Team 12 Members: Kyle Fennelly, Chris Flounders, and Veronica Tomchak

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Team 12 Members: Kyle Fennelly, Chris Flounders, and Veronica Tomchak
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

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Team 12 Members: Kyle Fennelly, Chris Flounders, and Veronica Tomchak
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

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Team 12 Members: Kyle Fennelly, Chris Flounders, and Veronica Tomchak
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

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even 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

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nucleotide 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

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probes 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

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Celsius (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

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a 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.

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Figure 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.

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Figure 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

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and 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

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Since 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

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Pharmacogenomics 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.

                                                                                               14
XIV. 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:

                                                                                                     15
10.1109/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.
    .
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
Thompson, F., & Madeo, M. (2009). Blood cultures: towards zero false positives. Journal of Infection
    Prevention, 10(1 suppl), s24–s26. doi:10.1177/1757177409342143
"Top Ten Countries with Maximum Hospitals." . N.p.. Web. 18 Feb 2014.
    
                                                                                                     16
Vartoukian, S. R., Palmer, R. M., & Wade, W. G. (2010). Strategies for culture of 'unculturable'
    bacteria. FEMS Microbiol Lett, 309(1), 1-7. doi: 10.1111/j.1574-6968.2010.02000.x
"Why Pharmacogenomics." . N.p.. Web. 18 Feb 2014. .
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

                                                                                                     17
Sensitivit          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	
  

                                                                                                                                                       18
statistics.	
  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.

                                                                                                                                                         19
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