corresponding aminoacyl adenylate by DltA. This molecule is
then covalently attached, as a
thioester, to a cofactor bound to the D-Ala carrier protein, DltC.
Although the precise roles of
DltB and DltD have not been confirmed, it is believed that they
facilitate the transport of DltC
through the membrane and the incorporation of D-Ala onto both
LTAs and WTAs.[43] It has
been found that D-alanylation is affected by several factors,
including growth media, pH and
temperature.[5] The attachment of D-alanyl esters to the
hydroxyls on TAs alters the net charge
of the polymer by adding positively charged amines. This
modification reduces the electrostatic

17. repulsion between neighboring TA chains and possibly
facilitates stabilizing ion-pair
formation between the cationic esters and the anionic phosphate
groups.[38]
The D-alanine modification modulates interactions between the
cell envelope and the
environment and has been implicated in many of the known
scaffolding/receptor functions of
WTAs.[5,44] For example, it has been shown that the absence
of D-alanyl esters on the TA
polymers increases susceptibility to cationic antimicrobial
peptides, possibly by increasing the
negative charge density on the cell surface.[45,46] Removing
the alanine residues also
increases bacterial sensitivity to glycopeptide antibiotics and to
the lytic activity of enzymes
produced by neutrophils during host infection.[40,41] In
contrast, the activity of autolytic
enzymes is decreased, suggesting a role for TAs in scaffolding
and/or activating bacterial
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enzymes involved in the processes of cell-wall synthesis and
degradation.[41] Removal of D-
alanyl esters from TAs has also been shown to attenuate the
binding of S. aureus to artificial
surfaces as well as host tissue. A recent study has illustrated the
importance of the charge
balance of WTAs in adhesion to artificial surfaces, such as glass
and polystyrene.[44]
Since D-alanylation promotes better adhesion to host tissue and
confers some resistance to lytic

19. enzymes produced by the host, mutant strains lacking this
modification have been studied in
animal infection models. For example, in a mouse tissue cage
infection model, bacterial strains
lacking D-alanylation were more susceptible to Toll-like
receptor 2-dependent host defenses;
[46] in a septicemia model, such strains were attenuated in their
ability to establish an infection,
possibly because they were more readily killed by
neutrophils.[40] Based on these and other
studies, it was proposed that the D-alanine modification is a
putative target for novel
antimicrobials that function by attenuating virulence. In 2005,
May et al. reported the synthesis
and evaluation of a nonhydrolysable analogue of D-Ala
aminoacyl adenylate as the first
designed inhibitor of DltA, the enzyme that activates D-Ala.
The compound enhanced the
activity of vancomycin against B. subtilis.[43] This result is
consistent with inhibition of DltA,
and supports the idea that small molecules that interfere with D-
alanylation might provide a
novel strategy for antimicrobials.
Glycosylation is a ubiquitous tailoring modification of WTAs
but its functions are not well
understood. Glucose is commonly added to the WTA polymers
in B. subtilis, whereas N-acetyl
glucosamine (GlcNAc) is added in S. aureus (Figure 2).[5]
Depending on the bacterial strain,
the stereochemistry of the glycosidic linkage may be β-, α-, or a
mixture of the two anomers.
All sequenced B. subtilis and S. aureus strains contain one or
more putative glycosyltransferase
genes clustered with the WTA biosynthetic genes (Figure 3).
For example, B. subtilis 168

20. contains a gene for a putative retaining glycosyltransferase that
might add a-Glu to the glycerol
phosphate polymers. S. aureus strains contain two genes
encoding putative inverting
glycosyltransferases that might transfer β-GlcNAc to the
poly(ribitol phosphate) polymers.
Although some S. aureus strains have been shown to contain α-
glycosidically linked WTAs,
there are no genes yet identified for any glycosyltransferases
that can carry out this tailoring
modification. Furthermore, no studies have confirmed the
enzymatic functions of any of the
putative WTA glycosyltransferases or have explored the effects
of preventing WTA
glycosylation on bacterial cell growth, division, intercellular
interactions, or pathogenesis. In
fact, as far as we know there is only one piece of data
pertaining to the functions of WTA
glycosyltransferases in the literature: a transposon mutant in a
putative glycosyltransferase in
the S. aureus strain Newman showed attenuated virulence in a
nematode killing assay,
suggesting that glycosylation might play a role in pathogenesis
in S. aureus.[47] If
glycoslyation proves important for bacterial pathogenesis, the
glycosyltransferase tailoring
enzymes, like the enzymes involved in D-alanylation (see
above) would be possible targets for
antimicrobials.
Roles in cell elongation and division
Recent studies have implicated LTAs and WTAs in cell growth,
division, and morphogenesis.
In the rod-shaped organism B. subtilis, TAs have been shown to
play distinct roles in bacterial
morphogenesis. Preventing WTA expression results in the

21. production of round, severely
defective progeny, while preventing LTA biosynthesis causes
major defects in septum
formation and cell separation.[27,49] It is known that there are
separate multiprotein complexes
involved in septation and elongation in B. subtilis, and
Errington and co-workers have
suggested (based on localization studies using fluorescently
tagged enzymes) that the WTA
biosynthetic enzymes associate with the machinery involved in
elongation, while the LTA
enzymes might associate with machinery involved in septation
and cell division.[27,50] It was
suggested that the spatial distribution of these two anionic
glycopolymers determines their
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specific functions. Defects in S. aureus upon deletion of WTAs
are less pronounced than in
B. subtilis, and no specific roles in cell growth and division for
WTAs in this organism have
been proposed. However, Oku et al. recently reported that S.
aureus strains devoid of LTAs
show major defects in septal formation and cell separation and
grow only under a restricted
range of conditions, including reduced temperatures.[26]
Functions in biofilm formation and host tissue adhesion
As major components of the cell envelope, WTAs influence the
interactions of bacterial cells
with their environment in many ways. We have already
mentioned that S. aureus mutants
lacking WTAs show reduced initial adherence to artificial
surfaces, including glass and

23. polystyrene;[44] they are also impaired in their ability to form
biofilms. It has been shown that
WTA null mutants that are impaired in biofilm formation do not
have a reduced production of
the exopolysaccharide poly-N-acetylglucosamine (PNAG),
which has been identified as an
important factor for biofilm formation.[30] This finding
highlights the independent role that
WTAs play in biofilm formation.
S. aureus WTAs are also required for adhesion to host tissue.
Peschel and co-workers have
shown that S. aureus strains that do not express WTAs are
severely impaired in their ability to
adhere to nasal epithelial cells and are unable to colonize the
nasal passages of cotton rats.[8]
They have also shown that WTA-null mutants cannot colonize
endothelial tissues derived from
kidney and spleen.[9] The D-alanylation machinery was not
impaired in these strains, and D-
alanylation could still have occurred on LTAs; therefore, these
results implicate WTAs as
independent factors involved in cell adhesion. Since WTAs are
required for host infection and
play important roles in biofilm formation, it was suggested that
they are virulence factors, that
is, factors required for the establishment and spread of infection
in a host. Therefore, the
enzymes involved in WTA biosynthesis were suggested to be
targets for novel antimicrobials
that impede host colonization by S. aureus.[7]
Biosynthesis of Wall Teichoic Acids
Poly(glycerol phosphate) WTA biosynthesis in B. subtilis 168
The pathway for WTA biosynthesis was first characterized in B.

