Team 12 Members: Kyle Fennelly, Chris Flounders, and Veronica Tomchak
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Team
12
Members:
Kyle
Fennelly,
Chris
Flounders,
and
Veronica
Tomchak
Advisor: Aydin Tozeren
Instructor: Karen Moxon
Progress Report: Designing a DNA Microarray to Improve the Clinical Identification of
Pathogenic Strains of Staphylococcus aureus
Executive Summary
Group 12 overcame significant hurdles during the winter term, yielding a more focused and
acceptable senior design project. The project now attempts to improve upon bacterial culturing-
the current gold standard- as a method of detecting pathogenic strains of Staphylococcus
aureus. There exists a need for a better detection method because culturing is associated with
a high false positive rate, high noise, and long turnaround time. Our solution involves the
generation a DNA microarray that detects pathogenic strains of Staphylococcus aureus based
on genes that code for virulence factors. We have designed a microarray that has a lower false
positive rate, lower noise contribution, and faster turnaround time. Further, it has the added
benefit of quantifying expression of particular genes, thereby aiding in the development of
synergistic antibiotic therapies and more efficient treatment regiments.
Table of Contents
I. List of Figures and Tables………………………………………………………………..1
II. List of Abbreviations and Definitions…………………………………………………….2
III. Problems and Issues…………………..………………..………………………………..2
IV. Problem Statement………………..………………..………………..……………………2
V. Objective………………..………………..………………..……………………………….3
VI. Design Specifications………………..………………..………………..…………………3
A. Design Criteria
B. Design Constraints
VII. In-Depth Solution………………..………………..………………..……………………...5
A. Background
B. Relationship to Design Criteria
C. Design Process
VIII. Description of the Prototype to Date………………..………………..…………………8
IX. Testing………………..………………..………………..………………..………………..9
X. Societal and Environmental Impact………………..………………..………………...11
XI. Schedule for Spring Term………………..………………..………………..………….11
XII. Business Plan………………..………………..………………..………………………..11
A. Market Analysis
B. Competitive Environment
C. Intellectual Property
XIII. Lessons Learned………………..………………..………………..…………………….13
XIV. References………………..………………..………………..………………..………….14
XV. Appendix A - List of Probes ……………………..………………..……………………17
1
XVI. Appendix B - List of Resumes ………………..………………..………………..………….17
XVII. Appendix C - Competitive Landscape Comparison Criteria………………………….…..17
XVIII. Appendix D – Direct Answers to Instructor’s Questions………………………………….17
XIX. Appendix E – MYcroarray Manual…………………………………………………………..19
I. List of Figures and Tables
Figure 1 - Design Process Overview………………………..………………………………….….. 8
Figure 2 - Schematic of Microarray………………………..………………..……………..………..9
Figure 3 - False Positive Experiment……………………..………………..………………...........10
Table 1 - Competitive Matrix…………………………………………………………………………13
Figure 4 – Spring Term Gantt Chart………………………………………………………………..12
II. List of Abbreviations and Definitions
cDNA - complementary DNA;
MIAME - Minimum Information About a Microarray Experiment - prevailing microarray standard
Oligos – oligonucleotides; DNA fragments
VF - Virulence Factor; molecules that promote pathogenicity (i.e. molecules that allow
pathogens to infect a host); examples include adhesion proteins that attack host tissue, or
lipases that facilitate cell invasion
VFDB - Virulence Factor Database; an online database that contains all known virulence factors
G/C - Guanine/Cytosine Ratio; the number of guanine and cytosine molecules in a given
fragment of DNA divided by the total number of molecules in that fragment.
S. aureus - Staphylococcus aureus;
III. Problems and Issues
We have run into a number of problems during the winter term, forcing our project to
substantially change direction. We had previously planned to create a DNA microarray to detect
all known bacterial pathogens. However, the senior design instructors had qualms about that
project, claiming that it is too nebulous and that it does not fulfill the requirements of senior
design. Thus, we have decided to focus on identifying and characterizing gene expression of
just one particular pathogen- S. aureus. This decision has led to a more focused project that
actually fulfills the requirements of the course. The decision was not made until Week 5 of the
current term and we have lost a significant amount of time. Despite these fallbacks, our project
has direction and operates within the acceptability space of the Senior Design course; we will be
able to successfully complete the project by the end of spring term. Having struggled so much
through the first half of the Senior Design sequence, each member of this group gained a well-
developed understanding of the design process, which is a major purpose of the course itself.
IV. Problem Statement
Recent soil and water content research indicates that 99% of bacterial species cannot
be grown in culture (Vartoukian, Palmer, & Wade, 2010). Still, the standard method for
identification of a bacterial infection in a clinical setting is bacterial culturing, wherein a
suspicious human sample is cultured in a series of differential media. Not only is bacterial
culturing successful only for a fraction of prokaryotic species, it also suffers from other
2
weaknesses. First, it has a high false positive rate; (Burman et al., 1997) even claims that the
false positive rate can be as high as 12%. Second, it has a contamination rate of 10%, which
roughly equates to 10% noise or signal interference (Thompson & Madeo, 2009). Also, it is
time-consuming, often taking between 24-72 hours for diagnostic results (Fox, 2010). Finally,
culturing does not provide information about the genes that are expressed by the bacteria in the
sample. This information is essential to diagnosticians and antibiotic drug manufacturers,
especially as the incidence of antibiotic resistance will force the creation and usage of
synergistic antibiotic therapies to defeat infections (Torella, Chait, & Kishony, 2010). Taken
together, these facts demonstrate that a better method of detecting bacterial infections is
needed. This need is particularly evident for certain pathogenic strains of S. aureus,
which 1) can be antibiotic resistant (e.g. MRSA), 2) can cause infections with a 20-40%
mortality rate (Stoppler, 2014) and 3) place a heavy cost burden on the health care
system every year.