24. subtilis 168, which makes
poly(glycerol phosphate) WTAs (Figure 4 A).[51] The genes
involved in the synthesis of these
WTAs are known as tag genes (for teichoic acid glycerol). The
pathway starts in the cytoplasm
with the transfer of GlcNAc phosphate to an undecaprenyl
phosphate (also known as
bactoprenyl phosphate) carrier anchored in the bacterial
membrane. The enzyme that catalyzes
this reaction, TagO, is reversible and is related to a large family
of phosphosugar transferases
that includes the first enzyme in the dolichol pathway for N-
linked glycosylation in eukaryotes,
GPT, as well as MraY, an essential bacterial enzyme involved in
peptidoglycan biosynthesis.
[52,53] Following formation of the GlcNAc-pp-lipid by TagO,
an N-acetylmannosaminyl
transferase, TagA, transfers ManNAc from UDP-ManNAc to the
C4 hydroxyl of the GlcNAc
residue to form a β-linked disaccharide, which is the substrate
for the next enzyme in the
pathway, TagB.[54,55] TagB is a glycerophosphate transferase
that transfers a single
phosphoglycerol unit from CDP-glycerol to the C4 hydroxyl of
ManNAc to complete the
synthesis of the linkage unit (Figure 2).[54,56] The next enzyme
in the B. subtilis 168 pathway,
TagF, is a polymerizing cytidylyl transferase that attaches 35 or
more glycerol phosphates to
the linkage unit to form the anionic polymer.[57–59] The
catalytic domains of TagB and TagF
share significant sequence identity and belong to a group of
phosphotransferases that are
apparently unique to WTA biosynthesis. Other members of this
family include TarB, F, K, and
L (see below). Once assembled, the lipid-linked WTA polymer

25. is putatively modified by a
glycosyltransferase (TagE) and then exported to the external
surface of the bacterial membrane
by a two-component ABC (ATP binding cassette) transporter,
TagGH.[60] The polymer is
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coupled to peptidoglycan through the anomeric phosphate of the
GlcNAc residue. The
transferase responsible for carrying out this reaction has not
been identified. D-Alanine ester
formation occurs outside of the cell as described above.[5]
Poly(ribitol phosphate) WTA biosynthesis in B. subtilis W23
B. subtilis W23 makes a poly(ribitol phosphate) WTA rather
than a poly(glycerol phosphate)
WTA (Figure 2). A pathway for the biosynthesis of B. subtilis
W23 WTA was proposed by
Lazarevic et al., who designated the genes involved as tar genes
(for teichoic acid ribitol).
[19] The first three steps of the proposed pathway, mediated by
TarO, TarA, and TarB are
identical to those in B. subtilis 168, but the pathways then
diverge (Figure 4 B). The TagF
homologue in B. subtilis W23, TarF, functions not as a
polymerase but as a primase, and adds
one additional glycerol phosphate unit to the 168-type linkage
unit. The catalytic domains of
TagF, which is a polymerase, and TarF, which is a primase,
share significant sequence identity
and the structural features in each enzyme that determine
whether one or many glycerol
phosphate units is transferred to the linkage unit have not been
identified. Once the W23 linkage

27. unit is complete, the poly(ribitol phosphate) main chain is
assembled. Lazarevic et al. proposed
that the assembly of this poly(ribitol phosphate) chain requires
two enzymes: TarK, which
transfers a single ribitol phosphate residue to the linkage unit,
and TarL, which carries out the
polymerization of the ribitol phosphate chain.[19] TarK and
TarL in B. subtilis W23 were thus
suggested to function as a primase/polymerase pair, analogous
to the primase/polymerase pair
(TagB/TagF) that assembles the poly(glycerol phosphate) chain
in strain 168. Meredith et al.
used a genetic approach to verify that tarK and tarL from B.
subtilis W23 are both required for
the assembly of poly(ribitol phosphate) WTAs;[61] this is
consistent with the proposed
primase/polymerase model for biosynthesis. Pereira et al.
subsequently confirmed this finding.
[62] Once the poly(ribitol phosphate) WTA polymer is
assembled, the remaining steps are
thought to be similar to those in strain 168. That is, the WTA
polymer is glycosylated,
transported through the bacterial membrane by a two-component
transporter, TarGH, attached
to peptidoglycan by an unidentified transferase, and esterified
with D-alanine residues.
Poly(ribitol phosphate) WTA biosynthesis in S. aureus
Like B. subtilis W23, S. aureus also makes a ribitol phosphate
WTA polymer. Except for the
length of the polymer chain and the nature of the appended
sugar residues, the structures of
the poly(ribitol phosphate) WTAs are thought to be the same in
B. subtilis W23 and S.
aureus (Figure 2). The assembly of the linkage unit in S. aureus
is identical to its synthesis in

28. B. subtilis (TarO, TarA, TarB, TarF catalyze the same
reactions), but the main chain is
assembled not by a primase/polymerase pair, but by one of two
bifunctional poly(ribitol
phosphate) primase/polymerases (currently designated TarK and
TarL although their functions
are different from TarK/TarL in B. subtilis; Figure 4 C). It has
been proposed that S. aureus
TarL makes a primary WTA polymer (L-WTA) while S. aureus
TarK makes a secondary WTA
polymer (K-WTA).[61] As outlined in the following section,
however, there are still a number
of questions about the cellular roles of the two bifunctional
poly(ribitol phosphate) polymerases
in S. aureus. Once the ribitol phosphate polymer is completed in
the cytoplasm, glycosylation
occurs and the polymer is flipped to the external surface of the
membrane by an ABC-dependent
transporter complex (TarGH) before ligation to the cell wall by
unidentified enzyme(s) and D-
alanylation.
Gene Cluster Duplication in S. aureus
As noted in the previous section, S. aureus contains two
bifunctional poly(ribitol phosphate)
polymerases that have similar enzymatic functions rather than a
pair of enzymes containing
separate primase and polymerase activities. Qian et al. were the
first to note that S. aureus may
differ from B. subtilis in how it accomplishes poly(ribitol
phosphate) polymerization. In a
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genomic analysis of six sequenced S. aureus strains (Figure

30. 3)[48] these authors noted that all
of the strains contained an apparent duplication of the region of
the chromosome containing
the putative ribitol phosphate polymerase gene and the two
genes involved in the synthesis of
its CDP-ribitol substrate (tarI,J,L). These two similar gene
clusters have since been designated
in the literature as tarI,J,L and tarI′,J′,K. The tarK gene is
highly homologous to the tarL gene,
which suggests it might have the same enzymatic function.
Walker and co-workers provided
the first experimental evidence for a unique S. aureus-specific
poly(ribitol phosphate) WTA
biosynthetic pathway. By utilizing an approach previously
developed to study peptidoglycan
biosynthesis[63,64] and later applied to validate part of the B.
subtilis WTA pathway,[54] the
Walker group reconstituted the biosynthesis of S. aureus
poly(ribitol phosphate) WTA in vitro.
[65] Through the use of synthetic substrates, it was shown that
S. aureus TarL is, in fact, a
bifunctional enzyme that combines both primase and
polymerase activities (Figure 4).[65]
They demonstrated that a dedicated ribitol phosphate primase
was not required for WTA
polymer synthesis in S. aureus as it is in B. subtilis.
The in vitro studies of WTA biosynthesis established the
enzymatic function of TarL in S.
aureus, but did not answer the question of why S. aureus
contains an additional gene, tarK,
that is homologous to tarL. Meredith et al. and Pereira et al.
used genetics to probe the cellular
roles of tarK and tarL.[61,62] Under certain conditions, it was
shown that tarK can compensate
for the loss of tarL, and both groups have concluded that TarK,