Methods other than culturing exist but have downfalls associated with them: mass
spectrometry-based analysis (downfall- radioactivity used and expensive) and qPCR (low
throughput). Perhaps most important, none of these methods have the added benefit of
providing gene expression information to a diagnostician.
V. Objective
Design Team 12’s objective is to create a better method of detecting pathogenic strains
of S. aureus. In order to be better than culturing, the method must have a lower false positive
rate, noise, and turnaround time than culturing and must quantify the expression of genes that
code for virulence factors. The design group will do this by designing a DNA microarray with
probes for all known VFs that are expressed by pathogenic strains of S. aureus.
VI. Design Specifications
The problem statement highlighted four main problems with the current standard
detection method. Our general design criteria are as follows: low false positive rate, low signal
interference/noise, and fast turnaround time. Most importantly, our design must also provide
information about gene expression. To be an effective solution to the problem, our design must
meet each criterion. In the first section below, each criterion (italicized) will be described in detail
and its importance to the problem statement will be substantiated. The ways in which the
solution is constrained are discussed in the Constraints section; this section also includes a
discussion of applicable engineering standards.
A. Criteria
False Positive Rate
In general, a false positive is the declaration by a diagnostic method that an event
occurred when it actually did not. In the context of this project, a false positive is the declaration
that a particular gene sequence is present in a sample (i.e. is expressed) when it is not. The
false positive rate is the number of false positives out of the total number of declared positive
results. False positive rates vary between different conditions, but in the most extreme cases
bacterial culturing has a false positive rate of 12% (Burman et al., 1997). False positive results
can result in unnecessary treatment such as unnecessary administration of antibiotics.
Antibiotics or other medications taken outside of their clinical necessity can cause illness or
3even death, as such false positives should be eliminated as much as possible for a method to
be considered to have a positive outcome. Thus, in order to be effective, our solution must have
a false positive rate lower than 12%.
Noise/Interference
In terms of this project, signal noise (interference) is the component of a given probe
fluorescence that is attributable to probe DNA hybridizing to a sample DNA sequence that is not
100% complementary. The most common way for non-target bacteria to be present in cultures
is through contamination. Since signal noise is defined as the presence of unwanted data points
collected in a data set, the non-target bacteria that contaminate samples during culturing can
similarly be defined as unwanted extraneous data. For example, suppose a clinician orders a
culture of a skin lesion that is truly MRSA negative. During the culturing protocol, the technician
mistakenly allows MRSA to contaminate the culture, thereby increasing the noise of the final
result. (Thompson & Madeo, 2009) showed that contamination occurs in about 10% of cultures.
This contamination rate can be considered the contribution of noise to the final signal.
Therefore, in order for our solution to improve upon the current gold standard, our solution must
have a noise component less than 10%.
Turnaround Time
The turnaround time is the time from the initial preparation of the culture sample until the
identification results are obtained. Infectious bacteria multiply rapidly over time. Consequently,
in treatment, turnaround time is directly proportional to the intensity of infection. The extra time
incurred during the culturing process allows bacteria to proliferate, making treatment less
effective. Therefore, an infection identification method is more effective with a low turn-around
time. The turnaround time for culturing is approximately 24-48 hours (Fox, 2010). However,
turnaround times can be extremely large for some bacteria (e.g. some species of
Mycobacterium (Fukushima et al., 2003)). In order to improve upon the current gold standard,
our solution must have a turnaround time less than 24 hours.
Gene Expression
As indicated in the problem statement, information about pathogenic and antibiotic gene
expression certainly helps diagnosticians and drug manufacturers treat bacterial infections more
effectively. Culturing and other methods of pathogen detection do not provide information about
gene expression. Thus, our method must be able to provide a quantitative description of all
known pathogenic and antibiotic resistance genes expressed by S. aureus.
B. Constraints
In order to detect all pathogenic strains of S. aureus, the microarray’s probes must
represent genes that code for virulence factors (i.e. molecules that promote pathogenicity).
Virulence factors are generally conserved across strains, meaning that multiple strains use
similar mechanisms to infect their hosts. Further, the probes must be chosen from regions of
these VF-related genes that are identical in nucleotide sequence across all pathogenic strains of
S. aureus. For example, autolysin is a virulence factor that promotes adherence of bacteria to a
host cell. It is expressed in eighteen strains of S. aureus. The nucleotide sequence for autolysin
is not the same across all the strains. However, regions of the sequences are similar. Thus, we
must select probes from the regions of the gene coding for autolysin that are identical in
nucleotide sequence across all 18 strains of S. aureus. Although these regions of similarity in
4nucleotide sequence exist, there are limited in number and thus, our group is constrained to
choosing probes from within this region.