31. like TarL, is a bifunctional
enzyme that combines ribitol phosphate primase and polymerase
activities. However, Meredith
et al. and Pereira et al. have proposed alternative explanations
for the cellular roles of tarK and
tarL. Pereira et al. have suggested that tarK is a redundant gene
resulting from duplication, and
have argued that its function is decaying. Meredith et al.
reached a different conclusion based
on an in-depth analysis of tarK and tarL expression in cells.
Analysis of extracted WTAs from
strains that produce only TarL or TarK showed that these two
enzymes produce
electrophoretically distinct poly(ribitol phosphate) WTAs,
designated L-WTA and K-WTA
(Figure 5 A). K-WTA is significantly shorter than L-WTA and
is presumed, based on PAGE
banding patterns, to contain subtle differences in composition.
Furthermore, K-WTA
biosynthesis is negatively regulated (directly or indirectly) by
the agr (accessory gene
regulator) quorum sensing system.[61] Since tarK expression
can cause the polymer to shorten
by up to 50 %,[61] it was suggested that regulation of tarK by
agr allows S. aureus to
dynamically control WTA chain length as a function of cell
density. WTA polymer length
might affect exposure of surface adhesins, and it was proposed
that dynamic regulation of WTA
polymer length allows S. aureus to cycle between a pro-
adhesion state and a low-adhesion
state, perhaps to promote adhesion and dissemination at
appropriate times during the infection
process (Figure 5 B). Determination of the exact structures of
K-WTA and L-WTA and their
potential roles in virulence remain to be addressed.

32. Disagreement about the functions of tarK and tarL extends to
other genes within the two tarI
′J′K/IJL clusters. It has been reported that tarI′ and tarI are both
nonessential,[66] that only
tarI is essential,[67] or that tarI is only essential under a certain
set of growth conditions in
vitro but is nonessential in an in vivo infection model.[68]
Furthermore, in S. aureus Newman,
seven viable transposants were isolated within tarI′J′K but none
was isolated in tarIJL,[47]
suggesting that the gene duplications are not redundant.
Recently, Chaudhuri et al. have
reported that tarI, tarJ, and tarL are essential in S. aureus.[69]
These discrepancies can be
collectively resolved by suggesting that there are differences in
tarI′J′K expression, which
depend on culture conditions and strain backgrounds. The fact
that all fourteen sequenced S.
aureus strains retain both tarI′J′K and tarIJL intact, argues
against simple functional
redundancy and suggests a selective pressure for maintaining
both clusters.
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Characterization of the Wall Teichoic Acid Biosynthetic Genes
in S. aureus
WTA biosynthesis exhibits a mixed gene dispensability pattern
It was first observed in the 1960s that WTAs are not essential
for the survival of S. aureus in
vitro,[39] and a number of studies on genetically
uncharacterized WTA null mutants were
reported in subsequent years.[70–74] In 2004, Peschel and co-

34. workers characterized a defined
WTA-null S. aureus strain lacking tarO, the first gene in the
WTA biosynthetic pathway.[8]
This ∆tarO strain was reported to have a similar in vitro growth
rate to the wild-type strain,
but was greatly impaired in its ability to adhere to epithelial and
endothelial tissues. The
adhesion-impaired mutant was unable to colonize nasal
passages, leading to the suggestion
that WTAs might be virulence factors since they are required
for host infection. Brown and
co-workers subsequently reported that tarA, like tarO, can also
be deleted.[75] The ∆tarA
WTA-null strains are viable in vitro and are phenotypically
identical to the ∆tarO strains;
however, many of the genes downstream of tarA in the S. aureus
WTA pathway (depicted in
red in Figure 6) cannot be deleted unless tarO (or tarA) is
deleted first.[67] These downstream
genes are “conditionally essential”: that is, they are required for
viability in a strain background
containing a functional WTA pathway, but are not required in a
WTA-null background. This
mixed gene dispensability pattern implies that blocking late-
acting WTA biosynthetic enzymes
after flux into the pathway has been initiated is deleterious to
bacterial growth.
Possible explanations for conditional essentiality
The mixed gene dispensability pattern observed for WTA
biosynthesis has also been reported
for several other nonessential biosynthetic pathways in which a
cell surface glycopolymer is
assembled on an undecaprenyl phosphate carrier lipid.[76–80]
Such pathways exist in virtually
all bacteria. For example, they are involved in the synthesis of

35. O-antigens, capsular
polysaccharides, and exopolysaccharides. In pathogenic
bacteria, these cell surface polymers,
like WTAs, play roles in virulence. Two explanations have
generally been considered for the
apparent toxicity caused by blocking late steps in these
pathways. One explanation attributes
toxicity to depletion of undecaprenyl phosphate-linked
peptidoglycan precursors and the
resulting effects on peptidoglycan biosynthesis. Undecaprenyl
phosphate is used as the carrier
lipid in the peptidoglycan biosynthetic pathway; however, only
small amounts of this carrier
lipid are produced and the cell's capacity to increase these
levels is limited. Therefore, any
metabolic block that leads to accumulation or sequestration of
undecaprenyl phosphate-linked
intermediates is potentially harmful to cells. Indeed, a number
of cell-wall active antibiotics,
including bacitracin, vancomycin, and ramoplanin, function by
sequestering peptidoglycan
precursors.[81–83] An alternative explanation attributes the
observed toxicity upon blocking
nonessential bactoprenol-dependent pathways to an
accumulation of bactoprenol-linked
intermediates that are somehow directly harmful to cells.[84]
Evidence for and against both
mechanisms has been presented, but a consensus has not yet
been reached. Since these
possibilities are not mutually exclusive, it is possible that both
play a role.
Brown and co-workers have proposed that peptidoglycan
substrate depletion is the mechanism
for toxicity when WTA biosynthesis is blocked in B.
subtilis.[85] Microarray analysis was