In terms of Engineering Standards, the microarray field is very young and few
engineering standards actually exist. The prevailing standard, MIAME, does not substantially
constrain our design. It merely requires that our probes are properly annotated and that we
provide information about the number of replicates per probe. Our group will certainly provide
this information. Therefore, our solution will be compliant with the prevailing microarray standard.
VII. In-Depth Solution
The In-Depth Solution section contains three parts. In the background section, essential
details about microarray design are presented and jargon is defined. In the second section, we
will describe what parameters of the design process we can manipulate such that we can fulfill
the criteria and ultimately solve the problem. Finally, in the design process section, we will
present the overall design process: from gene gathering to probe selection to manufacturing
and finally to data analysis.
A. Background
Our group has decided that the most appropriate solution to this problem is to create a
DNA microarray. A DNA microarray is a 1x3” glass slide that contains an array of single
stranded DNA sequences, called probes, which represent short, unique regions of a certain
gene of interest. To perform a microarray experiment, it is necessary to extract mRNA from the
sample, reverse-transcribe it, and fluorescently label it, generating labeled single stranded DNA
sequences called complementary DNA (cDNA). If a given cDNA sequence is complementary to
a probe sequence then the pair will hybridize (i.e. “bind”) during heating in a buffered solution.
After hybridization, the chip is washed several times to clear the non-hybridized cDNA
sequences. Only the bound-cDNA remain, resulting in a fluorescent signature anywhere the
sample bound to (i.e. hybridized to) a probe. Finally, the chip is scanned with a laser scanner
that detects the presence and intensity of the fluorescence on the chip. The scanner then
generates a digital array of fluorescent intensities that ultimately indicate the “degree” of gene
expression as shown through how much of a sample DNA strand bound to a probe. The
intensities are tabulated and imported to a software suite (such as MATLAB) for analysis. After
standard data processing steps (e.g. normalization, background noise removal, etc.) a
quantitative description of gene expression is produced.
The most important design component of a microarray is the selection of the nucleotide
sequences of the probes. Ideally, an effective probe is one that uniquely identifies a gene by not
only binding with it reliably but also minimizes non-identical hybridization. For example, a probe
should hybridize with a strand that is completely complementary (e.g. ATCTG on probe only
hybridizes to TAGAC on sample sequence, etc.) and should not hybridize with a sequence that
is less than 100% similar.
In terms of the uniqueness of the probes, it is necessary to understand the functionality
of probe selection software. Our group has decided to use Picky 2.2 (because it is free) to assist
with the design of the nucleotide sequences for the probes. Picky 2.2’s input is a fasta file
containing nucleotide sequences for all genes of interest. The program aligns all sequences
within the target file and selects regions of highest dissimilarity. The user controls the end-
dissimilarity of the probes by inputting the maximum number of allowable continuous matches of
nucleotides. The more stringent the dissimilarity requirement the lower the output of useable
5probes but the higher the specificity of the probe for the gene of interest. The user can also
define the desired Guanine/Cytosine ratio and melting temperature difference among the probes.
Ultimately, our group modified these three parameters in order to create a chip that fulfills the
design criteria, thereby solving the problem in the problem statement (refer to the next section).
B. Relationship to Design Criteria
It is essential to understand that Picky’s user-modifiable parameters are directly related
to the design criteria discussed in the Criteria section. In other words, by modifying the
parameters of probe selection in Picky (e.g. Guanine/Cytosine Content, etc), we can modify our
design to achieve the design criteria all while working within the constraints. Having laid a
foundation, we can now explain the mechanism by which we will achieve each of the design
criteria.
False Positive Rate
To describe false positive rate of a microarray, it is necessary to describe the process by
which a probe is declared to have “significantly high” fluorescence. Microarrays are made such
that they contain two types of probes: perfect match (i.e. the probe’s nucleotide sequence is
identical to a small region of the gene of interest) and slight mismatch (i.e. the probe’s
nucleotide sequence is identical to a region of the gene of interest except in the middle, where
one nucleotide is changed). A probe set for a particular gene includes all perfect match probes
and all mismatch probes for that particular gene. The discrimination score (R) for each probe set
is calculated as
where PM is the perfect match fluorescent intensity and MM is the mismatch intensity. R is then
compared to a standard value called Tau (0.0015) using the nonparametric Wilcoxon Signed
Rank Test. Considering that the Wilcoxon Signed Rank Test is conducted at a
significance level of 0.05 (alpha=0.05) and that the false positive rate is equal to the
significance level, our microarray is capable of achieving 5% false positive rate.
Noise/Interference
Despite best efforts of probe design software, non-identical hybridization is inevitable in
some cases. Thankfully, a well-developed method exists for handling these cases. (Kane et al.,
2000) performed an experiment that showed that the noise contribution to a given probe
intensity signal increases with the number of contiguous complementary nucleotides on the
dissimilar sequence. For example, as the number of nucleotides that are exactly complementary
to a probe’s DNA sequence increases, the signal intensity increases. Ultimately, they showed
that in order to minimize interference from non-exact complements, probes need to be designed
to be less than 30% similar to each other. In order to design for a noise contribution of
approximately 1% (which is ten times less than the culturing’s 10%), probe sequences of
50-nucleotide length must contain less than 15 contiguous complementary sequences
from the expected non-target sequences. This value can be directly “plugged into” Picky. It is
directly proportional to total probe number but inversely proportional to the uniqueness of the
probe. Noise is further reduced by ensuring that G/C is between 40-60% (Garbarine & Rosen,
2008) and melting temperature difference among the probes is no greater than 15 degrees
6Celsius (Chou & Denise, n.d.).