36. used to identify genes up-regulated in B. subtilis upon depletion
of TagD, the
cytidylyltransferase that provides activated glycerol phosphate
for poly(glycerol phosphate)
synthesis. The promoters for ten highly up-regulated genes were
then fused to the lux operon
and the luminescence signal upon tag gene depletion was
evaluated. One of the promoters,
PywaC, gave a particularly robust luminescent signal when
WTA biosynthesis was disrupted at
a late step. The PywaC reporter was activated by cell-wall
active antibiotics that sequester
peptidoglycan precursors as well as by depletion of genes
involved in undecaprenol
biosynthesis. It was not activated by depletion of tagO. Since
the PywaC reporter strain
responded to perturbations known to affect pool levels of
bactoprenol-linked peptidoglycan
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intermediates and to late-acting tag gene depletion, it was
suggested that blocking late-acting
WTA enzymes is toxic because it affects levels of bactoprenol-
linked peptidoglycan substrates.
Brown and co-workers have speculated that their reporter assay
can be used in a high-
throughput screen to identify compounds that target either the
cell wall or wall teichoic acid
biosynthetic pathways.
Inhibitors of Wall Teichoic Acid Biosynthesis
Methicillin-resistant S. aureus infections have become a major
problem in the United States,
recently surpassing HIV/AIDS as a cause of death. Although

38. there are still a handful of effective
anti-MRSA antibiotics, clinical resistance is inevitable and,
indeed, has already been observed
for the most recently introduced antibiotics.[86] Thus, an urgent
need exists for the exploration
of new strategies to battle resistant S. aureus infections.
The WTA biosynthetic pathway has been speculated to be an
antibiotic target for many years,
but only one specific inhibitor has recently been reported. The
mixed gene dispensability
pattern implies that there are two distinct types of possible
antimicrobial targets within the
pathway: antivirulence targets (TarO and TarA; depicted in
green in Figure 6) and antibiotic
targets (the conditionally essential downstream enzymes;
depicted in red). Small molecule
inhibitors of the former are expected to impede colonization and
the spread of infection, while
inhibitors of the latter have been shown to prevent bacterial
growth (see below). The known
inhibitors of WTA biosynthesis are described below.
Inhibitors of antivirulence targets in the WTA pathway
Peschel and co-workers were the first to suggest that WTA
biosynthesis is an antivirulence
target in S. aureus.[8] This possibility has attracted
considerable attention because it is
speculated that resistance to nonessential targets involved in
pathogenicity (virulence factor
targets) will not develop as readily as it does to more traditional
antibiotic targets.[87,88] May
et al. have reported a small molecule that inhibits the D-alanine
tailoring modification in both
LTAs and WTAs, and the activity of this compound in
preliminary studies supports the

39. possibility that inhibiting D-alanylation could attenuate the
virulence of pathogenic organisms.
[43] In addition to this compound, there is a very potent natural
product inhibitor of WTA
biosynthesis, the uridine-containing antibiotic tunicamyin
(Figure 7).[72,89] Tunicamycin is
a promiscuous inhibitor of the large family of enzymes that
couple sugar phosphates to
membrane-embedded lipid phosphates.[52] Its well-known
antibacterial activity derives from
its ability to inhibit MraY, an essential phosphosugar
transferase in the peptidoglycan
biosynthetic pathway; however, tunicamycin also inhibits
TarO.[10,52] In fact, tunicamycin
is selective for TarO over MraY by a factor of at least 100. Its
selectivity for TarO makes it a
useful tool for shutting off WTA expression in vitro without
affecting bacterial growth rates.
Unfortunately, it cannot be used in animals to assess whether
inhibiting TarO is a viable strategy
for treating S. aureus infections because it is toxic to
eukaryotes. It inhibits an essential
eukaryotic phosphosugar transferase involved in the dolichol
pathway for N-linked
glycosylation (GPT), which catalyzes the same chemical
transformation as TarO. Nontoxic,
selective inhibitors of TarO (or TarA) remain to be discovered.
Inhibitors of antibiotic targets in the WTA pathway
The first inhibitor of a putative antibiotic target in the WTA
biosynthetic pathway was recently
reported by Swoboda et al.[10] It was discovered by using a
general cell-based screening
approach that exploits the conditional essentiality of the late-
acting enzymes (Figure 8).[10]
The screening strategy developed to discover WTA inhibitors is

40. applicable, in principle, to any
nonessential biosynthetic pathway containing conditionally
essential genes. It involves
screening a compound library against a pair of bacterial strains,
one a wild-type strain and the
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other a null mutant that does not express the polymer of interest
(e.g., WTAs). Compounds
that inhibit the growth of the polymer-expressing wild-type
strain, but not of the mutant, are
expected to target the conditionally essential enzymes in the
desired biosynthetic pathway.
Screening paired strains ensures target specificity while
eliminating compounds that inhibit
essential cellular processes or are toxic for other reasons
(Figure 8). Since antibiotic discovery
is challenging, a cell-based screen that ensures cellular activity
is critical, but designing a screen
to report on a particular pathway is typically difficult. The
reported strategy combines two
important features that are not often found together in high-
throughput screens: it is both cell-
based and pathway specific.
Swoboda et al. used the paired strain screening strategy to
identify WTA inhibitors with
antibiotic activity.[10] The growth of S. aureus RN4220 and the
corresponding ∆tarO strain
were monitored in the presence of a library of 55 000 small
molecules. Three inhibitors were
found to inhibit the wild-type strain without affecting the
mutant. The most active of the three
compounds (1835F03; Figure 7) was found to have a minimum

42. inhibitory concentration of 1–
2 µg mL–1 (2.5–5 µM) against all S. aureus strains examined,
including clinical MSSA and
MRSA isolates. A comprehensive set of genetic and
biochemical experiments have shown that
the target of the compound is TarG, the transmembrane
component of the dedicated, two-
component ABC transporter that exports WTAs from the
cytoplasm to the cell surface.
The discovery of a small molecule that inhibits a late-acting
step in WTA biosynthesis and has
growth inhibitory activity validates the WTA pathway as a
possible antibacterial target, but
the efficacy of this antibacterial strategy has yet to be
determined. Resistance to the reported
WTA inhibitor occurs at a high frequency in vitro (1 in 106)
and two classes of resistant mutants
have been identified. One class involves mutations in the target
(TarG), a common theme for
antibiotics. The other mutants contain changes in the tarO or
tarA genes, which abolish WTA
expression. The observation that this latter class of mutations
occurs frequently is perhaps not
surprising, as the pathway is not essential for growth in vitro.
Under ordinary circumstances,
obtaining a high frequency of resistant mutants in vitro would
suggest that a particular pathway
is not a viable antimicrobial target. However, the WTA
biosynthetic pathway presents an
unusual and previously unexplored paradigm. A large
percentage of the resistant mutants do
not express WTAs but, because Peschel and co-workers have
reported that S. aureus strains
lacking WTAs are incapable of colonizing a host, these resistant
mutants are not expected to

43. survive in vivo. If they do not, then the null mutants are not a
factor for resistance in animals.
As we have pointed out above, there are numerous other
pathways that contain conditionally
essential enzymes linked to virulence-factor expression. Many
of these enzymes could be good
antibiotic targets provided that the major mechanism for
resistance involves deletion of the
pathway, and results in the production of avirulent organisms.
The recent discovery of a small
molecule that inhibits a conditionally essential step in a
virulence factor pathway provides a
starting point for investigating this novel antibacterial strategy.
Outlook
Extensive work over several decades has illuminated many of
the roles of TAs in Gram-positive
bacteria and has firmly established their importance in bacterial
physiology. A better
understanding of the WTA biosynthetic pathway has been aided
by both biochemical and
genetic studies, and most of the steps in the B. subtilis and S.
aureus WTA biosynthetic
pathways have been reconstituted in vitro by using synthetic
substrates. A small molecule
antibiotic that targets WTA biosynthesis in S. aureus was
recently discovered by utilizing the
recent genetic and biochemical advances in this field, and will
make possible studies to evaluate
WTA biosynthesis as a pathway for therapeutic intervention.
Positive outcomes from these
studies would validate this class of virulence factors as
antibacterial targets and provide further
impetus for their study and exploitation.
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45. Acknowledgments
This work was supported by the NIH (1P01AI083214 and
5R01M078477 to S.W., and F3178727 to J.G.S.), a Mary
Fieser Postdoctoral Fellowship to J.C., and a training grant to
T.C.M (T32-AI07061-30).
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Chalker AF, Iordanescu S, Fan J, Fan F,
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Figure 1.
Simplified depiction of Gram-positive and Gram-negative
bacterial cell envelopes. Gram-
negative organisms have a distinct periplasm; Gram-positive
organisms do not, but recent
studies have suggested that they have a periplasmic-like
compartment between the plasma
membrane and the base of the peptidoglycan layers.[3] Proteins
are omitted from the depictions
for clarity. Membrane-embedded, membrane-anchored, and
peptidoglycan-associated proteins
are abundant in the cell membranes of both Gram-positive and
Gram-negative organisms. LTA:
lipoteichoic acid; LPS: lipopolysaccharide; WTA: wall teichoic
acid.
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Figure 2.
Representative chemical structures of wall teichoic acids from
different Gram-positive bacteria
(m = 1–3 and n = 20–40).
Swoboda et al. Page 15

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58. Figure 3.
Genetic organization of wall teichoic acid biosynthetic genes;
tag: teichoic acid glycerol; tar:
teichoic acid ribitol. Adapted from Qian et al.[48] (//: number
of nucleic acids between genes
if > 120 base pairs).
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Figure 4.
Differences in wall teichoic acid biosynthesis for: A) B. subtilis
168, B) B. subtilis W23, and
C) S. aureus.
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Figure 5.
Proposed role for the distinct TarK and TarL WTA polymers. A)
TarK and TarL make
electrophoretically distinct WTA polymers. PAGE analysis of
WTAs extracted from various
S. aureus strains shows that poly(ribitol phosphate) polymer
length corresponds to gene
regulation. The agr system directly or indirectly represses the
expression of TarK, leading to
an increase in WTA polymer length. RN4220 has a partial
defect in agr while RN450 has a
fully functional agr system (agr+). TarK makes a short
secondary polymer, K-WTA, while
TarL makes a longer primary polymer, L-WTA. The L-WTA
was extracted from a ∆tarK
mutant of S. aureus RN4220. K-WTA was extracted from an S.

61. aureus RN4220 mutant over-
expressing tarK in a ∆tarL background.[61] B) Expression of
the shorter K-WTA at low cell
density might allow for increased accessibility of adhesins,
leading to a pro-adhesion state;
whereas, the expression of the longer L-WTA at high cell
density (mediated by the activation
of agr) might lead to a low-adhesion phenotype.
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Figure 6.
Depiction of the primary Staphylococcus aureus wall teichoic
acid (L-WTA) biosynthetic
pathway. Nonessential WTA pathway enzymes are colored
green and their deletion leads to
an avirulent phenotype. Conditionally essential enzymes are
colored red and their deletion is
lethal in a wild-type background but permitted in a ∆tarO or
∆tarA background. Following
intracellular assembly, the poly(ribitol phosphate) polymer is
transported to the outside by a
two-component ABC transporter, TarGH, and then covalently
linked through a phosphodiester
bond to the MurNAc sugars of peptidoglycan by an unidentified
enzyme the biological and
genetic properties of which have not been established. In
addition to TarI, J, and L, all S.
aureus strains contain a homologous set of enzymes (designated
TarI′,J′ and K; Figure 3) that
directs the synthesis of a distinct WTA polymer (K-WTA); their
cellular functions remain
incompletely understood.
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64. Figure 7.
Chemical structures of currently known inhibitors of wall
teichoic acid synthesis and export
in Staphylococcus aureus. An inhibitor of DltA, which is
involved in modification of both
WTAs and LTAs, has also been reported.[43]
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Figure 8.
General screening strategy for the identification of small-
molecule inhibitors of conditionally
essential enzymes/targets in nonessential biosynthetic pathways;
*: the mutant strain is
incapable of initiating polymer synthesis. In the described
screen, the paired strain lacked the
first enzyme involved in WTA biosynthesis (TarO).
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Name: ____________________________________
Bio 351- Homework 1 (10 points)
DUE TUESDAY JANUARY 16th at 11PM on Blackboard.
ONLY Word documents accepted.
Go to pages 1-5 of the article: Wall Teichoic Acid Function,
Biosynthesis, and Inhibition to answer questions 1-3 below (30
words maximum per answer):
1. What are the differences between cell wall associated
teichoic acids (WTA) and lipoteichoic acid (LTA) (2 points)?
WTA and LTA are modified by D-alanylation, a process that
incorporates D-alanine into teichoic acids. How is D-alanylation

67. beneficial for gram-positive bacteria (2 points)?
2. How does cation-binding influence the stability of a gram-
positive surface (cell wall)? (2 points)
3. What roles are teichoic acids involved in that contribute to
the stability of a gram-positive cell wall? (2 points)
Correct the Statement (1 point each): The statements from each
question below are false. Correct the statement by crossing out
a word/words. The statement must be correct, not partially
correct. Please do not rewrite your corrected statement. You
must cross out the word on this document, and manually correct
each statement.
4. Lipoteichoic acids are seen within the plasma membrane of E.
coli.
5. Lipopolysaccharides are molecules seen within the periplasm
of gram positive bacteria.
Part 3 Inferential Statistics
QNT/561 Version 9 2
Part 3: Inferential Statistics
Option 1: Manufacturing Database
The National Association of Manufacturers (NAM) contracts
with your consulting company to determine the estimate of
mean number of production workers. Construct a 95%
confidence interval for the population mean number of
production workers. What is the point estimate? How much is
the margin of error in the estimate?

68. Suppose the average number of employees per industry group in
the manufacturing database is believed to be less than 150
(1000s). Test this belief as the alternative hypothesis by using
the 140 SIC Code industries given in the database as the
sample. Let α = .10. Assume that the number of employees per
industry group are normally distributed in the population.
You are also required to determine whether there is a significant
difference between mean Value Added by the Manufacturer and
the mean Cost of Materials in manufacturing using alpha of
0.01.
You are requested to determine whether there is a significantly
greater variance among values of Cost of Materials than of End-
of-Year Inventories.
Option 2: Hospital Database
As a consultant, you need to use the Hospital database and
construct a 90% confidence interval to estimate the average
census for hospitals. Change the level of confidence to 99%.
What happened to the interval? Did the point estimate change?
Determine the sample proportion of the Hospital database under
the variable “service” that are “general medical” (category 1).
From this statistic, construct a 95% confidence interval to
estimate the population proportion of hospitals that are “general
medical.” What is the point estimate? How much error is there
in the interval?
Suppose you want to “prove” that the average hospital in the
United States averages more than 700 births per year. Use the
hospital database as your sample and test this hypothesis. Let
alpha be 0.01.
On average, do hospitals in the United States employ fewer than