Turnaround Time
To fulfill this design criterion, we need to achieve a turnaround time of less than 24 hours.
By choosing to use microarray technology, we have significantly decreased the turnaround time
from 24-48 hours (required for culturing) to less than 12 hours. In terms of microarray
technology, turnaround time includes the time required to extract and label sample DNA (3
hours; ((RNA Extraction, n.d). and (“DNA Labeling, Hybridization, and Detection (Non-
Radioactive),” n.d.)), perform hybridization/wash cycles (7 hours; (“DNA Labeling, Hybridization,
and Detection (Non-Radioactive),” n.d.)), fluorescent scanning (15 minutes; (Agilent, 2013)),
and data analysis (variable). As hybridization technology improves and becomes more
automated, the turnaround time promises to decrease by 90% (May, 2013).
Gene Expression
The solution must quantify the expression levels of all pathogenic genes utilized by S.
aureus. Virulence factors are any of the molecules created by pathogenic bacteria that aid in the
infection of a host. Non-pathogenic strains of bacteria do not produce virulence factors. The
genes that encode for virulence factors can be considered markers for pathogenicity. Therefore
in order to detect pathogenic strains of S. aureus, our microarray must contain probes that are
specific for S. aureus virulence factors. The nucleotide sequences that code for S. aureus’s
virulence factors are readily available in online databases such as the Virulence Factor
Database (henceforth VFDB). Although these nucleotide sequences are very similar across the
scope of S. aureus strains, since virulence factors are evolutionarily conserved, they differ in the
regions affected by genetic drift. Thus, in order to make effective probes for virulence factor-
related genes, our group must select the regions of these genes that are identical across all
sequenced strains of S. aureus. Information about the expression of genes responsible for
antibiotic resistance is also very helpful. As such, our solution must contain probes for these
genes as well. Several genes responsible for antibiotic resistance have already been
characterized (Lowry, 2003).
C. Design Process
Considering the above, three types of probes were designed (see Figure 1). The first two
types are probes for virulence factor and antibiotic resistance genes. Genes coding for virulence
factors were obtained from the VFDB. As discussed in the Constraints section, we identified the
most similar regions of virulence factor-related genes expressed by all strains of S. aureus (i.e.
all strains that have been completely sequenced and annotated). Genes coding for antibiotic
resistance were obtained from (Lowry, 2003). Each gene symbol listed in (Lowry, 2003) was
inputted to NCBI Gene, yielding different DNA sequences that code for that gene across all
sequenced strains of S. aureus. Although the sequences were very different between strains,
only one sequence for each gene listed in (Lowry, 2003) was extracted and imported to Picky.
We were restricted to choosing only one sequence due to time constraints (i.e. we needed to
order the chip).
The third type of probe is control. These probes are designed such that they detect gene
sequences that are expressed by all strains of S. aureus bacteria. In a given microarray
experiment, these probes should fluoresce most strongly because all the bacteria in the sample
should be expressing these genes. Ultimately, these control probes help discover errors in the
microarray experiment process. If the control probes do not fluoresce during an experiment, it is
safe to declare that the data from that experiment are flawed. (Chaudhuri et al., 2009) published
7a list of all genes that are essential for growth of S. aureus in culture. They clustered all these
genes into the following categories of genes: DNA metabolism, RNA metabolism, Protein
Synthesis, Cell Wall and Associated Proteins, Carbon Metabolism and Respiration, and
Nucleotides and Cofactors. We included one gene from each category in our microarray.
Figure 1 - Overview
VIII. Description of the Prototype to Date
The first prototype was received on 02/27/2014. The schematic is contained within
Figure 2, where each element of a probe set is represented by a shape: circles represent
perfect match probes and triangles represent mismatch probes. Each type of probe set is
represented by a different color. Probe sets that target virulence factor genes are orange,
control genes are maroon, and antibiotic resistance genes are green. There are a total of 714
unique probes. Each probe is then repeated 7 times (based on the manufacturer’s
recommendation), yielding slightly under 5,000 total probes. See Appendix A for a list of all
probes.
8Figure 2 - Schematic of Microarray
IX. Testing
This section will describe how we plan to ensure that our design effectively solves the problem
listed above (i.e. fulfills the design criteria).
False Positive Rate
The false positive rate is equal to the level of significance used in the Wilcoxon Signed
Rank Test. As long as we use a significance level less than 12% then we will successfully fulfill
the false positive rate criteria (i.e. our method of detection will have a lower false positive rate
than the current gold standard). A more appropriate method of ensuring that the false positive
rate of the microarray is below 12% is to perform hybridization in a solution that contains a set of
known DNA fragments that are completely complementary to their probes. The false positive
rate will then be determined by dividing the total number of significant probe intensities (as
measured by the Wilcoxon Signed Rank Test) by the number of known DNA fragments in the
sample solution. Refer to Figure 3.
9Figure 3 - False Positive Experiment
Significant probes (as measured by Wilcoxon Signed Rank Test) are in red. In the failing
case, probes that were not present in the original sample were declared significant, causing
false positives.