69. 900 personnel? Use the hospital database as your sample and an
alpha of 0.10 to test this figure as the alternative hypothesis.
Assume that the number of births and number of employees in
the hospitals are normally distributed in the population.
Option 3: Consumer Food
Suppose you want to test to determine if the average annual
food spending for a household in the Midwest region of the U.S.
is more than $8,000. Use the Midwest region data and a 1%
level of significance to test this hypothesis. Assume that annual
food spending is normally distributed in the population.
Test to determine if there is a significant difference between
households in a metro area and households outside metro areas
in annual food spending. Let α = 0.01.
The Consumer Food database contains data on Annual Food
Spending, Annual Household Income, and Non-Mortgage
Household Debt broken down by Region and Location. Using
Region as an independent variable with four classification
levels (four regions of the U.S.), perform three different one-
way ANOVA's—one for each of the three dependent variables
(Annual Food Spending, Annual Household Income, Non-
Mortgage Household Debt). Did you find any significant
differences by region?
Option 4: Financial Database
Use this database as a sample and estimate the earnings per
share for all corporations from these data. Select several levels
of confidence and compare the results.
Are the average earnings per share for companies in the stock
market less than $2.50? Use the sample of companies
represented by this database to test that hypothesis. Let α = .05.
Test to determine whether the average return on equity for all

70. companies is equal to 21. Use this database as the sample and α
= .10. Assume that the earnings per share and return on equity
are normally distributed in the population.
Do various financial indicators differ significantly according to
type of company? Use a one-way ANOVA and the financial
database to answer this question. Let Type of Company be the
independent variable with seven levels (Apparel, Chemical,
Electric Power, Grocery, Healthcare Products, Insurance, and
Petroleum). Compute three one-way ANOVAs, one for each of
the following dependent variables: Earnings Per Share,
Dividends Per Share, and Average P/E Ratio.
Copyright © 2017 by University of Phoenix. All rights reserved.
Option 1 - ManufacturerSIC CodeNo. Emp.No. Prod.
Wkrs.Value Added by Mfg.Cost of MaterialsEnd Yr.
Inven.Indus.
Grp.2014333702351878713363012021318315724427743157120
32041692450627222873212041007021667370403407120522013
72071212030115512068969126401367436131207261842581913
01946120814372352103352171991209171126205481961231351
21121152344255575506221232287163422213221508314155221
46462426225542221524724714219929322274634307535714273
22313126731061325322417138177072673225169147898610421
20833226514131454140697322755444076712514463228847638
06899410143229614742765504129132312722123971635642322
00178942389262314423329425011045111212727423438321916
22836824235171459936419742363428206318134504237113471
17423831251445132152642392241791060312376274742418368
57759661578524217214710404192853979524325720913274186
32332952445143190921703555245826846067290580524994785
51881351604525127323312464129803535625270535447401182
96253372922905101447625481614182375595662595439281826
94718626115112201327972572621169018848205964257726355

71. 42965510604150272652121631566824634397672672321822591
82896354277271403136306928483894827212116179826940121
68273136571785788633736827469259699282387482756044373
84072957243008276412838783811688827721123989104757782
78655043882055504827955394055109823682818045165671129
82644928211579250253459661929283213106598132718711533
92841267531801199324535928551288497984921789286126752
88864693585779287372412277111302354928976451154713085
27499291674326006132880107181029525183464618265810299
14821874446670103016554707970911067113028744249617511
30561464528380510571130612295727571951411113087635985
56215726411874113111512131318654041231332162163351231
43731190716827161231522538562123166474739519912317873
28255751231976233177401232112917179432821332260516532
35271505133236450485042548831332417133509228282813325
31252176138770013326453626961183600133272051521573917
01019661332817139995652631332972537838543216521333122
11742918045696121981433212810690616913154314333352642
00111841834143341511141057356941433516212316670318926
37714336947958564696938143393223316427908001434133273
99993641453153421401071175087203124153434532441235271
12115344432315279743152772041534510481693649091768153
46259211198802153139971534712999779362321181153484024
35281689107715349300219217181927364601535179551051312
95436791635294709545118583339163532051331817823474734
41635429521122673143436730163551921101922116515682316
35626517223110185437898163572599641135608571027716358
20114717521218194857163593922932532213897496416361745
16700552314951736217112014278126573887173631088794661
25782299173641571171342811065307617365493734597621107
01736625812038705295919467173675883688405944486131451
73691511061392013398351417371772634105899223639158521
83723771904522042367368141837314110879037760216518374
31232590436312331837518141435167441218376812999868120
47701837947353564547611021838118668210718760618319382
27214129028180287681193842681573105116787776119385271

72. 72390102042619386613614032811422901938764415382177193
91433027613646145120393131068550632820394103768327660
42608203953526264317897992039624191406997415203991791
23111998530286120
&"Helvetica Neue,Regular"&12&K000000&P
Option 2 - HospitalHospitalGeog.
RegionControlServiceCensusBirthsPersonnel1121107312792211
11981077176231213561027231041111003553285711916818164
21159381010777441657357428421481131912125317331594101
11212572331111127169241126313043020313631430325146212
33204967615641221134716611111679176318426485051862121
92450154319631112146575520631124095921631501993325226
21142227595423621111149410912461114013136712563128451
30026621154168975327621150158360728631144201792929631
42995354306217720454083152111916861251325212750338633
52115126144342211792026204735221175141213433622146115
17172337132320963812174052939111414271936944012125310
74104241131180142110714211118476215254312124331941983
44121115496670451112151442165346132480167471311241107
79348121189298984149111181113316501119093511312803735
21212881732635311110810649435412115475960555721761317
59656731165175111655731229505685833110105075932169714
47960321129913661321185224314566232137839663486633211
14130888564332490243653211062514100166321460371433016
73214312633768341295561193693211251327116170321174153
22713111021618572331143392057331117312171224743212072
64117047532122379081576321825207127731164351567832113
91168176979321109793875803122980790813315203088231134
14708331216804948433221011185141390016188611147024487
12180776525881315045147289132113094901214514529791111
76128484792131129123493221603194019422141821543928952
21172951989622113849612319722164589545982216280666399
21113170182010022126539682581101342456012981023224001
26103311310365525341043317202511053321908510634111204
32107312375086410833215066109321783063556110311123169

73. 34711132154662391123219682797311313182570439114112110
60184911511130010211631256026211734136342885118331127
49454911931218006111203415903301215211270147112251137
07512354113286262124521100235328125331473393771263111
94398575127321172127519161285315165699262012953112013
64571130511179714703131241140053513223278016013323268
02021342211867791330135221910370136211340220231231375
11254334627451385211081071815139521613525761402211742
54502141212306080814223228050143221395699728144221923
24624087145211335331130121461214606814711131642073090
14814241601358149111743395761501218613028415132138911
45152321147114323121533422320112415431213803361553213
85094151563212451026177915731217103381583315144745315
93412811614371603127970261161721569226091627116956264
71637114078611647411630207416572123121222232166212523
09481672413104091682224301531692216671074117022123111
65162517114111466538172131144110678917313143376395174
34118509561753218263736217612249014417713124352229178
13163447396179121274122722561801319396373118114186303
81477182132280102183132250106184131181868939185531391
18939218651130228493516187521801728785188521632171607
18922131364273190222170063019111120329931379192121296
01108193131831964583194721846015141957112938721619672
11871946159319772177545105519851210403991995118583883
42005114751104
&"Helvetica Neue,Regular"&12&K000000&P
Option 3 - Consumer FoodAnnual Food Spending ($)Annual
Household Income ($)Non mortgage household debt
($)Region: 1 = NE 2 = MW 3 = S 4 = WLocation: 1
= Metro 2 = Outside
Metro890956697231801156843594570521110706526871614911
14112740412183911138556318218866111561979064218991126
94259818774119127574241576611135147204527685116314380
46854511762252408280571143224140569981138052968448061
16674492461359211734741491408811291126703158761180264

74. 87531671411856755555167831110345714832140711869450980
19114118821464037817118678519271441511143318476917295
11961959062166871192865795214161118206583551953811164
08816941518711127576952214651111774096132011773957796
22057111538388276189611457932264797911116796592801112
87769924273301116232911089876119621540701990811817147
23817819111212877427313401186425980549631112400603346
63211918554114185931278624068015202129775582631486126
77152008217131230593964312179121321170309132211274084
64505602121158176140338741214233808331147812335231899
27621226302164726631290936592411355121265265923513212
95596281112613126112423353149121043165134151961212630
64621214331245783655355021295516291011376121026270727
13287129551576341185721101435654916136218955596621162
72110197573501843221112345644710871219320611360219089
51526490221123007997917270211148466733151452111215753
59156112172044079589752155793912865762111723754821250
82193536399802177614584566712142613822385762198306678
71178211238677852936218673558251416721109445702290182
19910642631276821992875881174232142643434321323217971
41243210092182905302120151211266966991925021727249719
20838219784583991606521918750477940721586639112204092
19456518861166822627034797146229518623485201221096878
70417002228865536203200422922651577159222249133476117
70422697660968177992281525128181672228872501318763228
06259238108152288954734411814228444526452246922614835
30917139224563343551061222818550630211873133912905615
73531743648721183633195225045916478311129072805212383
11040356954222183146933934324696315626388331437131118
69550213557631130557760581731878357937185913113031633
43255313136813647917950315549403811425731410826309265
81316314414212247031770054579290653174794055131757319
09350369640431986354422243343180435183626213329552736
00363743292865187329631327987480031726132387536519135
79321074675152106593268884497423711325479489234594326
94943769212213210650759473335732518841423336413253114

75. 01891779132469136772582932805659690195943211304536542
30663281125906724032869665962032586937254101573237763
35681414332118295693403213087888221756541109865963527
86341576238407188674111617786271189441989547710229304
11629364443316874181855887135424411397287954115494111
24354778125524146353982519494411006349536121954184266
01021378741743649139223564111747510524553411539770500
12025416842548941621741967860570410641128525762531228
41101145695625907418496614001093416689505321710641156
96727741779341984169981216074112529668911768941102106
74311999541886864782144894164263898717864411109664867
58394110086504218689412587270761753441124925178420284
42845654135220374268015329123342426339498043494342780
25220528579429717728412234942602646238201654256184593
81053842102177771618516428338597117980429048421061978
64240173646299354210906534031817742151487129066964288
30667592097242848157616287674211358762211373421055378
20259204269695516424795421321961171214824235433409325
96942732650647107504284585989822940421176652884259704
2990873629711242
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Option 4 - FinancialCompanyTypeTotal RevenuesTotal
AssetsReturn on EquityEarnings per ShareDividends per
ShareAverage P/E
RatioAFLAC672512945417.12.080.2211.5Albertson's41469052
1921.42.080.6319Allstate6201068091820.13.560.3610.6Amerad
a Hess7834079350.20.080.6698.3American
General63362806207.12.191.421.2American
Stores419139853612.21.010.3423.5Amoco7362873248916.72.7
61.416.1Arco
Chemical2399541166.21.142.840.4Ashland71431977779.53.81.
112.4Atlantic Richfield7192722532221.85.412.833.8Bausch &
Lomb51916277360.891.042.6Baxter
International56138870711.51.061.1347.2Bristol-Myers
Squibb5167011497744.43.141.5224.1Burlington

76. Coat1177777512.31.180.0212.9Central Maine
Power395422992.40.160.979.6Chevron7419503547318.64.952.2
815.2CIGNA61493510819913.74.881.111.4Cinergy3435388581
3.31.591.822.4Dayton
Hudson12775714191181.70.3316.2Dillard's1681755929.22.310.
1615.7Dominion Resources37678201937.92.152.5817.7Dow
Chemical2200182404023.67.73.2411.6DPL31356358513.91.20.
9114.3E. I. DuPont
DeNemours2466534294221.32.081.2327.9Eastman
Chemical24678577816.33.631.7616Edison
International392352510112.31.73113.6Engelhard2363125866.10
.330.3861.8Entergy39562270014.21.031.825.4Equitable696661
5143812.32.860.213.4Ethyl71064106753.60.710.512.6Exxon713
72429606419.43.371.6317.1FPL
Group363691244912.23.571.9214.4The
GAP16508333833.71.30.222Georgia
Gulf29666132282.390.3211.8GIANT
Food4423115227.91.180.7826.9A &
P41026229956.91.660.3517.8Great Lakes
Chemicals2131122705.51.190.6240.5Green Mountain Power
Company31793268.31.571.6114Hannaford
Bros.4322612279.91.40.5426.6Hercules218662411473.18114.5
Houston Industries368731841581.661.513.7Jefferson-
Pilot625782313114.53.471.0413.3Johnson &
Johnson5226292145326.72.410.8524.1Liberty6660318511.13.34
0.7712.7The Limited19189430110.60.790.4826.7Lincoln
National64899771750.40.211.96300.2Lubrizol216741462192.66
1.0114.5Lyondell
Petrochemical73010155946.23.580.96.4Mallinkrodt5186829881
4.82.470.6616May Department
Stores112685993020.53.111.216.2McKesson5208575608111.59
0.526Mercantile
Stores1314421787.93.531.1916.3Merck5236372581236.63.741.
6926.6Millennium
Chemicals23048432612.62.470.68.3Mobil7659064355916.84.01
2.1217.2Monsanto27514107747.20.480.590.7Morton223882805