Noise/Interference
Referring to Figure 2, it is clear that the microarray includes a standard probe of 50
nucleotides in length and a probe that is 30% similar to the standard probe (15 continuous
standard nucleotides, 35 dissimilar nucleotides). These probes will be incubated in a solution
containing the standard probe’s complete complement (i.e. all 50 sequences complement the
standard probe). This DNA sequence will be ordered from Integrated DNA Technologies. After
hybridization, which will be performed by MYcroarray, the probe intensity will be analyzed. The
dissimilar probe’s intensity will be divided by the standard probe’s intensity. The resulting value
should be between 0.01 and 0.1 (i.e. less than 10%). Ultimately, this analysis will demonstrate
that as long as the probes are designed such that they are less than 30% similar to the non-
targets, the contribution of noise to a given probe’s intensity will be less than 10%.
Turnaround Time
We are not able to directly test turnaround time because our group will not be performing
the “wet lab” component of this project. We plan to outsource that work to MYcroarray, the
microarray chip manufacturer. We will, however, confirm with them that their turnaround time is
less than 24 hours, thereby testing this criterion.
Gene Expression
This criterion is inherently accomplished by having chosen DNA microarray analysis as
the solution method. Recalling that the probes on our array were designed to target pathogenic
10and antibacterial resistant S. aureus genes found on government-hosted scientific databases, it
is clear that our DNA microarray provides information on meaningful gene expression.
For clarity’s sake, we intend to test both false positive rate and noise contribution by
performing one master experiment. We will order the following types of DNA sequences from
Integrated DNA Technologies: 1) 20 different single stranded DNA sequences that are exactly
complementary to 20 of our designed probes and 2) two sequences that are complementary to
the noise probe set (i.e. one for standard probe and one for the 30% similar probe). To improve
the hybridization results, these DNA fragments will be selected such that their melting
temperatures are very similar. These DNA fragments will be shipped to MYcroarray along with
our designed microarray. MYcroarray will then perform the hybridization and will send us the
data. We will then analyze the data based on the descriptions above. If false positive rate is less
than 12% and noise contribution is less than 10% then our design is successful because it
fulfills our design criteria.
X. Societal and Environmental Impact
Positive
The creation of a DNA microarray that can identify pathogenic strains of Staphylococcus
aureus would allow for an improvement in current methods of bacterial identification. The
current method being bacterial culturing; which as described above has a high false positive
rate (of in some cases 12%) and a long culturing time (24-72 hours). The improvements of our
device would be a revolution in the way bacteria is identified in a clinical setting.
One of the biggest impacts that this design has is that it can be used as a template that
can be modeled with other bacterial strains. The way of identifying bacterial infections in a
clinical setting could be drastically improved; not only in identification time but accuracy.
Bacterial culturing could in theory become obsolete to the evolution of DNA microarrays in a
clinical setting. With the lowering cost of technology, it can be assumed that DNA microarrays
will continue to become cheaper to producer with better accuracy. Allowing DNA microarrays to
identify pathogenic bacterial strains to become a viable option in a clinical setting.
Negative
In order to properly identify a bacterial sample on a DNA microarray it must be properly
treated with DNA hybridization techniques. These techniques require additional training and
specific reagents. This can be seen as a negative, compared to the ease of culturing a bacterial
sample and letting it grow. The techniques of DNA hybridization can be easily muddled with if
done improperly. This could lead to the misuse of resources (time, money, and reagents),
allowing the infection to grow and become more aggressive.
Once the bacterial solution has been properly prepared with DNA hybridization
techniques it has to be scanned for analysis. In order to scan these microarrays a machine
designed specifically for microarray scanning must be used. This machine also takes time to
train technicians and take up resources (space, time, money). The implementation of this new
technique would require more resources and training of technicians, which in a clinical setting is
not always available.
XI. Schedule for Spring Term
11Since our project changed so drastically, our schedule from fall term was very inaccurate.
Thus, it will not be reproduced here. Majority of the remaining time in the senior design course
will be focused upon testing the device. Figure 4 contains an itemized list of tasks that need to
be completed in order to test the microarray. The first major step is selecting and ordering the
DNA fragments (referred to as oligos in the figure) for the testing procedure described above.
We will then generate SSPE buffer according to Appendix E, combine the fragments into one
vial, perform necessary dilutions, and ship the vial to MYcroarray along with our designed
microarray. MYcroarray will then perform the hybridization. Once finished, MYcroarray will send
us the data from the experiments. Our group will then generate (in parallel to the hybridization)
and execute (which will take much less than one day, assuming efficient coding) the algorithms
needed to extract the information about the design criteria. Assuming all goes as planned, we
should finish this process during Week 5 or 6 of spring term.
Figure 4 - Spring Term Gantt Chart and Task List
This figure was generated using OpenProj, project management software.
XII. Business Plan
Market Analysis
Industry: Pharmaceutical—Pharmacogenomics
12Pharmacogenomics is the industry of using genetics and genetic interaction to identify and/or
treat disease. It encompasses everything from companies like “23 and me” that sequence
peoples’ genomes to test them for ancestry data or mutated genes to tools that analyze
genetics to customize disease treatments.
Target Market Demographics: Pharmacogenomic Diagnostic devices
Our specific market is the industry of using pharmacogenomics to diagnose and treat disease.
Annual Dollars Spent
This is a complicated metric for this industry. Currently most methods are being
produced and invented, and are not in current wide use. Analysts say that it is an strong
emerging economic field, and many reports use current estimates of the cost from waste of non-
targeted drug programs (i.e. using a regimen of drugs instead of a specific drug for the disease
the patient has) to forecast potential financial gain.
According to Market forecasting site Markets and Markets: “The global DNA & gene chip
(microarray) market was valued at $760 million in 2010 and is expected to reach $1,425.2
million by 2015 growing at a CAGR of and 13.4%.” (Markets and Markets) Which is only the
sale of the gene chips themselves, regardless of application.
In terms of the potential monetary gain one must consider these statistics:
According to the director of the CDC , Muin J Khoury:
● “82% of American adults take at least one medication and 29% take five or more;
● 700,000 emergency department visits and 120,000 hospitalizations are due to ADEs annually;
● your list item
● $3.5 billion is spent on extra medical costs of ADEs annually;
● At least 40% of costs of ambulatory (non-hospital settings) ADEs are estimated to be
preventable.
● More than 2 million people are hospitalized every year due to adverse drug reactions, making
it one of the leading causes for hospitalizations in the U.S.” (Khoury)
● According to a gene chip company named Genopath the cost of fixing “drug related problems”
caused by non-specific drug mistreatment has cost health care companies upwards of 100
billion dollars annually in the past. ( “Why Pharmacogenomics”)
These costs show the potential savings the medical industry can expect to gain by utilizing gene-
specific drug therapy.
Target End-Users
● Hospitals: ~300,000 worldwide as of 2012 ("Top Ten Countries with Maximum
Hospitals.")
● Genomic Counselors: 2,100 in the US as of 2012 with a forecast of 41% growth over
the next ten years (Bureau of Labor Statistics)
● Medical Scientists: 103,000 in the US with a growth of 13% over the next ten years.
(Bureau of Labor Statistics)
Target Market Patient Population
● Patients with Staph infections in hospitals: around 1,200,000 per year in hospitals
worldwide. ("Staph Infection Statistics”)
Competitive Environment
There are two main industry standards currently in use by hospitals for diagnosing and treating
staph infections:
● Using a rapid bacteria culture to diagnose infection before treatment
● Using medical knowledge and inspection by a doctor to diagnose with sight
The specific industry used tests that we compared to our design were:
● The Staphaurex Plus kit
13● tube coagulase test
● thermostable-endonuclease test
● RAPIDEC staph kit
● Non-testing diagnosis
Table 1 - Competitive Matrix
Factor Our The the tube thermostabl RAPIDE Sight-
Technolo Staphaure coagula e- C staph diagnos
gy x Plus kit se test endonucleas kit is
e test
Sensitivi 9 2 9 7 10 2
ty (%)
Specifici 10 10 10 9 10 2
ty
(%)
accuracy 9 3 3 3 3 2
time 6 10 1 1 10 2
Total 34 25 23 20 33 8
(/50
For determining figures see Appendix C. Values taken from (Chapin et. al.) and (Pang, Yu, et
al.)
Intellectual Property
It does not make sense to pursue a patent for this design for several reasons:
1)This specific design does not have a long product-life - the patent process can take 5 or more
years for processing. This technology is ever-evolving. Specifically, this technology must be
able to adapt to changing patient populations and even the evolving of microbes as their
genomes change. Staying stagnant for 5 years would render this design virtually worthless
2) This design is easily reproduce-able - the amount of specificity that would likely be necessary
to prove originality, uniqueness and non-obviousness would likely allow for others to exactly
copy our probe selection design if they viewed our patent application in the public domain. We
do not have the budget to hire a lawyer to ensure protection of our idea.
3) Our design is easily kept a trade secret - instead of submitting our design into the public
domain where it may or may not end up qualifying for a patent, we can simply keep the specifics
of our probe selection process as a trade secret if need be.
XIII. Lessons Learned
Our group gained a great deal of knowledge so far in the senior design course: how to
use project management software (OpenProj), the importance of thinking ahead, the design
process, DNA microarray technology (probe selection algorithms, data analysis, experiment
protocols), collaborating with vendors in different disciplines (microarray manufacturers and
biochemists), writing technical reports (through guided trial and error), understanding the
positive and negative impacts and unintended consequences of an engineering design, and the
importance of engineering standards. Together, this knowledge makes each member of this
group more attractive to potential employers, which is essentially the entire purpose of a Drexel
education.
14XIV. References
Agilent. (2013). Agilent’s DNA Microarray Scanner with SureScan High-Resolution Technology.
Retrieved from http://crb-
gadie.inra.fr/fileadmin/plateforme_data/documents/agilent_general/Scanner_brochure.pdf
Bureau of Labor Statistics, . N.p.. Web. 18 Feb 2014. .
Bureau of Labor Statistics, . N.p.. Web. 18 Feb 2014. .
Burman, W. J., Stone, B. L., Reves, R. R., Wilson, M. L., Yang, Z., El-Hajj, H., … Cave, M. D. (1997).