77. 12.31.480.3625.2Murphy Oil72138223812.32.941.3518Mylan
Laboratories555584813.50.820.1622.4NALCO
Chemical214341441252.1118.3Nevada
Power3799233910.11.651.614.2NIPSCO32587493714.11.530.91
4.4Olin22410194617.431.214.5Orion
Capital615913884164.150.69.8Owens &
Minor531177139.40.60.1821.7Pacific
Corporation36278138805.20.681.0834.2J. C.
Penney130546234937.72.12.1326.9Pennzoil72654440615.83.76
117.1Pfizer5125041533627.91.70.6835.4Pharmacia &
Upjohn56710103805.80.611.0856.2Phillips
Petroleum7154241386019.93.611.3412.4Poe &
Brown612919425.11.480.3516.3PPG27379686828.53.941.3314.
7PP&L
Resources33049948511.41.81.6712Progressive64190756018.75.
310.2417Rohm &
Haas23999390019.82.130.6313.4Ruddick4230088512.51.020.32
17Schering-Plough56778650751.21.950.7424.6Sears,
Roebuck1412963870020.32.990.9217.4Stryker598098520.51.28
0.1127.2Sun7105314667182.7113Sunamerica621143563714.71.
80.319.5Texaco7466672960020.94.871.7511.5The TJX
Companies17389261026.31.750.098.2Torchmark622831096717.
52.390.5914.2Tosco713282597510.91.370.2423Travelers637609
38655514.92.540.417Ultramar Diamond
Shamrock71088255959.51.941.116.1Union
Carbide26502696428.84.530.7910.7United States Surgical
Corporation5117217267.51.210.1629UNOCAL76064753028.92.
650.815.5UNUM640771320015.22.590.5617USX-
Marathon7157541056512.61.580.7619.8Valero
Energy7575624939.62.030.4217.2Warner-
Lambert58180803130.71.040.5135.7WEIS
Markets418199729.21.870.9416.9Wellman2108313194.80.970.3
520.5Winn-Dixie
Stores413219292115.31.360.9827.2WITCO221872298141.551.1
224.9Zenith Nation Insurance660112527.81.57117
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78. Week 6 Options
QNT/561 Version 9 2
University of Phoenix Material
Option 1: Manufacturing Database
This database contains six variables taken from 20 industries
and 140 subindustries in the United States. Some of the
industries are food products, textile mill products, furniture,
chemicals, rubber products, primary metals, industrial
machinery, and transportation equipment. The six variables are
Number of Employees, Number of Production Workers, Value
Added by Manufacture, Cost of Materials, End-of-Year
Inventories, and Industry Group. Two variables, Number of
Employees and Number of Production Workers, are in units of
1000. Three variables, Value Added by Manufacture, Cost of
Materials, and End-of-Year Inventories, are in million-dollar
units. The Industry Group variable consists of numbers from 1
to 20 to denote the industry group to which the particular
subindustry belongs.
Option 2: Hospital Database
This database contains observations for six variables on U.S.
hospitals. These variables include Geographic Region, Control,
Service, Census, Number of Births, and Personnel.
The region variable is coded from 1 to 7, and the numbers
represent the following regions:
1 = South
2 = Northeast
3 = Midwest
4 = Southwest
5 = Rocky Mountain
6 = California

79. 7 = Northwest
Control is a type of ownership. Four categories of control are
included in the database:
1 = government, nonfederal
2 = nongovernment, not-for-profit
3 = for-profit
4 = federal government
Service is the type of hospital. The two types of hospitals used
in this database are:
1 = general medical
2 = psychiatric
Option 3: Consumer Food
The consumer food database contains five variables: Annual
Food Spending per Household, Annual Household Income, Non-
Mortgage Household Debt, Geographic Region of the U.S. of
the Household, and Household Location. There are 200 entries
for each variable in this database representing 200 different
households from various regions and locations in the United
States. Annual Food Spending per Household, Annual
Household Income, and Non-Mortgage Household Debt are all
given in dollars. The variable Region tells in which one of four
regions the household resides. In this variable, the Northeast is
coded as 1, the Midwest is coded 2, the South is coded as 3, and
the West is coded as 4. The variable Location is coded as 1 if
the household is in a metropolitan area and 2 if the household is
outside a metro area. The data in this database were randomly
derived and developed based on actual national norms.
Option 4: Financial Database
The financial database contains observations on seven variables
for 100 companies. The variables are Type of Industry, Total

80. Revenues ($ millions), Total Assets ($ millions), Return on
Equity (%), Earnings per Share ($), Dividends per Share ($),
and Average Price per Earnings (P/E) ratio. The companies
represent seven different types of industries. The variable Type
displays a company's industry type as:
1 = apparel
2 = chemical
3 = electric power
4 = grocery
5 = healthcare products
6 = insurance
7 = petroleum
Copyright © 2017 by University of Phoenix. All rights reserved.
This signature assignment is designed to align with specific
program student learning outcome(s) in your program. Program
Student Learning Outcomes are broad statements that describe
what students should know and be able to do upon completion
of their degree. The signature assignments might be graded with
an automated rubric that allows the University to collect data
that can be aggregated across a location or college/school and
used for program improvements.
Purpose of Assignment
The purpose of this assignment is for students to synthesize the
concepts learned throughout the course. This assignment will
provide students an opportunity to build critical thinking skills,
develop businesses and organizations, and solve problems
requiring data by compiling all pertinent information into one
report.
Assignment Steps
Resources: Microsoft Excel®, Signature Assignment Databases,
Signature Assignment Options, Part 3: Inferential Statistics

81. Scenario: Upon successful completion of the MBA program, say
you work in the analytics department for a consulting company.
Your assignment is to analyze one of the following databases:
• Manufacturing
• Hospital
• Consumer Food
• Financial
Select one of the databases based on the information in the
Signature Assignment Options.
Provide a 1,600-word detailed, statistical report including the
following:
• Explain the context of the case
• Provide a research foundation for the topic
• Present graphs
• Explain outliers
• Prepare calculations
• Conduct hypotheses tests
• Discuss inferences you have made from the results
This assignment is broken down into four parts:
• Part 1 - Preliminary Analysis
• Part 2 - Examination of Descriptive Statistics
• Part 3 - Examination of Inferential Statistics
• Part 4 - Conclusion/Recommendations
Part 1 - Preliminary Analysis (3-4 paragraphs)
Generally, as a statistics consultant, you will be given a
problem and data. At times, you may have to gather additional
data. For this assignment, assume all the data is already
gathered for you.
State the objective:
• What are the questions you are trying to address?
Describe the population in the study clearly and in sufficient
detail:
• What is the sample?
Discuss the types of data and variables:
• Are the data quantitative or qualitative?
• What are levels of measurement for the data?

82. Part 2 - Descriptive Statistics (3-4 paragraphs)
Examine the given data.
Present the descriptive statistics (mean, median, mode, range,
standard deviation, variance, CV, and five-number summary).
Identify any outliers in the data.
Present any graphs or charts you think are appropriate for the
data.
Note: Ideally, we want to assess the conditions of normality too.
However, for the purpose of this exercise, assume data is drawn
from normal populations.
Part 3 - Inferential Statistics (2-3 paragraphs)
Use the Part 3: Inferential Statistics document.
• Create (formulate) hypotheses
• Run formal hypothesis tests
• Make decisions. Your decisions should be stated in
non-technical terms.
Hint: A final conclusion saying "reject the null hypothesis" by
itself without explanation is basically worthless to those who
hired you. Similarly, stating the conclusion is false or rejected
is not sufficient.
Part 4 - Conclusion and Recommendations (1-2 paragraphs)
Include the following:
• What are your conclusions?
• What do you infer from the statistical analysis?
• State the interpretations in non-technical terms. What
information might lead to a different conclusion?
• Are there any variables missing?
• What additional information would be valuable to
help draw a more certain conclusion?