The incidence of false-positive cultures for Mycobacterium tuberculosis. American Journal of
Respiratory and Critical Care Medicine, 155(1), 321–326. doi:10.1164/ajrccm.155.1.9001331
Chapin Kimberle, Musgnug Michael. “Evaluation of Three Rapid Methods for the Direct Identification
of Staphylococcus aureus from Positive Blood Cultures.” J Clin Microbiol. 2003
September; 41(9): 4324–4327. doi: 10.1128/JCM.41.9.4324-4327.2003. PMCID: PMC193828
Chaudhuri, R. R., Allen, A. G., Owen, P. J., Shalom, G., Stone, K., Harrison, M., … Charles, I. G.
(2009). Comprehensive identification of essential Staphylococcus aureus genes using
Transposon-Mediated Differential Hybridisation (TMDH). BMC Genomics, 10(1), 291.
doi:10.1186/1471-2164-10-291
Cheung, V. G., Morley, M., Aguilar, F., Massimi, A., Kucherlapati, R., & Childs, G. (1999). Making
and reading microarrays. Nature Genetics, 21(1 Suppl), 15–19. doi:10.1038/4439
Chou, H.-H., & Denise, M. (n.d.). Picky Tutorial. Iowa State University Complex Computation
Laboratory. Retrieved from
http://www.complex.iastate.edu/download/Picky/tutorials/Picky%20Tutorial%201.00.pdf
DNA Labeling, Hybridization, and Detection (Non-Radioactive). (n.d.). Bowling Green State
University. Retrieved from http://personal.bgsu.edu/~gangz/Page085-
092_DNA_Labeling_Hybridization_And_Detection_Non-radioactive_label.pdf
Fox, A. (2010). Chapter 2 - CULTURE AND IDENTIFICATION OF INFECTIOUS AGENTS. In
Bacteriology. Board of Trustees of the University of South Carolina. Retrieved from
http://pathmicro.med.sc.edu/fox/culture.htm
Fukushima, M., Kakinuma, K., Hayashi, H., Nagai, H., Ito, K., & Kawaguchi, R. (2003). Detection and
Identification of Mycobacterium Species Isolates by DNA Microarray. Journal of Clinical
Microbiology, 41(6), 2605–2615. doi:10.1128/JCM.41.6.2605-2615.2003
Garbarine, E., & Rosen, G. (2008). An information theoretic method of microarray probe design for
genome classification. Conf Proc IEEE Eng Med Biol Soc, 2008, 3779-3782. doi:
1510.1109/iembs.2008.4650031
Joseph Peter Torella, Remy Chait, & Roy Kishony. (2010). Optimal Drug Synergy in Antimicrobial
Treatments. PLoS Computational Biology, 6(6). doi:10.1371/journal.pcbi.1000796
Kane, M. D., Jatkoe, T. A., Stumpf, C. R., Lu, J., Thomas, J. D., & Madore, S. J. (2000). Assessment
of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Research,
28(22), 4552–4557.
K. Ito, Ralph , and Laurence M. Demers. "Pharmacogenomics and Pharmacogenetics: Future Role
of Molecular Diagnostics in the Clinical Diagnostic Laboratory." . doi:
10.1373/clinchem.2004.031625 Clinical Chemistry September 2004 vol. 50 no. 9 1526-1527.
Web. 18 Feb 2014.
Khoury, Muin J. "Medications for the Masses? Pharmacogenomics is an Important Public Health
Issue." . Centers for Disease Control and Prevention, 11 Jul 2011. Web. 18 Feb 2014.
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Lowy, F. D. (2003). Antimicrobial resistance: the example of Staphylococcus aureus. The Journal of
Clinical Investigation, 111(9), 1265–1273. doi:10.1172/JCI18535
Markets and Markets, .N.p.. Web. 18 Feb 2014. .
May, M. (2013). Easier Hybridization for Microarrays. Retrieved from
http://www.biosciencetechnology.com/articles/2013/03/easier-hybridization-
microarrays#.UvKM22SwIXI
Pang, Yu, , et al. "Multicenter Evaluation of Genechip for Detection of Multidrug-Resistant
Mycobacterium tuberculosis." Journal of Clinical Microbiology. 51.6 (2013): 1707-1713. Print.
.
RNA Extraction. (n.d.). Human Genome Project. Retrieved from
http://imihumangenomproject.blogspot.com/2012/12/rna-extraction.html
"Staph Infection Statistics." . N.p.. Web. 18 Feb 2014. .
Stoppler, M. (2014). Staph Infections. Retrieved from
http://www.medicinenet.com/staph_infection/page5.htm
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Prevention, 10(1 suppl), s24–s26. doi:10.1177/1757177409342143
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bacteria. FEMS Microbiol Lett, 309(1), 1-7. doi: 10.1111/j.1574-6968.2010.02000.x
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Yacoby I, Benhar I. (2007). Targeted Anti-Bacterial Therapy. Infect Discord Drug Targets. ;7(3):221-
9. Review.PMID:17897058
XV. Appendix A - List of Probes
Please see the attached Excel spreadsheet entitled AppendixA_StaphAureus_GeneChip_Final for
the list of designed probes. Note the s in the gene name indicates that that probe sequence is
standard whereas the c indicates that the probe is a mismatch sequence (i.e. the middle
nucleotide of the standard has been substituted).
XVI. Appendix B - Group Resumes
Please see the attached resumes (required as a part of the business plan).
XVII. Appendix C - Competitive Landscape Comparison Criteria
Values Used for Competitive Matrix
Factor Our The the tube thermostable- RAPIDE Sight-
Technolog Staphaure coagulas endonuclease C staph diagnosi
y x Plus kit e test test kit s
17Sensitivit 88 23 92 68-85 98 N/A
y (%) (low)
Specificit 98 99 100 93 100 N/A
y (low)
(%)
accuracy Depends Depends Depends Depends on Depends Depend
on blood on blood on blood blood culture on blood s on
culture culture culture (low) culture doctor
(high) (low) (low) (med) (low)
time ~12 h > 1h ~24 h ~2 h ~24 h >1 h
Total
Values were taken from (Chapin et. al.) and (Pang, Yu, et al.)
XVIII. Appendix D – Direct Answers to Instructor’s Questions
After
submission
of
the
Progress
Report
Draft,
our
reviewer
had
the
following
request
followed
by
a
list
of
questions:
This
is
a
vast
improvement
on
the
proposal
of
last
term
but
a
lot
of
time
has
been
lost
and
much
remains
unclear.
I
have
commented
through
out
but
would
ask
that
in
working
on
this
you
also
address
these
questions
specifically
right
here
so
I
can
evaluate
your
progress
–
Here
are
the
reviewer’s
questions
with
the
associated
answers:
(1) There
is
not
timeline
and
progress
assessment
i.e.
a
checklist
of
what
you
plan
to
do
by
when
and
what
is
already
done.
Correct,
we
did
not
include
a
timeline
and
progress
assessment
in
the
draft.
It
has
been
included
in
this
report.
See
Figure
4.
(2) The
numbers
you
bring
as
gold
standards
–
what
is
the
reasoning
behind
them
–
what
is
the
reason
you
think
your
method
can
surpass
them?
Sorry
for
the
confusion.
Part
A:
The
numbers
themselves
are
not
gold
standards
and
we
did
not
intend
to
claim
that
the
gold
standard
value
of
false
positive
rate
for
detection
of
S.
aureus
is
12%.
Rather,
detection
of
S.
aureus
via
culturing
is
the
gold
standard
method
(because
it
is
most
common)
and
it
has
“these
numbers”
associated
with
it.
Part
B:
We
believe
the
numbers
associated
with
our
method
(i.e.
DNA
microarray
technology)
surpass
the
numbers
associated
with
culturing.
The
main
reason
for
this
claim
is
that
DNA
microarray
technology
employs
a
method
of
a
statistical
analysis
(with
an
associated
significance
level)
unlike
that
of
culturing.
For
example,
using
our
microarray,
one
is
able
to
declare
with
a
certain
level
of
confidence
whether
a
given
bacterium
is
present
in
a
sample.
Culturing
provides
a
simple
yes
or
no
answer
without
any
“help”
from
18statistics.
As
an
added
benefit,
microarray
technology
provides
information
about
gene
expression;
culturing
does
not.
(3) How
did
you
choose
the
list
of
probes?
This
as
far
as
I
can
tell
is
the
lion
share
of
what
you
(and
not
the
company)
are
doing
in
this
study.
You
give
some
explanation
for
the
criteria
in
choosing
but
you
do
not
describe
what
you
did
to
get
them
how
many
you
did
not
choose
and
what
are
each
groups
charecteristics
We
understand
your
confusion,
as
we
poorly
explained
the
probe
selection
process.
Please
see
the
revised
Design
Process
subsection
in
the
In-‐Depth
Solution
section:
VII.
Subsection
C.
(4) How
do
you
plan
to
validate
your
probe
works?
You
describe
how
you
will
send
out
the
chips
to
test
they
have
no
(or
not
many)
false
positives
but
how
will
you
test
that
there
are
true
positives
?
do
you
plan
to
acquire
clinincal
sampels?
Do
some
kind
fo
computational
analysis?
The
master
experiment
described
in
the
Testing
section
covers
both
false
positives
and
true
positives
(as
well
as
noise
contribution).
True
positives
occur
when
the
fluorescence
of
probe
set
for
a
given
DNA
fragment
is
correctly
declared
significant
by
the
Wilcoxon
Signed
Rank
test.
In
theory,
there
is
no
reason
to
believe
that
our
technology
will
not
(forgive
double
negative)
detect
a
fragment
of
DNA
when
it
is
present
in
a
sample.
Further,
true
positive
rate
was
not
included
as
a
design
criterion
because
culturing
has
a
high
true
positive
rate
and
is
thus
not
necessarily
a
problem
that
needs
solving.
We
also
think
the
following
information
would
be
helpful
to
our
reviewer:
• The
Criteria,
Solution,
and
Testing
sections
are
almost
identical
in
layout.
The
criteria
section
lists
our
general
criteria
that
address
how
a
detection
method
can
be
better
than
bacterial
culturing.
The
Solution
section
then
indicates
how
our
device
will
accomplish
each
of
those
criteria.
Within
this
section,
we
indicate
our
reasons
for
why
we
think
our
technology
can
overcome
the
pitfalls
of
culturing.
The
Testing
section
explains
how
we
will
test
each
criterion.
XIX. MYcroarray Manual
Please find this in the .zip package submitted to BBLearn.
19You can also read
